StudyFlow , an edtech newsletter platform with 80,000+ student subscribers, tested whether urgency language in a CTA button drives higher click-through rates on its weekly study-tips email. "A 2-point CTR lift across our full list translates to thousands of additional engaged sessions — we need to know if this is real." — Head of Growth
📋 Dataset Variables: CTA Version: Control ("Browse Resources") or Variant ("Get Study Tips Now") Click: Binary: clicked the CTA (1) or did not (0)
🤔 Classroom / Managerial Analysis Questions:- Is the 2 pp CTR difference between the variant and control statistically significant at α = 0.05?
- What is the 95% confidence interval for the difference in proportions?
- Should StudyFlow roll out the urgency CTA to the full 80,000-subscriber list? What would be the business case?
BoxRevive , a subscription box company, ran a retargeting campaign test on 2,000 cart abandoners to determine whether framing the recovery offer as a bonus bundle converts better than anchoring on the price discount, using a conservative α = 0.04 to control false-positive risk across a heavy testing quarter. "Gift-framed offers consistently outperform straight discounts in our user research — I need the A/B data to back that up before we shift the whole retargeting budget." — VP of Performance Marketing
📋 Dataset Variables: conversion: Binary: customer completed purchase after retargeting ad exposure (1 = converted, 0 = did not) offer_framing: Retargeting ad frame shown (Price Anchor: "Save $15" / Bonus Bundle: "Get 3 Bonus Items")
🤔 Classroom / Managerial Analysis Questions:- At α = 0.04, does the Bonus Bundle framing produce a statistically significantly higher conversion rate than the Price Anchor framing?
- What is the 96% confidence interval for the difference in conversion proportions? Does it exclude zero?
- Should BoxRevive switch all cart-abandoner retargeting to bonus bundle framing? What additional segments or AOV ranges would you want to test before scaling?
Calder Bay Transit , through its rider app, tested two rider-rewards offer structures on 1,795 active users to determine whether tiered, activity-based bonus points drive significantly higher partner program signups than a standard flat reward at α = 0.05. "If tiered rewards actually move the needle on partner signups, we're redesigning our entire loyalty architecture around variable incentives — but first, I need the numbers to hold up." — Sasha Berglund, Director of Planning and Customer Experience
📋 Dataset Variables: partner_signup: Binary: user linked partner account after receiving the offer (1 = signed up, 0 = did not) offer_type: Loyalty offer variant (Standard: flat 500 pts / Tiered: 500–1,000 pts based on activity)
🤔 Classroom / Managerial Analysis Questions:- At α = 0.05, does the Tiered Bonus offer result in a statistically significantly higher partner signup rate than the Standard 500-point offer?
- What is the 95% confidence interval for the difference in signup proportions (Tiered minus Standard)?
- Should Calder Bay Transit redesign its rider rewards around variable, activity-based bonuses? What segment of users (by activity level) is most likely driving the lift in the tiered group?
GlowDirect , a DTC beauty brand, ran a 50-person concept test of two CTA sticker designs before committing to a full 5,000-impression in-market launch test. "25 panelists per cell is tiny, but we need the effect size first — if the playful CTA shows a big gap here, it's worth a full-scale run." — Performance Marketing Manager
📋 Dataset Variables: Would Tap: Binary: panelist said they would tap the sticker after viewing the Story frame (1 = yes, 0 = no) CTA Variant: CTA sticker shown (Team A: "Shop Now" / Team B: "Tap to Unlock Deal")
🤔 Classroom / Managerial Analysis Questions:- At α = 0.05, does the playful CTA sticker significantly outperform "Shop Now" in this 50-panelist pilot?
- What is the 95% confidence interval for the difference in rates? How does the CI width reflect the limits of a 50-person sample?
- The observed gap is 20 percentage points. If that is not significant, what does it tell you about the sample size GlowDirect needs, and would you recommend the 5,000-impression test?
An e-commerce company deploys an AI chatbot to handle first-line customer inquiries — order status, returns, product questions, and troubleshooting. The bot has access to the customer's order history and account details via context injection.
A retail brand generates personalized email subject lines for their weekly promotional campaigns. Each call sends customer segment data and campaign details, then receives a single optimized subject line.
A marketplace platform auto-generates product descriptions from seller-provided specifications. A copywriter reviews the draft, requests edits (tone, length, SEO keywords), and the AI refines. Typically 2–3 rounds of revision.
A hotel chain processes customer reviews from multiple platforms (Google, TripAdvisor, Booking.com) through an LLM to extract sentiment scores, key themes, and actionable insights. Each review is analyzed independently.
A marketing analytics platform (like MktPraxis!) embeds an AI assistant that helps students and analysts understand statistical results, interpret charts, and get guidance on methodology. The AI receives the current tool state — inputs, computed results, and report text — as context on every message.
A digital agency uses AI to generate multiple ad copy variants for Google Ads and Meta campaigns. A strategist provides the brief, the AI generates 5 variants, the strategist picks favorites and requests refinements for different audiences.
ValuMart , a regional home-goods retail chain with 22 locations, tracked 3 years of monthly sales alongside advertising spend and promotional events, and wants a sales forecast it can plan the next six months around. "We know advertising works — what we don't know is how much, when the effect hits, and whether our holiday promotions are actually lifting sales or just pulling purchases forward." — VP of Digital Marketing
📋 Dataset Variables: sales: Monthly revenue in USD across all 22 locations ad_spend: Monthly advertising expenditure in USD promotion: Promotional month indicator (1 = promo active, 0 = standard) date: Month index in YYYY-MM format (36 periods)
🤔 Classroom / Managerial Analysis Questions:- How much higher are sales in a month with $1,000 more ad spend, holding the promotion flag constant? Why is that not the same as the return on an extra $1,000 of advertising?
- Are promotional months associated with a statistically significant sales boost above what the time-series trend already predicts?
- If ValuMart increases Q1 ad spend by 20% over last year, what does the model forecast for monthly sales across those 3 months?
PeakForm , a direct-to-consumer health supplement brand selling exclusively through Instagram, partnered with 3 fitness and wellness influencers to drive daily sales — and needed to quantify each creator's individual sales lift before renegotiating contract rates. "We're paying three very different rates for three very different audiences — the model should tell us whether we're getting proportional return on each one." — Head of Influencer Marketing
📋 Dataset Variables: daily_sales: Units sold on that day across all SKUs influencer_A: Fitness personality (850K followers) posted that day (0/1) influencer_B: Nutrition expert (1.2M followers) posted that day (0/1) influencer_C: Wellness lifestyle (600K followers) posted that day (0/1) date: Daily index (300 consecutive days)
🤔 Classroom / Managerial Analysis Questions:- Which influencer produces the largest estimated daily sales lift, and by how many units — controlling for the time-series structure?
- If PeakForm's contract rates are proportional to follower count, which influencer delivers the best sales-per-dollar-spent ROI?
- What does the model forecast for a week with no influencer posts? How does that baseline compare to a week with all three posting once each?
PuzzleVault , a free-to-play mobile puzzle game studio, tracked 6 years of monthly new-user signups alongside ad spend and major feature releases to separate organic seasonal growth from paid acquisition effects before planning its 2026 UA budget. "Every January we spike — but we can't tell how much is seasonal, how much is our New Year's campaign, and how much is just the trend. We need to decompose it properly before we commit $1.2M to next year's plan." — Growth Analytics Lead
📋 Dataset Variables: monthly_signups: New user accounts created that month mobile_ad_spend: Monthly UA ad budget in USD ($44K–$84K range) new_feature: Major feature release shipped that month (0/1) month: YYYY-MM format (2020-01 to 2025-12, 72 periods)
🤔 Classroom / Managerial Analysis Questions:- After controlling for trend and seasonality, how many additional signups does a $10,000 increase in monthly ad spend produce on average?
- Does the model confirm that January–February represent a statistically significant seasonal peak? How large is the seasonal component in absolute signup terms?
- The 2023 feature release landed in January — does the SARIMAX model suggest it amplified the seasonal peak, or was the spike entirely seasonal and trend-driven?
GlacierScoops , a regional soft-serve chain with 14 locations in the Pacific Northwest, needs to model the relationship between average monthly temperature and ice cream revenue to optimize staffing and ingredient orders. Revenue swings dramatically — summer months near 90°F can generate $82K while winter months near 34°F drop below $18K. The operations manager, Lisa Kwon, put it directly: "We over-ordered dairy by 40% last February because we didn't have a proper forecast model. I need something that uses temperature data to tell us when surges are coming."
📋 Dataset Variables: ice_cream_sales: Total monthly revenue across all 14 locations (USD) avg_temp_f: Average daily temperature for the month (°F) — key exogenous driver month: Date index: Jan 2023 – Dec 2024 (24 time periods)
🤔 Classroom / Managerial Analysis Questions:- What is the estimated revenue impact of each 1°F increase in monthly average temperature, holding the AR component constant?
- If the weather forecast shows an unusually warm March with an avg temp of 58°F, what does the model predict for that month's revenue?
- How does adding temperature as an exogenous predictor improve forecast accuracy vs. a pure AR model of sales alone?
Tanager Cycles ' dealer-fulfilled online store has been growing steadily since 2022 and now spends $4.5K–$11.4K/month across Google and Facebook ads. The Director of Brand and Digital, Keiko Matsuda, is frustrated with single-channel attribution: "Google says it deserves 71% of our revenue. Facebook says 68%. I want a time-series model that treats both channels simultaneously so I can see their individual marginal contribution without double-counting."
📋 Dataset Variables: monthly_revenue: Total monthly ecommerce revenue (USD) — has an upward trend requiring d=1 google_ads: Monthly Google Ads spend (USD) facebook_ads: Monthly Facebook Ads spend (USD) month: Date index: Jan 2022 – Dec 2024 (36 time periods)
🤔 Classroom / Managerial Analysis Questions:- After differencing for trend, which ad channel shows a larger marginal revenue coefficient — and is the difference statistically significant?
- If Tanager reallocates $2,000/month from Facebook to Google, what does the model predict for the change in monthly revenue?
- Does the AR component remain significant after controlling for both ad channels? What does that imply about organic revenue momentum?
ViralCurve Agency manages a growing lifestyle influencer account and wants to forecast daily engagement to help the creator decide when to publish paid sponsorships. The account grew from 180K to 640K followers over Jan 2022 – Sep 2024, with daily engagement ranging from 2,400 to 87,000 interactions. The talent manager, Bri Okafor, frames the business need clearly: "We charge 3x more for posts during high-engagement windows. If we can predict 5-day peaks, we can schedule sponsorships around them instead of guessing."
📋 Dataset Variables: daily_engagement: Total engagement (likes + shares + comments) per day posts_published: Number of posts published that day (0–2) paid_promotion: Binary: 1 = paid/sponsored post published, 0 = organic only is_weekend: Binary: 1 = Saturday or Sunday, 0 = weekday date: Daily index from 2022-01-01 to 2024-09-30 (1,004 days)
🤔 Classroom / Managerial Analysis Questions:- Does the AR component remain significant after controlling for posting behavior and day-of-week? What does engagement "momentum" mean in practical terms?
- Is the paid promotion coefficient positive or negative — and how would you explain that finding to Bri Okafor in terms of audience psychology?
- Using the fitted model, in what 5-day window over the next month would you schedule a premium sponsorship to maximize expected engagement?
CapitolComedy , a political satire Instagram account with 2.3M followers, wants to forecast daily engagement so the team can schedule premium sponsored posts during predicted high-engagement windows and charge advertisers a 30% premium. The content manager, Alex Rivera, explained the business stakes: "Our ad revenue depends on consistent engagement. If we can predict when engagement will spike around political events, we can time sponsorship placements and charge 30% more per post."
📋 Dataset Variables: daily_likes: Total post likes aggregated per day (range: 15K–180K) posts_count: Number of posts published that day (0–3, typically 1–2) political_event: Binary: major political event that day (debate, scandal, election result) weekend: Binary: Saturday or Sunday indicator (engagement typically lower) date: Daily from 2024-01-01 to 2024-12-31 (365 days)
🤔 Classroom / Managerial Analysis Questions:- What is the estimated additional daily likes associated with a major political event, holding other factors constant?
- Try AR(2) or AR(3) — does multi-day momentum improve model fit, consistent with how political stories unfold over several days?
- Based on the model, which factor matters more for predicting a high-engagement day: a political event occurring or a second post being published?
Canyon Creek Bistro operates three casual-upscale restaurants across the metro region: Downtown , Harborplace , and Suburban . Over the past quarter, average Yelp star ratings at the waterfront Harborplace location dropped from 4.2 to 2.6 stars despite identical menu recipes and supplier contracts. VP of Operations Elena Rostova suspects the waterfront kitchen is struggling with ticket pacing and plate temperature during peak dinner rushes, telling the analytics team: "Don't just hand me a positive-versus-negative sentiment average — I need to know whether hostess wait times, lukewarm entrees, or waterfront pricing surcharges are actually driving our 1-star and 2-star reviews."
📋 Dataset Variables: review_text: Unstructured Text star_rating: Outcome (1–5 Stars) location: Grouping Factor check_size_usd: Numeric Metric helpful_votes: Engagement Count
🤔 Classroom / Managerial Analysis Questions:- Capital Allocation Decision: Based on the Key-Driver Regression slopes (\\(b_k\\)) and construct prevalence rates (\\(\\pi_k\\)), should Elena Rostova prioritize redesigning the Harborplace host-stand reservation pacing or lowering menu prices?
- Lexical vs. Neural Evidence: How do the Fightin' Words distinctive tokens (\\(|z_w| > 1.96\\)) clarify the specific operational mechanism behind high BART entailment scores on Cold Food & Kitchen Execution ?
- Occlusion Attribution Check: Open the Review Inspector on a 1-star Harborplace review and examine the clause saliency shift (\\(\\Delta P_c\\)). Which exact clause contributes the largest probability jump?
VeloSound Acoustics launched the $199 VeloSound Pro True-Wireless Earbuds to strong 4.5-star reviews in Q1 ( Batch_v1.0 ). In Q3, the hardware team shipped factory firmware Batch_v1.4 alongside a revised charging case supplier, and verified product returns spiked from 8% to 38%. Director of Product Quality Marcus Vance needs to determine whether the star-rating collapse is driven by a firmware regression in Bluetooth multipoint switching or a hardware defect in the charging case pogo pins, noting: "Even our angry 1-star reviewers still praise the Active Noise Cancellation — we need zero-shot construct scoring to separate acoustic praise from connectivity and battery failures."
📋 Dataset Variables: review_text: Unstructured Text star_rating: Outcome (1–5 Stars) firmware_batch: Cohort Group helpful_votes: Community Votes verified_return: Return Indicator
🤔 Classroom / Managerial Analysis Questions:- Engineering Triage: If an over-the-air (OTA) patch can fix Bluetooth Multipoint Dropouts in two weeks while fixing Charging Case Failure requires a physical recall, how many star-rating points can VeloSound recover from the software patch alone?
- Why Sentiment Lexicons Fail Here: Why would a traditional dictionary sentiment score misclassify a 2-star review that says "Active noise cancellation completely silences airplane cabin roar, but Bluetooth multipoint drops every ten minutes" ?
- Group Contrast Swap: What happens to the Fightin' Words \\(z\\)-scores if you change the grouping column from firmware_batch to verified_return ( Yes vs. No )?
LuminaBotanicals sells a $68 Ceramide Barrier Repair Serum on a 60-day auto-replenish subscription. While subscribers in the Dry_Mature segment renew at 82% and rave about overnight hydration, subscribers in the Sensitive skin segment churn at an alarming rate, and Combination skin buyers complain about a clogged glass pipette dropper. Chief Brand Officer Priya Nair wants quantitative proof before reformulating the botanical essential oil blend or replacing the packaging vendor: "Show me whether our Sensitive segment churn is driven by lavender-bergamot fragrance irritation or by the defective glass dropper."
📋 Dataset Variables: review_text: Unstructured Text star_rating: Outcome (1–5 Stars) renewed_subscription: Retention Outcome (0/1) skin_segment: Customer Segment days_to_review: Usage Tenure
🤔 Classroom / Managerial Analysis Questions:- Fragrance-Free Variant Decision: Using the construct co-occurrence and regression VIFs between Redness & Irritation Reaction and Fragrance Sensitivity , should LuminaBotanicals launch an unscented SKU for the Sensitive segment?
- Packaging Vendor Negotiation: How much does Dropper Packaging & Pump Defect depress star ratings among customers who otherwise report high Glass-Skin Glow & Hydration Efficacy ?
- Lexical Contrast Across Segments: What clinical and sensory words distinguish Sensitive from Dry_Mature in the Fightin' Words table (\\(|z_w| > 1.96\\))?
AdditiveEdge , a DTC apparel brand scaling its paid social presence across Instagram, Facebook, and TikTok, wants to quantify the marginal revenue return on each additional dollar of weekly spend. Across 120 weeks of always-on activity the CMO, Rachel Voss, needed hard numbers: "I can't approve a 40% budget increase next quarter without a credible estimate of the revenue lift. Give me the slope."
📋 Dataset Variables: Weekly_Ad_Spend_USD: Weekly paid media spend in USD (Instagram + Facebook + TikTok combined) Weekly_Revenue_USD: Total weekly ecommerce revenue in USD
🤔 Classroom / Managerial Analysis Questions:- What is the estimated marginal revenue per additional $1,000 of paid social spend, and is it statistically significant?
- How much of weekly revenue variance does paid social spend explain? What might explain the remaining variance?
- Using the regression equation, forecast projected weekly revenue if spend increases to $40,000 next quarter.
Copperpaw Pet Supply , the online pet retailer, rotates four creative themes through its promotional email calendar — a plain Control, a Discount Focus, a New Arrivals showcase, and a Brand Story piece — and wants to know whether the theme moves average order value. The Director of Customer Marketing, Quinn Harlow, has a suspicion worth testing: "Everyone on my team has a favourite theme and a bar chart to prove it. Before I build four separate production tracks, show me whether those gaps survive a model."
📋 Dataset Variables: Email_Segment: Creative theme used in the send: Control_Group, Discount_Focus, New_Arrivals, or Brand_Story Average_Order_Value: Average order value in USD for that campaign send
🤔 Classroom / Managerial Analysis Questions:- Which theme shows the largest coefficient relative to Control_Group, and is it statistically distinguishable from zero?
- Sort the four group means and put them in a bar chart. Then look at the model. Why does the bar chart look more convincing than the regression output?
- If Copperpaw cannot detect a theme effect at n = 40 per group, what should Quinn do next: run a bigger test, accept the null, or measure a different outcome than AOV?
Novu Labs , a consumer skincare brand, ran a concept test with 50 focus group participants who rated their immediate reaction to a new product on a 5-point Likert scale and also completed a brand favorability instrument (0–100). The brand manager, Devon Park, wanted evidence before investing in full launch research: "If a quick Likert reaction score can predict deep brand favorability, we can cut 3 weeks from every concept test we run."
📋 Dataset Variables: LikertRating: Immediate concept reaction on 1–5 scale (1 = very negative, 5 = very positive) BrandRating: Composite brand favorability score (0–100) from validated instrument
🤔 Classroom / Managerial Analysis Questions:- What is the estimated brand favorability score for a respondent who rated the concept a 4 on the Likert scale?
- Is the slope statistically significant — and what does that mean for Devon's case to replace the full instrument with a quick rating?
- Given the R² and the modest n=50 sample, how would you communicate the precision limits of this regression to a non-statistical stakeholder?
Meridian Studios , an indie RPG developer, is balancing the economy for its D&D-style fantasy game ahead of Early Access launch. The game design lead, Todd Reyes, pulled 50 quest logs from playtesting (levels 1–20) and wants a regression model of how gold accumulation scales with character level: "If the slope is too steep above level 12, endgame players will break the shop economy in the first week. I need a number, not a gut feeling."
📋 Dataset Variables: character_level: Character level at time of the quest log (1–20 scale) gold_earned: Total gold accumulated across full playthrough to that level quest_number: Quest log identifier — leave this out of the model, it is a row label, not a predictor
🤔 Classroom / Managerial Analysis Questions:- What is the estimated gold earned at levels 18, 19, and 20 based on the regression equation?
- Is the linear model adequate, or do residuals suggest a polynomial (quadratic) model would fit better at higher levels?
- If the flagship endgame item costs 4,000 gold, at what predicted character level should a typical player be able to afford it?
You're a marketing analyst at Catalyst Digital , a mid-size digital agency based in Austin, Texas, managing paid media and content campaigns for 15 B2B clients ranging from SaaS startups to enterprise software vendors. The agency has developed a proprietary "Performance Score" (0-100) that combines reach, engagement rate, and conversion metrics into a single composite measure.
📋 Dataset Variables: performance_score: Composite campaign effectiveness score (0-100). Combines reach (30%), engagement rate (30%), and conversion rate (40%). A score of 50 represents "meeting expectations" based on historical benchmarks.
You're the analytics lead at Velocity Fitness , a growing gym chain with 8 locations across the Denver metro area. The company has 4,200 active members paying base memberships ranging from $49-89/month, plus optional services like personal training ($75/session), group classes ($15 drop-in), and retail products.
📋 Dataset Variables: lifetime_value: 12-month trailing customer value in dollars. Includes membership fees, personal training, class drop-ins, retail purchases, and facility rentals. Updated monthly in the CRM.
You're the product analyst at Thistledown Market , whose grocery delivery app serves the Calder Bay metro area. The grocer acquired 48,000 registered app users over the past 18 months through aggressive app-install campaigns, referral bonuses, and sign-up prompts at the tills of its nine stores. But a large chunk of those users downloaded the app and never came back.
📋 Dataset Variables: app_opens_30d: Number of times the user opened the app in the trailing 30 days (opens, not days: a heavy user can open it several times a day). Range in this sample: 0-86, with 17 users above 30. Zero means no opens since registration.
You're the marketing analyst at Artisan Coffee Collective , a specialty coffee roaster based in Portland, Oregon. The company operates 3 retail cafés, an e-commerce store, and a subscription program. With 2,800 active customers in the database, CEO Raj Mehta has noticed something interesting about purchase patterns.
📋 Dataset Variables: purchases_per_year: Count of distinct purchase transactions in the trailing 12 months. Includes in-store, online, and subscription renewals. Range: 1-19.
CraftHarvest , a specialty retail brand, ran a holiday campaign split-test to determine whether emotional storytelling outperforms functional utility messaging in email click-through rates. "We need to know if the emotional angle actually drives more clicks, or if it just feels better in the room." — Director of Email Marketing
📋 Dataset Variables: Creative: Email framing: Utility ("Practical Gifts") or Story ("Share Joy") Click: Binary: Clicked or Not Clicked
🤔 Classroom / Managerial Analysis Questions:- Is the 5.6 pp difference between Story and Utility click rates (clicks out of all 500 recipients of each version) statistically significant at α = 0.05?
- What are the expected cell counts under the null hypothesis of independence?
- Based on these results, which creative would you recommend for the next campaign and why?
ShopNest , a multi-channel e-commerce retailer, analyzed 346 sessions that reached the cart page to test whether the marketing acquisition channel is independently associated with checkout completion, before committing to a channel investment strategy at the conservative α = 0.01 threshold. "If paid search is closing 68% of its carts while organic only closes 51%, those 17 percentage points have to mean something — I want to know if channel independently predicts checkout, not just correlates with intent." — Director of Digital Marketing
📋 Dataset Variables: purchase: Cart outcome (Purchase vs. Abandoned cart) channel: Marketing acquisition channel (Email / Paid Search / Organic Search)
🤔 Classroom / Managerial Analysis Questions:- At α = 0.01, is there a statistically significant association between marketing channel and cart completion?
- Which channel–outcome cell shows the largest deviation from the expected counts under independence?
- Should ShopNest reallocate budget toward Paid Search? What confounds (e.g., audience intent, bid strategy, branded vs. non-branded queries) might explain the gap before attributing it to channel alone?
Tessmer Community Health analyzed whether visitors arriving on desktop vs. mobile devices behave differently on its new-patient enrollment page. "If mobile users are reaching for the phone instead of filling out the form, we need to know — that changes the whole page design." — Paloma Reinholt, Director of Outreach and Enrollment
📋 Dataset Variables: Device: Desktop or Mobile visitor Action: Submit Form, Click to Call, or No Action
🤔 Classroom / Managerial Analysis Questions:- Is device type significantly associated with user action at α = 0.10?
- Which device–action combination shows the largest deviation from expected counts?
- Should Tessmer redesign the mobile page to emphasize form submission over click-to-call? What does the data suggest?
FitPulse , a fitness app with 120 loyalty program members, ran a pilot test to determine whether loyalty segment (Enrolled, In-Store Only, Digital App Only) predicts how members respond to push notification incentives. "We have three loyalty segments and one push notification — if response patterns are truly independent of segment, we should send the same message to everyone. The data will tell us whether they are." — Retention Product Manager
📋 Dataset Variables: Action: Member response to push notification (Used Coupon / Made Visit / No Action) Loyalty Segment: Loyalty program tier (Enrolled / In-Store / Digital App)
🤔 Classroom / Managerial Analysis Questions:- Is loyalty segment significantly associated with action taken at α = 0.05? What does the p-value tell us about whether to personalize messages?
- Which segment–action combination shows the largest deviation from what would be expected under independence?
- If FitPulse were to personalize push notifications by segment, what specific call-to-action would you recommend for each of the three segments?
Greystone Keep , a competitive dungeon-crawling campaign tracked across 100 Level 10+ encounters, became the dataset for an unusual experiment: does character class (Barbarian vs. Wizard) determine survival? "If Wizards keep dying in high-level dungeons, we need to buff their defenses or give them Shield as a bonus action — the data will tell us if the gap is real." — DM Aldric
📋 Dataset Variables: Class: Character class: Barbarian or Wizard Survival: Encounter outcome: Survived or Perished
🤔 Classroom / Managerial Analysis Questions:- Is the association between character class and survival statistically significant at α = 0.05?
- What are the expected survival counts for each class if survival were independent of class?
- Given these results, should DM Aldric introduce a balancing mechanic for Wizards? What statistical threshold would justify the change?
Zenith Beverages conducted regional supermarket taste tests across 5 newly formulated ready-to-drink (RTD) iced tea flavors. The null hypothesis posits equal consumer preference across all 5 varieties ( H ₀: π₁ = π₂ = ... = π₅ = 0.20). The VP of Brand Portfolio wants to know: "Are consumers genuinely indifferent between flavors, or should we concentrate manufacturing lines and ad spend on a clear standout favorite?"
A cloud hosting provider logs unauthorized penetration attempts. The SecOps team tests whether attack frequency is evenly distributed across all 7 days of the week ( H ₀: π i = 1/7) to optimize SOC analyst staffing rotations.
Corporate governance audits whether executive candidate sourcing aligns with published industry labor benchmarks (35% Tech, 25% Finance, 20% Ops, 20% Marketing).
You're a marketing analyst at CloudMetrics , a mid-size B2B SaaS platform (4,200 active subscribers) providing analytics dashboards to e-commerce companies. The VP of Customer Success, Rachel Nguyen , has flagged that churn has been creeping up over the past two quarters.
You're on the growth team at NexaFlow , a marketing automation startup that sells to mid-market companies. The Head of Revenue, Marcus Chen , wants a lead scoring model so the sales team can prioritize the “hottest” inbound leads.
You're a data scientist at PixelReach , a digital advertising platform that serves display ads across 500+ publisher sites. The algorithms team lead, Aisha Patel , wants to improve the real-time bidding (RTB) model that predicts whether a user will click on an ad impression.
AxonTech , a smartphone manufacturer, ran a 1,200-task choice-based conjoint study to understand which product attributes drive purchase intent before committing to the design and pricing of its next flagship. "We're heading into our most expensive product launch in five years without a clear read on whether price is a barrier or whether camera quality is the real purchase driver — conjoint will tell us which levers actually matter." — VP of Product Strategy
📋 Dataset Variables: choice: Whether respondent selected this phone option (Binary: 1 = chosen, 0 = not chosen) brand: Smartphone brand (BrandX / BrandY / BrandZ) screen_size: Display size in inches (5.5" / 6.0" / 6.5") storage: Internal storage capacity (64GB / 128GB / 256GB) battery: Battery life in hours (12h / 18h / 24h) camera: Camera resolution tier (Standard 12MP / Enhanced 24MP / Professional 48MP)
🤔 Classroom / Managerial Analysis Questions:- Which attribute has the highest relative importance in driving smartphone choice — brand, camera, storage, or price?
- What is the estimated willingness-to-pay premium for upgrading from a Standard (12MP) to a Professional (48MP) camera?
- Based on the part-worth utilities, what combination of features and price point maximizes predicted choice share for AxonTech's BrandX against BrandY and BrandZ?
Nightjar , a challenger streaming service, commissioned a 2,000-task choice-based conjoint study among current streaming subscribers to optimize its content strategy, feature set, and pricing before its public launch. "Before we commit to a $50M originals budget for year one, I need to know if customers actually value exclusive content or if they just want lower prices and a big existing library." — Chief Strategy Officer
📋 Dataset Variables: choice: Whether respondent selected this streaming option (Binary: 1 = chosen, 0 = not chosen) content_library: Total title count (Small 5K / Medium 15K / Large 30K) originals: Exclusive original content volume (None / Moderate 5/mo / Extensive 15/mo) ad_free: Ad experience (With ads / Ad-free) streams: Simultaneous device streams allowed (1 / 2 / 4) quality: Video quality (HD / 4K Ultra HD)
🤔 Classroom / Managerial Analysis Questions:- Which attribute drives the most choice variance — price, ad experience, originals volume, or library size?
- What is the estimated monthly willingness-to-pay premium to switch from an ad-supported to an ad-free experience?
- If Nightjar can afford only one differentiating investment — doubling the originals budget or upgrading all streams to 4K — which option produces the higher predicted choice share gain?
Halbrook State University 's College of Business asked 50 graduate students to evaluate course design trade-offs in a choice-based conjoint study. The goal: redesign the Marketing Research course based on actual stated preferences, not faculty assumptions. "We've been arguing about flipped classroom vs. traditional lecture for two years — the conjoint data will tell us what students actually value, not what they say they value in town halls." — Associate Dean of Curriculum
📋 Dataset Variables: choice: Whether student selected this course design (Binary: 1 = chosen, 0 = not chosen) format: Course delivery format (Traditional Lecture / Flipped Classroom / Project-Based / Hybrid Online) weekly_hours: Required weekly time commitment in hours (2.5 / 3.5 / 4.5 / 5.5 / 6.5 / 8.0 — numeric/linear) assessment: Evaluation method (Exams / Projects / Mixed / Case Studies)
🤔 Classroom / Managerial Analysis Questions:- Which course attribute — format, weekly workload, or assessment method — has the highest relative importance in driving student choice?
- How much additional weekly workload would the average student accept in exchange for switching from Exams to Project-Based assessment?
- If Halbrook State can only run one course format, which format maximizes predicted choice share across the four student segments?
The Marlowe Riptide , a young club in the fast-growing Major League Women's Lacrosse (MLWL), ran a 2,500-task choice-based conjoint study among prospective members to design next season's membership before locking in seat maps, pricing, and perks. "We get one shot to launch the membership that defines this franchise for a decade. I don't want to guess whether families want clinic access or whether everyone just wants a cheaper ticket — conjoint will tell us exactly which levers fill the building." — VP of Ticketing & Fan Experience
📋 Dataset Variables: chosen: Whether the respondent picked this membership option (Binary: 1 = chosen, 0 = not chosen) seat_zone: Seat location (Endline GA / Sideline Reserved / Midfield Club) price: Per-game equivalent price ($12 / $18 / $24 / $30 / $36) flexibility: Ticket flexibility (Fixed Seats / Flex Credits / Full Transfer) perk: Membership perk (None / Merch Credit / Player Meet & Greet / Youth Clinic Access) promo_nights: Themed promo nights included (0 / 2 / 4)
🤔 Classroom / Managerial Analysis Questions:- Which attribute has the highest relative importance in driving membership choice — price, seat zone, perk, or flexibility?
- Estimate the willingness-to-pay (per game) for upgrading the perk from "None" to "Youth Clinic Access." How does it compare to the WTP for a Midfield Club seat?
- Using the part-worth utilities, design the membership package and price point that maximizes predicted take rate against a "None" option — and a second, premium tier for Founding Diehards.
Basecamp Burrito is a 40-unit Southwest fast-casual chain opening its first flagship location adjacent to Arizona State University. Menu Engineering Director Maya Lin intercepted 100 hungry campus diners who were already in line to buy lunch, asking each student to evaluate 10 choice sets of 3 combo configurations (a forced-choice design, since diners are already at the counter ordering a meal).
📋 Dataset Variables: Price: $8.99 (ref), $11.49 , $14.99 Protein: Chicken (ref), Plant-Based , Steak Portion: Regular (ref), Large (+50%) Heat Level: Mild (ref), Medium , Hot Side Included: Chips + Guac + Drink (ref), Chips + Salsa , Chips Only
🤔 Classroom / Managerial Analysis Questions:- Why does Heat Level show a near-zero average utility difference between Mild and Hot despite having the largest standard deviation in the table?
- Does upgrading Side Included from Chips Only to Chips + Guac + Drink offset the utility loss of raising price from $8.99 to $11.49 ?
StreamWave is preparing to launch a new US streaming service in a crowded market where consumers already suffer from "subscription fatigue." VP of Pricing Devon Brooks surveyed 120 prospective subscribers across 10 choice tasks . Crucially, every task included 3 subscription plans plus a 4th "None — I would not subscribe to any of these" option so the team can measure how many households simply walk away when prices rise or ads are forced.
📋 Dataset Variables: Monthly Price: $6.99 (ref), $11.99 , $16.99 Content Focus: All-Access Bundle (ref), Live Sports & News , Movies & Series Ad Experience: Full Ads (ref), Pre-Roll Only , No Ads Simultaneous Streams: 1 Screen (ref), 2 Screens , 4 Screens Offline Downloads: No Downloads (ref), 5 Titles , Unlimited Alternative = None: ASC_None (Outside Option Hurdle)
🤔 Classroom / Managerial Analysis Questions:- When StreamWave offers only a single $11.99 standard plan against the Rival Sports+ Streamer, what percentage of the 10,000-household market chooses "None / Would Not Buy" ?
- If StreamWave upgrades Ad Experience from Full Ads to No Ads , does the gain come primarily from stealing the rival's customers or from converting "Would Not Buy" holdouts?
SoundPulse is a consumer audio startup designing its debut true-wireless earbud line. Because most shoppers already own a working pair of earbuds, Product Lead Elena Rostova included a "None — I would keep my current headphones and not buy any of these" option in all 10 choice tasks across 120 surveyed shoppers . In the survey, shoppers walked away in 32.4% of tasks whenever the offered combinations missed their core use-case requirements or price ceiling.
📋 Dataset Variables: Price: $49 (ref), $99 , $179 Battery Life: 4 Hours (ref), 6 Hours , 10 Hours Noise Cancellation: No ANC (ref), Passive Only , Active ANC Fit Style: In-Ear (sealed) (ref), Open-Ear , Sport Hooks Water Resistance: No Protection (ref), Splash-Proof (IPX4) , Fully Waterproof (IPX7) Alternative = None: ASC_None (Walk-Away Threshold)
🤔 Classroom / Managerial Analysis Questions:- Compare Fit Style under Mean Individual Importance vs. Importance of Average Utilities—why do the two metrics diverge so sharply?
- Design a two-SKU SoundPulse lineup (e.g., SoundPulse Sport at $99 + SoundPulse Value at $49) competing against ApexAudio Pro ($179) . How much can you reduce the "Would Not Buy" walk-away rate?
AxonTech , a smartphone manufacturer, ran a 1,200-task choice-based conjoint study to understand which product attributes drive purchase intent before committing to the design and pricing of its next flagship. "We're heading into our most expensive product launch in five years without a clear read on whether price is a barrier or whether camera quality is the real purchase driver — conjoint will tell us which levers actually matter." — VP of Product Strategy
📋 Dataset Variables: choice: Whether respondent selected this phone option (Binary: 1 = chosen, 0 = not chosen) brand: Smartphone brand (BrandX / BrandY / BrandZ) screen_size: Display size in inches (5.5" / 6.0" / 6.5") storage: Internal storage capacity (64GB / 128GB / 256GB) battery: Battery life in hours (12h / 18h / 24h) camera: Camera resolution tier (Standard 12MP / Enhanced 24MP / Professional 48MP)
🤔 Classroom / Managerial Analysis Questions:- Which attribute has the highest relative importance in driving smartphone choice — brand, camera, storage, or price?
- What is the estimated willingness-to-pay premium for upgrading from a Standard (12MP) to a Professional (48MP) camera?
- Based on the part-worth utilities, what combination of features and price point maximizes predicted choice share for AxonTech's BrandX against BrandY and BrandZ?
Nightjar , a challenger streaming service, commissioned a 2,000-task choice-based conjoint study among current streaming subscribers to optimize its content strategy, feature set, and pricing before its public launch. "Before we commit to a $50M originals budget for year one, I need to know if customers actually value exclusive content or if they just want lower prices and a big existing library." — Chief Strategy Officer
📋 Dataset Variables: choice: Whether respondent selected this streaming option (Binary: 1 = chosen, 0 = not chosen) content_library: Total title count (Small 5K / Medium 15K / Large 30K) originals: Exclusive original content volume (None / Moderate 5/mo / Extensive 15/mo) ad_free: Ad experience (With ads / Ad-free) streams: Simultaneous device streams allowed (1 / 2 / 4) quality: Video quality (HD / 4K Ultra HD)
🤔 Classroom / Managerial Analysis Questions:- Which attribute drives the most choice variance — price, ad experience, originals volume, or library size?
- What is the estimated monthly willingness-to-pay premium to switch from an ad-supported to an ad-free experience?
- If Nightjar can afford only one differentiating investment — doubling the originals budget or upgrading all streams to 4K — which option produces the higher predicted choice share gain?
Halbrook State University 's College of Business asked 50 graduate students to evaluate course design trade-offs in a choice-based conjoint study. The goal: redesign the Marketing Research course based on actual stated preferences, not faculty assumptions. "We've been arguing about flipped classroom vs. traditional lecture for two years — the conjoint data will tell us what students actually value, not what they say they value in town halls." — Associate Dean of Curriculum
📋 Dataset Variables: choice: Whether student selected this course design (Binary: 1 = chosen, 0 = not chosen) format: Course delivery format (Traditional Lecture / Flipped Classroom / Project-Based / Hybrid Online) weekly_hours: Required weekly time commitment in hours (2.5 / 3.5 / 4.5 / 5.5 / 6.5 / 8.0 — numeric/linear) assessment: Evaluation method (Exams / Projects / Mixed / Case Studies)
🤔 Classroom / Managerial Analysis Questions:- Which course attribute — format, weekly workload, or assessment method — has the highest relative importance in driving student choice?
- How much additional weekly workload would the average student accept in exchange for switching from Exams to Project-Based assessment?
- If Halbrook State can only run one course format, which format maximizes predicted choice share across the four student segments?
The Marlowe Riptide , a young club in the fast-growing Major League Women's Lacrosse (MLWL), ran a 2,500-task choice-based conjoint study among prospective members to design next season's membership before locking in seat maps, pricing, and perks. "We get one shot to launch the membership that defines this franchise for a decade. I don't want to guess whether families want clinic access or whether everyone just wants a cheaper ticket — conjoint will tell us exactly which levers fill the building." — VP of Ticketing & Fan Experience
📋 Dataset Variables: chosen: Whether the respondent picked this membership option (Binary: 1 = chosen, 0 = not chosen) seat_zone: Seat location (Endline GA / Sideline Reserved / Midfield Club) price: Per-game equivalent price ($12 / $18 / $24 / $30 / $36) flexibility: Ticket flexibility (Fixed Seats / Flex Credits / Full Transfer) perk: Membership perk (None / Merch Credit / Player Meet & Greet / Youth Clinic Access) promo_nights: Themed promo nights included (0 / 2 / 4)
🤔 Classroom / Managerial Analysis Questions:- Which attribute has the highest relative importance in driving membership choice — price, seat zone, perk, or flexibility?
- Estimate the willingness-to-pay (per game) for upgrading the perk from "None" to "Youth Clinic Access." How does it compare to the WTP for a Midfield Club seat?
- Using the part-worth utilities, design the membership package and price point that maximizes predicted take rate against a "None" option — and a second, premium tier for Founding Diehards.
Crave operates a regional restaurant delivery marketplace out of Austin, Texas. Last quarter, an external lifecycle agency ran a mobile push-notification pilot and arrived at Friday's growth review with a slide deck claiming that weekly_push_alerts significantly increases 90-day dinner orders ( "Poisson p = 0.004 — give us $95,000 to roll this cadence out across all six metro markets" ).
📋 Dataset Variables: orders_90d: Outcome (Count) · Integer \\(\\ge 0\\) cravepass_tier: Categorical · Baseline: Standard avg_delivery_fee_usd: Continuous · USD ($0.99–$7.99) promo_vouchers_redeemed: Continuous · Integer (0–9) weekly_push_alerts: Continuous · Integer (0–6) diner_zone_miles: Continuous · Miles (0.4–9.8)
Mosaic Analytics sells usage-based cloud data pipeline software from Seattle, Washington. During annual budget planning, a junior finance analyst ran a count regression on annual_support_tickets without adjusting for how long each customer had been on the platform—and concluded that customers who received Guided Launch Onboarding file 82% more support tickets (\\(\\text{IRR} = 1.82, p < 0.001\\)) than self-serve accounts!
📋 Dataset Variables: annual_support_tickets: Outcome (Count) · Integer \\(\\ge 0\\) active_account_months: Exposure Offset (\\(E_i > 0\\)) · Months (1.0–24.0) guided_onboarding: Categorical · Baseline: Self_Serve active_data_pipelines: Continuous · Integer (1–32) workspace_seats: Continuous · Integer (3–65) plan_tier: Categorical · Baseline: Growth
Copperpaw Pet Supply is a Denver-based DTC pet nutrition brand. Retention Lead Quinn Harlow is deciding how to allocate next year's acquisition & onboarding budget between two programs: a $25 First-Bag Promo Code ( Promo_25_Off ) and a complimentary 15-Minute Tele-Vet Nutrition Consult ( Vet_Consult ) . Across 720 new trial households, 41% never placed a repeat order (\\(Y = 0\\)), while active repeaters ordered 1 to 14 bags over the following 12 months.
📋 Dataset Variables: annual_bag_orders: Outcome (Count) · Integer \\(\\ge 0\\) first_bag_promo: Categorical · Baseline: Full_Price vet_consult_completed: Categorical · Baseline: No_Consult dog_weight_lbs: Continuous · Pounds (11.5–96.0) dogs_in_household: Continuous · Integer (1–3)
Thistledown Market is a 14-store specialty grocer in Minneapolis, Minnesota known for its scratch sourdough and pastry counters. Last month, store managers in outer-ring suburbs complained that headquarters' linear regression forecast predicted negative monthly bakery visits (\\(\\hat{Y} = -1.85\\)) for loyalty members living more than 9 miles from a store, corrupting regional labor-allocation totals.
📋 Dataset Variables: monthly_bakery_visits: Outcome (Count) · Integer \\(\\ge 0\\) drive_distance_miles: Continuous · Miles (0.4–14.5) shopping_day_pattern: Categorical · Baseline: Weekday_Commuter household_size: Continuous · Integer (1–5) coffee_bar_buyer: Binary (0/1) · Indicator
You're a marketing analyst at Peak Performance Outdoor , a specialty retailer with 60 stores across five U.S. regions. The VP of Sales, Marcus Chen, wants to understand how weekly revenue distributions differ across regions — not just averages, but the full shape. "I need to know if the Southwest's low average is because ALL stores are low, or if it's a few underperformers dragging the number down," he said at last Tuesday's leadership meeting.
📋 Dataset Variables: region: Store region: Northeast, Southeast, Midwest, West, Southwest weekly_revenue_k: Weekly store revenue in thousands of dollars
You're the analytics lead at BrightPath Digital , a mid-size e-commerce company selling sustainable home goods. The CMO, Sarah Torres, is presenting to the board next week and needs to show the relationship between daily ad spend and the number of conversions it buys. "We've put about $100K into Google Ads across these 200 campaign-days. I need to show whether the next dollar still pulls its weight — or whether we hit diminishing returns somewhere," she told you over coffee on Monday morning.
📋 Dataset Variables: daily_ad_spend: Google Ads spend on that campaign-day, in dollars (mostly $150–$1,200) daily_conversions: Purchases attributed to those ads on that campaign-day (a count, not a rate)
You work as a CX analyst at Harbor Market , a regional department store chain with 15 locations. After the Q4 satisfaction survey (N = 400 responses) came in, the VP of Customer Experience, Diana Reyes, noticed that overall satisfaction scores were flat — but she suspects the averages are hiding important differences between product departments. "Food & Beverage complaints have been climbing, but I can't tell if that's a noisy minority or a real trend. Break it down by product category for me."
📋 Dataset Variables: product_category: Department: Electronics, Apparel, Home & Garden, Food & Beverage satisfaction_level: Response: Very Dissatisfied → Very Satisfied (5 levels)
You're a growth analyst at TechFlow , a B2B SaaS company with $4M ARR. The Head of Growth, Jake Morrison, is building the 2026 pipeline plan and pulled every deal the company closed last year. "We've been spreading budget evenly across sources, but I suspect they're buying fundamentally different deals. Show me the full picture — not just the average deal size." Each row here is one closed-won deal , so every number is a fact about that single customer. Three variables means several pairs to explore: Source × Deal Size (cat × cont), Source × Sales Cycle (cat × cont), or Deal Size × Sales Cycle (cont × cont).
📋 Dataset Variables: lead_source: Where the deal originated: Product-Led Trial, Paid Search, Inbound Content, Events / Webinar, Partner Referral, Outbound SDR first_year_acv_usd: First-year annual contract value for that deal, in dollars (roughly $2K–$150K) days_to_close: Days from first touch to signature for that deal
You're the analytics lead at Nexus Arena , a competitive esports platform with 2 million active players. The Head of Player Development, Kai Nakamura, is designing a new coaching program and needs to understand the interplay between player role, rank, mental state, and performance. "We know higher-ranked players react faster — but is that the full story? How does competitive anxiety fit in? Is there a sweet spot, or does anxiety only hurt? And do Tanks and Supports experience the game differently than Carries?" This dataset includes a psychological measure (a competitive anxiety composite score) alongside traditional performance metrics.
📋 Dataset Variables: player_role: Nominal — Carry, Support, Tank, Flex, Controller rank_tier: Ordinal — Bronze → Silver → Gold → Platinum → Diamond → Master competitive_anxiety: Interval — Competitive anxiety composite, the mean of 8 self-report items (1.0–7.0 scale) reaction_time_ms: Ratio — Average in-game reaction time in milliseconds win_rate_pct: Ratio — Overall win rate as a percentage
You're a brand strategist at Meridian Insights , a market research consultancy. Your client, a consumer goods conglomerate, wants to track how brand health metrics have evolved over the past two years for four competing brands. The SVP of Marketing, Diana Reyes, needs to present quarterly brand health trends at the board meeting. "I need to see who's rising, who's falling, and whether our ad spend is actually moving the needle on familiarity," she said during your kickoff call. Each row is one survey respondent in one month , rating one brand — about 8–10 respondents per brand per month.
📋 Dataset Variables: month: Temporal — Monthly period from 2023-01 to 2024-12 brand: Nominal — Four competing brands: Apex Athletics, NovaSpark, EcoVibe, BlueShift brand_familiarity_0_10: Interval — "How familiar are you with this brand?", answered 0–10 by that respondent purchase_intent_score: Interval — That respondent's purchase intent, 1–10 ad_spend_k: Ratio — The brand's ad spend that month, in thousands of dollars. This is a brand-month fact merged onto every respondent row , not something the respondent reported — which is exactly how survey data and media data get joined in practice
You're a product manager at Luminary Electronics , which just launched its flagship wireless earbuds, the "Aura Pro." The product has been in market for one full year, sold across three channels. The CEO, James Whitfield, wants to understand the full product lifecycle story before the annual planning cycle. "Show me where each channel peaked, when satisfaction dipped, and whether the return rate is under control as we enter Q4 clearance," he told the product team at last Friday's all-hands. Each row is one channel in one week — 52 weeks × 3 channels.
📋 Dataset Variables: week: Temporal — Week of the year (W01 through W52) channel: Nominal — Sales channel: Online, Retail, or Wholesale units_sold: Ratio — Units sold that week through that channel avg_review_score: Interval — Mean post-purchase review score from that week's buyers (1–7 scale) return_rate_pct: Ratio — Returns that week as a percentage of that week's units sold
You're the Head of Analytics at Vortex Games , publisher of the competitive FPS Iron Vanguard — a free-to-play title with about 3.5 million monthly active users. The CFO, Priya Sharma, is preparing the annual investor deck and needs you to untangle how gameplay engagement and in-game store spending interact across platforms and spender segments. "Mobile is our fastest-growing platform, but I can't tell if those players are actually spending or just inflating our DAU numbers. And do our whale spenders behave differently during tournament weeks? I need the full 365-day picture — no cherry-picked quarters." Each row is one platform × spender-tier segment on one day , so the four tiers split that day's player base rather than overlapping: 365 days × 3 platforms × 4 tiers.
📋 Dataset Variables: day: Temporal — Daily date from 2025-01-01 to 2025-12-31 (365 days) featured_mode: Nominal — The mode the storefront promoted that day: Ranked, Casual, or Tournament. A property of the day , not of the segment platform: Nominal — PC, Console, or Mobile spender_tier: Ordinal — Free → Minnow → Dolphin → Whale (55% / 25% / 13% / 7% of players) daily_active_users_k: Ratio — Active users in that platform-tier segment that day, in thousands avg_session_min: Ratio — Mean session length for that segment that day, in minutes
You're the retention marketing analyst at FitLife Gym , a regional fitness chain with 12 locations and 3,500 active members across the Midwest. VP of Member Services, Marcus Chen , is concerned about rising churn rates—currently at 6.2% monthly, up from 4.8% last year. He's asked you to build a predictive model to identify at-risk members before they cancel, enabling targeted retention offers.
📋 Dataset Variables: churned: Whether the member cancelled within 30 days (1 = Churned, 0 = Stayed). This is your target variable to predict. months_member: How long the member has been active (1-48 months). New members (< 6 months) tend to have higher churn risk. visits_per_month: Average gym visits per month over the last 3 months (0-30). Engagement is a leading indicator of retention. contract_type: Membership contract: "Month-to-Month", "Annual", or "Two-Year". Month-to-month members can cancel anytime without penalty. age: Member's age in years (18-72). Different age groups may have different commitment patterns.
🤔 Classroom / Managerial Analysis Questions:- What combination of tenure and visit frequency best identifies high-risk members? At what thresholds would you trigger an intervention?
- Should FitLife offer the same retention strategy to month-to-month vs. annual members, or does the tree suggest different risk profiles?
- If Marcus can only afford to contact 200 members/month with retention offers, which leaf nodes would you prioritize?
You're supporting the sales team at CloudSync , a B2B SaaS company selling cloud storage solutions to small and mid-size businesses. Head of Sales, Jennifer Park , has a team of 8 SDRs who are overwhelmed with inbound leads from the website. She needs a way to prioritize which leads to call first.
📋 Dataset Variables: converted: Whether the lead converted to a paid customer within 60 days (1 = Converted, 0 = Did Not Convert). website_visits: Number of website visits before signup (1-50). More visits may indicate higher intent or more research needed. email_opens: Number of marketing emails opened in first 2 weeks (0-12). Engagement with nurture content signals interest. company_size: Self-reported company size: "Small" (1-50 employees), "Medium" (51-200), or "Enterprise" (201+). days_since_signup: Days between signup and the first sales call (1-60). Lead freshness often correlates with responsiveness.
🤔 Classroom / Managerial Analysis Questions:- Define a "Hot Lead" rule based on the tree. Which combination of features gives the highest conversion probability?
- Jennifer wants to create three buckets: Hot (call immediately), Warm (call within 48h), Cold (nurture via email only). How would you map tree leaves to these buckets?
- The tree shows company_size matters—but is the pattern that bigger = better, or is there a sweet spot in the middle?
You're the data analyst for Artisan Coffee Co , a direct-to-consumer specialty coffee subscription service with 15,000 active customers. CMO David Rodriguez wants to implement personalized marketing, but first needs to classify customers into value tiers: Premium , Standard , and Budget .
📋 Dataset Variables: segment: Customer value segment after 6 months: "Premium" (top 20% LTV), "Standard" (middle 50%), or "Budget" (bottom 30%). first_order_value: Dollar amount of customer's first order ($15-$150). May signal price sensitivity and quality expectations. orders_first_90_days: Number of orders placed in first 90 days (1-12). Early purchase frequency predicts long-term behavior. subscription_status: Whether customer enrolled in subscription: "Subscriber" or "One-Time". Subscribers have 3x higher retention. referral_count: Number of friends referred in first 90 days (0-5). Referrers tend to be brand advocates with high engagement.
🤔 Classroom / Managerial Analysis Questions:- What's the simplest rule to identify Premium customers with high confidence? David wants a "Premium fast-track" trigger for the welcome sequence.
- For customers predicted as Standard, what additional action (referral program, subscription upsell) might move them toward Premium?
- The model makes mistakes—which misclassification is most costly? (Treating a Premium as Budget? Or Budget as Premium?) How would you adjust?
RetailPlus , a regional department store chain operating across 12 markets, rolled out an in-store mobile app across 4 test cities while keeping 8 similar control cities app-free. Store revenue is tracked over 12 monthly quarters. Reproduces the textbook chapter figure ($420K → $465K vs $400K → $415K).
📋 Dataset Variables: market: Market location identifier (4 test cities: M1–M4; 8 control cities: M5–M12) period: Time period (Quarters 1–12; App launched at Quarter 7) sales: Average sales in thousands of dollars per store ($k / store / quarter) treated: Treatment indicator: 1 = test market (with app), 0 = control market (app-free)
🤔 Classroom / Managerial Analysis Questions:- Why does comparing test stores before vs. after the launch wrongly credit the app with $15K of market growth?
- Why does comparing test stores to control stores only in the post-period overstate the app's lift by $50K?
- What is the business risk of using naive comparisons to justify capital allocation for mobile app features?
Hearthstone Roasters , a specialty coffee brand with retail cafes across the Western United States, ran an 8-week TV campaign flight across 6 Pacific Northwest DMAs against 14 Western control DMAs over 26 weeks. Weekly store revenues average ~$50,000.
📋 Dataset Variables: market: Designated Market Area (DMA) identifier (6 test DMAs, 14 control DMAs) period: Calendar week (Weeks 1–26; TV flight launched at Week 14) sales: Average weekly cafe sales in dollars (USD / store / week) treated: 1 = test DMA exposed to TV advertising, 0 = unexposed control DMA
🤔 Classroom / Managerial Analysis Questions:- How much weekly incremental revenue did the TV flight generate per store?
- Why is randomization inference more reliable than standard regression formulas when analyzing a small number of geographic markets?
- At $2.4M for a national rollout, what minimum incremental lift is required across the company's 400 stores to break even?
Pinnacle Fitness , a premium health club operator, rolled out a digital class-booking app first to the 8 clubs whose general managers eagerly volunteered. Over 20 months, naive DiD suggests a handsome lift of +$2,377 per month in ancillary revenue.
📋 Dataset Variables: market: Club location identifier (8 volunteer clubs, 16 non-volunteer clubs) period: Month number (Months 1–20; App launched at Month 11) sales: Monthly ancillary club revenue in dollars (USD / club / month) treated: 1 = volunteer club with app, 0 = non-volunteer control club
🤔 Classroom / Managerial Analysis Questions:- Why did the naive DiD regression report a statistically significant lift when the app actually generated $0 in incremental revenue?
- How does the pre-trend test protect management from rolling out an ineffective app across 150 clubs?
- When test units are chosen by voluntary opt-in, why does Difference-in-Differences break down?
Oakline Furniture remodeled 10 flagship showrooms to an open-concept experience while keeping 30 stores unchanged. During month 9, partial construction closures caused a temporary revenue drop immediately prior to the grand reopening in month 10.
📋 Dataset Variables: market: Showroom store identifier (10 remodeled flagships, 30 control showrooms) period: Month number (Months 1–18; Grand reopening at Month 10) sales: Monthly showroom revenue in dollars (USD / store / month) treated: 1 = remodeled showroom, 0 = unremodeled control showroom
🤔 Classroom / Managerial Analysis Questions:- Why does treating the construction month (month 9) as an ordinary pre-intervention period distort the parallel trends diagnostic?
- How does inspecting the event study plot allow you to separate temporary transition disruption from ongoing campaign lift?
- If the construction closure cost $35,000 and the ongoing lift is $18,000/month, how many months after reopening does the remodel break even?
Kestrel Market , a neighbourhood grocer, keeps running out of its best-selling oat milk. The truck comes Monday and Thursday, and once the shelf is empty it tends to stay empty until the next truck. "The distributor will add a Saturday drop for $180 a week. I need to know how many empty-shelf days that actually saves, not how many it saves on a good week." Priya Natarajan, Category Manager
📋 Dataset Variables: date: The day, Monday 1 July 2024 to Sunday 28 June 2026 out_of_stock: 1 if the shelf was empty at closing, 0 if not promo: 1 on days of the monthly four-day oat milk promotion delivery_day: 1 on truck days (Monday and Thursday)
🤔 Classroom / Managerial Analysis Questions:- What is the outcome-lag odds ratio, and what does it say about the shelf in plain words?
- How many empty-shelf days a year would a Saturday delivery save in the long run?
- Would you recommend the third delivery? What else would you want to know first?
Ferngrove Fresh delivers groceries in one city, and on some days every delivery slot sells out. The operations lead has noticed that sell-outs bunch together and wants a rule: "When we sell out, put two extra drivers on the next day. Sell-outs come in streaks." Marcus Bell, Head of Operations. Extra drivers cost $400 a day.
📋 Dataset Variables: date: The day, Monday 1 July 2024 to Sunday 28 June 2026 sold_out: 1 if every delivery slot sold out, 0 if not rain: 1 on days with rain in the forecast that morning weekend: 1 on Saturday and Sunday
🤔 Classroom / Managerial Analysis Questions:- With no drivers, how strong does the momentum look? With both drivers, how strong is it?
- What explains the difference, in one sentence a manager would accept?
- Would you adopt the "extra drivers after a sell-out" rule? What rule would you suggest instead?
Brightwick Candles sells hand-poured candles online and sets a daily sales target. When the shop misses it, the owner usually runs a one-day flash sale the next day. "The days we run flash sales are our worst days. I think they cheapen the brand and I want to stop." Elena Brightwick, Owner
📋 Dataset Variables: date: The day, Monday 1 July 2024 to Sunday 28 June 2026 hit_target: 1 if the day's sales reached the target, 0 if not flash_sale: 1 on flash-sale days weekend: 1 on Saturday and Sunday
🤔 Classroom / Managerial Analysis Questions:- With no memory in the model, what does the flash-sale coefficient say? With one lag?
- Why do the two answers differ? Which one answers the owner's question?
- What would you tell the owner, in two sentences?
The same oat milk shelf, but a year in Kestrel switched to a cheaper distributor. "The new supplier saves us 6% on cost. Has anything else changed?" Priya Natarajan, Category Manager
📋 Dataset Variables: date: The day, Monday 1 July 2024 to Sunday 28 June 2026 out_of_stock: 1 if the shelf was empty at closing, 0 if not promo: 1 on days of the monthly four-day promotion delivery_day: 1 on truck days (Monday and Thursday)
🤔 Classroom / Managerial Analysis Questions:- What does the stability check say, and what does the rolling chart show around the middle of the data?
- Is the cheaper supplier really saving 6%, once empty-shelf days are counted?
- How would you change the analysis to describe the new supplier properly?
Northvale Outdoor tracks the average cost-per-click ($1.65 to $2.45) it pays across 78 weeks on branded paid search ($420,000 annual search budget). The bidding is a live second-price auction against competitors nobody can see, so each week's price starts from last week's and moves by whatever the auction does. "Finance wants a CPC number for next quarter's budget. I keep being told to build a proper model. I am not convinced a proper model will help." — Paid Media Manager
📋 Dataset Variables: week_starting: Weekly Monday date stamp (78 consecutive weeks, no gaps) avg_cost_per_click: Weekly average paid-search CPC in USD (continuous target to forecast)
🤔 Classroom / Managerial Analysis Questions:- Read the ratio column: Every method here is scored against "repeat last week". Which ones come out above 1.00, and what does that number mean?
- Test holdout sensitivity: Push the sliders around until some method scores below 1.00. Then move Periods to hide a few weeks either way (try 8, 14 and 16). Does the same setting still win, by the same margin? What does that tell you about a score you tuned against? Then press Score from many origins with the setting you tuned: at how many origins does it beat naive, and what is its pooled ratio?
- Managerial pushback: If a colleague showed you a CPC forecast with a confident downward line on it, what would you now ask them?
Kettle Software , a B2B project-management SaaS ($4.8M ARR), has grown its subscriber base steadily across 48 months from 1,200 to over 5,200 accounts with no seasonal pattern worth speaking of — business software gets bought all year round. "We need a headcount plan for support. Support load tracks subscribers almost exactly, so whatever the subscriber forecast says, that is the plan." — COO
📋 Dataset Variables: month: Monthly period label (YYYY-MM across 48 months) active_subscribers: Active paying B2B subscriber accounts at month end
🤔 Classroom / Managerial Analysis Questions:- Diagnose systematic bias: Moving average and smoothing both score above 1.00. Look at the forecast line for each — in which direction are they wrong, and why?
- Horizon risk: Extend the forecast horizon. What happens to the trend method the further out you go, and should you believe it?
- Operational staffing decision: Support headcount is being planned from this. Which number would you give the COO, and what would you say about it?
Ashgrove Garden Centre sells plants, tools and compost across 5 regional sites ($6.2M annual turnover, 60 months of unit records). Demand is wildly different in May and December, and has grown modestly year on year. "Everyone here knows spring is busy. What I cannot do is tell the buyers how busy, five months before it happens." — Head of Merchandising
📋 Dataset Variables: month: Monthly calendar period (60 months across 5 full annual cycles) units_sold: Total retail units sold across all 5 garden centre locations
🤔 Classroom / Managerial Analysis Questions:- Complexity vs. accuracy: Run "same as this month last year", then Holt-Winters. How much does the extra complexity actually buy?
- Seasonal amplitude: Look at the seasonal panel in Act 1. Which months sit furthest above and below the trend line, and by how many units?
- Procurement horizon: The buyers order stock four months ahead. Does the forecast stay trustworthy that far out?
Foldwell Apparel sells direct-to-consumer clothing ($1.8M average annual run rate across 72 months). Revenue has a huge Q4 holiday peak, a steady climb, 4 big promotional pushes scattered across 6 years, and 1 month where a supplier failure took a chunk out of stock. "The model is fine until it isn't. Every time we run a big promo the forecast is embarrassing." — Director of Growth
📋 Dataset Variables: month: Monthly calendar period (72 months across 6 years) revenue: Monthly direct-to-consumer revenue in USD promotion: Promotional month indicator (1 = major campaign push, 0 = normal month) — univariate smoothing cannot use it
🤔 Classroom / Managerial Analysis Questions:- Line up the spikes: Find the months with the largest errors. Line them up against the promotion column in Step 1. What do you notice?
- Structural limit of smoothing: Would a better choice of method, or better slider settings, have caught those months? Why not?
- Step up to ARX: What would you have to know in advance for next year's promo months to be forecastable in ARX?
Context: The university district in Austin, TX has three competing coffee shops within walking distance of campus. Campus Perk survives on foot traffic, The Daily Grind is beloved for ambiance, and MegaBrew Coffee just opened a flagship store twice the size of either competitor.
Context: Five grocery stores compete across the Austin metro area, ranging from a local organic market to two MegaGrocer big-box locations. City council has asked the planning commission to assess whether any residential zones are "grocery deserts" — areas where no store has more than 15% visit probability.
Context: BankFirst has two branches in Austin (Downtown full-service + Mueller express) and competes against RivalBank, which holds two full-service locations (South Austin + The Domain). BankFirst's CEO, Patricia Okafor, has budget to open one new branch and needs to choose the optimal location.
Context: Five coffee shops compete for the SDSU College Area market in San Diego, CA. Aztec Grind sits on campus with late-night hours but limited space. Hardy's Roasters is a popular independent on College Avenue. Chain Coffee dominates El Cajon Blvd with brand recognition and drive-through access. Two smaller players — Brewed Scholar and Java Junction — round out the walkable neighborhood.
Your ecommerce brand is planning its peak holiday campaign. The CRM team is debating whether it's worth investing extra creative time in a more polished, benefit-led subject line versus reusing the simple control line.
You manage CRM for a mid-sized retailer with a growing loyalty program. The team suspects that generic "points balance" updates are underperforming, and that a more tailored upsell email could push members toward bigger baskets.
Your growth team is considering a refer-a-friend incentive that offers existing customers a small credit when a friend makes their first purchase. Finance wants evidence that this generates enough downstream revenue to cover costs.
You work on insights for a subscription streaming platform. Product marketing suspects that "always-on" subscribers behave differently from "seasonal bingers" who reactivate around marquee releases.
Your adventuring party is in a heated tavern debate: do warriors or rogues deal more damage per round in combat? The fighter insists that raw strength gives warriors the edge, while the rogue argues that sneak attacks create superior output.
RetainIQ , a subscription-based SaaS company, wants to segment its customer base to tailor retention campaigns, identify high-value accounts for VIP treatment, and spot customers who are drifting away before they churn.You're the CRM analyst tasked with building actionable segments from 12 months of transaction history.
📋 Dataset Variables: recency_days: Days since last purchase or activity (lower = more recent) frequency: Number of purchases in last 12 months monetary_value: Total spending ($) in last 12 months
🤔 Classroom / Managerial Analysis Questions:- Which segment has the highest average monetary value and could be targeted for upsell?
- How many customers fall into the group that used to buy often but has not bought recently?
- What retention campaign strategy would you recommend for each segment?
Drip Studio , a B2C email marketing agency, has accumulated 18 months of campaign data across 450 sends. Leadership wants to identify distinct performance clusters so the patterns that work can be replicated and the ones that do not retired or redesigned.
📋 Dataset Variables: open_rate: Email open rate (0–1 scale) click_rate: Click-through rate (0–1 scale) conversion_rate: Conversion rate from email (0–1 scale) unsubscribe_rate: Unsubscribe rate (0–1 scale, lower = better)
🤔 Classroom / Managerial Analysis Questions:- Which cluster represents your best-performing campaigns? What metrics define it?
- Are there campaigns with high open rates but poor conversions — what creative fix does that suggest?
- What percentage of campaigns fall into the cluster that is weak on every metric?
Meridian Consumer Goods carries 300 SKUs across five categories. The executive team wants to rationalize the product line by identifying strategic clusters based on pricing, profitability, and market performance — then making invest, hold, or discontinue decisions for each group.
📋 Dataset Variables: price_point: Average retail price ($) gross_margin: Gross margin percentage (0–1 scale) sales_volume: Annual unit sales (thousands) market_share: Category market share (0–1 scale)
🤔 Classroom / Managerial Analysis Questions:- Which cluster holds the products that deserve continued investment, and what defines it?
- How many SKUs fall into the low-margin, low-volume cluster?
- Are there products with high market share but declining margins that need repricing?
GrabGo Insights , a consumer research consultancy, surveyed 500 young adults (ages 18–34) who purchase grab-and-go snackbars from convenience stores at least occasionally. Respondents rated eight statements about their general attitudes and preferences in the snackbar category — not about any specific brand or product. Each item uses a 0–100 scale (0 = not at all important, 100 = extremely important). The survey was designed to encourage tradeoffs: respondents were told they couldn't rate everything highly and should spread their scores to reflect true priorities. A lower score on a dimension doesn't mean the respondent doesn't care about it at all — it means other dimensions mattered more to them.
📋 Dataset Variables: health_importance: Importance of clean, natural ingredients taste_indulgence: Priority placed on rich taste and flavor satisfaction price_sensitivity: Importance of value-for-money and low price convenience: Importance of easy, on-the-go packaging and portability brand_loyalty: Tendency to repurchase the same snackbar brand protein_functional: Importance of high protein or functional nutrition benefits
🤔 Classroom / Managerial Analysis Questions:- Which segment is the largest? What brand positioning would best appeal to them?
- Is there a segment that represents an under-served niche — high on dimensions that current market leaders ignore?
- If you were launching a new snackbar, which two segments would you target first, and how would the product differ for each?
The Marlowe Riptide are heading into their third season in the fast-growing Major League Women's Lacrosse (MLWL) . The front office has spent two years acquiring fans through every channel imaginable — youth clinics, Instagram, group/school nights, corporate partners, and walk-up sales — but markets to all of them with the same email blasts and the same offers. You're the new fan-insights analyst, handed an engagement export of 900 ticketed fans.
📋 Dataset Variables: games_attended: Home games attended out of a 12-game slate merch_spend: Total team-store spending ($) avg_price_paid: Average ticket price paid ($) — discount depth shows up here distance_miles: Distance from Marlowe Arena (miles) email_engagement: Email engagement score (0–100) youth_events: Youth clinics / camps attended by the household
🤔 Classroom / Managerial Analysis Questions:- Which segment combines high attendance with high youth-event activity — the one the club should anchor a youth-membership strategy around?
- Which segment pays the lowest average price and engages the least? Is it worth a retention spend, or should the club accept the churn?
- For each segment, recommend one specific marketing action (offer, channel, or product) that fits its profile.
Astera Workflow Cloud , a mid-market SaaS company serving operations teams, has 500 active accounts spread across four regions and three subscription tiers. Growth has slowed because lifecycle campaigns are still broadcast to the full base. The retention director asked you to build mixed-type clusters that combine product behavior and account profile attributes so marketing can run segment-specific nurture and expansion plays.
📋 Dataset Variables: annual_spend: Annual revenue in thousands ($K) - continuous login_frequency: Logins per month - continuous tenure_months: Customer lifetime in months - continuous region: Geographic region (West, East, South, Central) - categorical subscription_tier: Plan level (Basic, Pro, Enterprise) - categorical acquisition_channel: How customer signed up (Organic, Paid, Referral, Partner) - categorical
🤔 Classroom / Managerial Analysis Questions:- Which cluster should receive immediate white-glove retention support from CSMs?
- Where is the clearest data-backed upsell path from Pro to Enterprise?
- How should acquisition-channel mix influence lifecycle campaign prioritization?
HarborLane Commerce , a digital-first retail brand with a 300-SKU assortment, is preparing next season's buy plan. Finance flagged working-capital pressure from slow movers, while merchandising wants to preserve strategic breadth. You were asked to cluster products using both numeric performance metrics and categorical attributes to separate investment priorities from exit candidates.
📋 Dataset Variables: price: Retail price in dollars - continuous monthly_sales: Average monthly unit sales - continuous avg_rating: Customer rating (1-5 scale) - continuous category: Product category (Electronics, Apparel, Home, Sports) - categorical brand_tier: Brand positioning (Premium, Mid, Budget) - categorical stock_status: Inventory availability (In-Stock, Limited, Backorder) - categorical
🤔 Classroom / Managerial Analysis Questions:- Which cluster should receive incremental inventory and merchandising budget immediately?
- Where do ratings and sales diverge in ways that imply merchandising, not product-quality, issues?
- What share of the portfolio appears to be rationalization or clearance candidates?
VantageSignal CRM collects inbound demand from webinars, content syndication, demo requests, and outbound prospecting. Sales leadership is missing SLA targets because reps triage leads manually. You need to build mixed-type lead clusters that combine engagement intensity and firmographic fit, so SDR routing can prioritize high-probability pipeline.
📋 Dataset Variables: engagement_score: Lead engagement score (0-100) - continuous email_opens: Total email opens in last 30 days - continuous estimated_deal_size: Predicted deal value in thousands ($K) - continuous industry: Industry vertical (Tech, Finance, Healthcare, Retail) - categorical company_size: Employee count tier (SMB, Mid-Market, Enterprise) - categorical lead_source: Acquisition source (Webinar, Content, Demo Request, Cold Outreach) - categorical
🤔 Classroom / Managerial Analysis Questions:- Which segment should be routed to SDRs for immediate follow-up vs. automated nurture?
- Where do engagement and firmographic fit disagree, and what routing policy should resolve that conflict?
- How should lead-source mix influence weekly pipeline forecasting and SLA planning?
You're a market research analyst at GreenLeaf Home & Garden , a regional chain of 14 home improvement stores across the Midwest. The VP of Marketing, Diana Rodriguez, commissioned a survey of loyalty program members to understand who your customers really are beyond simple demographics.
📋 Dataset Variables: age: Respondent age in years (range: 22-78) income_k: Annual household income in thousands (self-reported brackets converted to midpoints) brand_loyal: "I usually buy the same brands I've always bought" (Yes/No) price_sensitive: "Price is the most important factor in my purchase decisions" (Yes/No) online_buyer: "I prefer to research and buy products online" (Yes/No) eco_conscious: "I'm willing to pay more for environmentally friendly products" (Yes/No)
🤔 Classroom / Managerial Analysis Questions:- How many distinct customer personas emerge from the data? Use BIC to justify your choice.
- Create a 1-sentence "persona description" for each segment that Diana could use in presentations.
- Which segment represents the best target for GreenLeaf's new "EcoHome" product line (premium, sustainable products)?
- If you had to pick ONE segment to prioritize for the upcoming digital ad campaign, which would it be and why?
You're a data analyst at StreamVision Media , a mid-sized streaming service preparing to launch in three new markets. The Chief Content Officer, Marcus Chen, needs to understand how potential subscribers currently consume media so the team can position StreamVision against Netflix, Hulu, and traditional TV.
📋 Dataset Variables: tv_hours: Weekly hours watching traditional/live TV (mean: 12.4, range: 0-45) streaming_hours: Weekly hours on streaming services like Netflix, Hulu, Disney+ (mean: 10.2, range: 0-35) social_hours: Daily hours on social media platforms (mean: 1.8, range: 0-6) news_source: Primary source for news: TV, Online, Print, or Social cord_cutter: Has dropped cable/satellite subscription in past 3 years (Yes/No)
🤔 Classroom / Managerial Analysis Questions:- How many viewer segments exist? Does a 2-class or 3-class solution fit better (compare BIC)?
- Which segment should StreamVision target for launch? Consider both size and receptivity to streaming.
- What advertising channels would you recommend for reaching each segment based on their media habits?
- Marcus is debating whether to position StreamVision as a "Netflix alternative" or a "cable replacement." Based on the segments, which positioning makes more sense?
You're a CRM analyst at UrbanThread , a direct-to-consumer fashion brand that sells exclusively online. The company has grown rapidly over the past two years (from $8M to $22M in annual revenue), but the CMO, Priya Sharma, is concerned that the one-size-fits-all loyalty program isn't working.
📋 Dataset Variables: monthly_spend: Average monthly spending in $ (mean: $496, range: $61-$2,327) purchase_freq: Average purchases per month (mean: 5.5, range: 0.5-12.6) avg_order_value: Average order value in $ (mean: $116, range: $15-$285) returns_rate: Proportion of items returned (0-1 scale; mean: 0.12)
🤔 Classroom / Managerial Analysis Questions:- How many behavioral segments emerge? Compare 2-class, 3-class, and 4-class solutions using BIC.
- Which segment has the highest true profitability after accounting for return costs?
- Design a differentiated retention strategy for each segment (e.g., loyalty perks, free shipping thresholds, personalized recommendations).
- If UrbanThread can only invest in ONE segment improvement initiative this quarter, which should it be? Consider both revenue potential and feasibility.
BeautyForge , a DTC beauty brand managing 400+ creator partnerships across Instagram and TikTok, wanted to identify which post-level factors predict tracked purchases. "Micro or mega? US or APAC? We're spending $200K a quarter on creators without knowing which levers actually move the needle." — Performance Marketing Analyst
📋 Dataset Variables: Converted: Binary: post generated ≥1 sale (1) or no tracked sale (0) Story_Views: Total unique viewers who saw the post Swipe_Ups: Link taps from post to product page Influencer_Tier: Mega / Mid / Micro Audience_Region: US, EU, LATAM, APAC
🤔 Classroom / Managerial Analysis Questions:- Holding story views constant, does an additional swipe-up significantly increase the odds of conversion?
- Are Micro-tier posts significantly more likely to convert than Mega-tier posts after controlling for reach and engagement?
- Which audience region shows the strongest conversion advantage, and what campaign strategy follows from that finding?
DemoForce , a B2B SaaS company with a 12-person outbound sales team, tracked 600 leads through a multi-touch email sequence to understand what actually predicts a booked demo. "Our reps are burning cycles on low-score leads while high-intent prospects go cold — the model should tell us who to call first." — VP of Sales
📋 Dataset Variables: Responded: 1 = demo booked, 0 = no demo Emails_Sent: Number of emails delivered Total_Opens: Opens across sequence Lead_Score: Marketing automation score (0–100) Industry: SaaS, Ecommerce, Manufacturing, Agency
🤔 Classroom / Managerial Analysis Questions:- By how much does a 10-point increase in Lead Score multiply the odds of booking a demo, controlling for other variables?
- Which industry has the highest predicted probability of response relative to the baseline industry?
- Should SDRs prioritize high-score SaaS leads over mid-score Ecommerce leads? What does the model suggest?
LuxeBrand , a mid-market apparel retailer, A/B tested a high-incentive promo (25% off + free shipping) against a standard offer to 59 site visitors, aiming to quantify the lift before a full-scale rollout. "If the rich offer converts at 2× the rate, the margin trade-off still pays out — but we need the number." — Director of E-Commerce Growth
📋 Dataset Variables: Converted: 1 = completed promo goal, 0 = no conversion High_Incentive: 1 = 25% off + free shipping, 0 = standard Days_Since_Last_Visit: Days since visitor last visited site
🤔 Classroom / Managerial Analysis Questions:- By how much does the high-incentive offer multiply the odds of conversion compared to the standard offer?
- Is recency (days since last visit) a statistically significant predictor after controlling for incentive level?
- If the high-incentive promo costs $8 more per visitor, does the conversion lift justify the spend? What information would you need to calculate break-even?
CartLoop , a subscription e-commerce brand with 5,000 active accounts, tasked its CRM team with building a churn prediction model to prioritize win-back campaigns before the Q4 renewal window. "We lose 18% of subscribers every quarter — if we can identify who's about to leave 30 days out, we can act before they cancel." — Retention Analytics Lead
📋 Dataset Variables: Churned: Binary: 1 = left, 0 = stayed Recency_Days: Days since last purchase Purchase_Frequency: Total orders in past year Avg_Order_Value: Average spend per order Customer_Segment: Premium, Standard, Basic, Trial
🤔 Classroom / Managerial Analysis Questions:- By how much do the odds of churn increase for each additional day of recency, holding other variables constant?
- Are Trial-segment customers significantly more likely to churn than Premium customers after controlling for RFM metrics?
- Should CartLoop's win-back campaign prioritize by segment tier, recency, or a combination? What rule would you derive from the odds ratios?
Marchbank Industrial Supply , which runs 40+ contractor webinars, branch workshops and a trade conference each year, analyzed 220 email invitees to model which factors drive event registration. "We send thousands of invites but our registration rate is only 23% — if timing and email engagement predict who converts, we can stop blasting and start targeting." — Carla Esposito, Marketing Manager
📋 Dataset Variables: Registered: Binary: 1 = registered, 0 = did not Days_Before_Event: Days between invite and event date Email_Opens: Times the invite email was opened Event_Type: Webinar, Workshop, Conference
🤔 Classroom / Managerial Analysis Questions:- For each additional email open, by how much do the odds of registering increase, controlling for event type and timing?
- Do Workshops convert significantly better than Webinars after controlling for engagement and lead time?
- If Marchbank's current registration rate is 23%, which single intervention — sending earlier, increasing email opens, or shifting to Workshops — has the highest predicted impact on registration?
PeakGear , a mid-market outdoor apparel DTC brand with 12,000 active customers, has been using gut-feel segmentation to target email campaigns. The CRM team wants to move from intuition to a data-driven model using the three classic customer behavior metrics: Recency (how recently they bought), Frequency (how often they buy), and Monetary value (how much they spend). "We know our Gold members buy more, but is that because they're Gold — or because they were already frequent buyers? We need the model to untangle these effects." — CRM Analytics Lead
📋 Dataset Variables: Purchased_Next_Quarter: Binary: 1 = made a purchase in Q2, 0 = did not Recency_Days: Days since last purchase (lower = more recent) Purchase_Frequency: Number of orders in the past 12 months Avg_Order_Value: Average spend per order ($) Loyalty_Tier: Gold / Silver / Bronze (based on lifetime spend rules)
🤔 Classroom / Managerial Analysis Questions:- For each additional day of recency, by how much do the odds of purchasing next quarter change? Express as a percentage change per 10 days.
- After including all three RFM metrics, does Loyalty_Tier remain a statistically significant predictor? What does this tell you about whether tier-based targeting adds value beyond the raw metrics?
- If PeakGear can only send win-back emails to 200 of their 500 customers, how would you use this model's predicted probabilities to rank and select the target list?
SignalPath , a B2B marketing-technology SaaS company ($5M ARR, 15-person sales team), tracks every lead from first touch to close. The VP of Sales wants a data-driven lead score to replace gut-feel prioritization. "Our top 3 reps deliver more than half of revenue — but it's not because they work harder. They just pick better leads. I want every rep to pick like Sarah."
📋 Dataset Variables: Converted: 1 = deal won, 0 = deal lost (stalled deals excluded). About 22% of these leads converted, SignalPath's win rate. Company_Size: Number of employees at the prospect company Pricing_Page_Visited: 1 = visited pricing page, 0 = did not Pages_Viewed: Total pages viewed on SignalPath's website Time_On_Site_Min: Total minutes spent on site before first sales call Industry: SaaS, Technology, Financial, Healthcare, Retail, Manufacturing, Professional Services
🤔 Classroom / Managerial Analysis Questions:- What is the single strongest predictor of deal conversion? How much does it increase the odds?
- After controlling for pricing-page visits and engagement, does inbound vs. outbound lead source significantly predict conversion?
- Build a model with all predictors — which variables are significant at p < 0.05 and which can be dropped?
The Marlowe Riptide , a second-season club in the new Major League Women's Lacrosse (MLWL) , just finished their inaugural membership campaign. Now the renewal window is open. "Retention is everything for a young franchise — re-signing a member is a fraction of the cost of finding a new one. I need to know who's at risk of walking, and what actually drives a fan to re-up, before my reps start dialing." — Director of Ticketing & Membership
📋 Dataset Variables: renewed: Binary: 1 = renewed for Year 2, 0 = lapsed games_attended: Home games attended (out of 12) email_open_rate: Club email open rate (0–100%) distance_miles: Distance from Marlowe Arena (miles) prior_seasons: Seasons as a member before this renewal (1–2) youth_player: Household has a youth lacrosse player (Yes / No)
🤔 Classroom / Managerial Analysis Questions:- Holding distance and channel constant, how much does each additional game attended change the odds of renewal?
- After controlling for engagement, are youth-player households significantly more likely to renew? Quantify the odds ratio.
- Use the model's predicted probabilities as a renewal score. Which 20% of members are most at risk, and what save offer would you design for them?
You're the CRM analyst at StyleBox , a subscription fashion service. Marketing Director Sarah Chen wants to predict which email recipients will convert based on their engagement score. The data shows a clear pattern — high engagement leads to high conversion.
📋 Dataset Variables: engagement_score: Composite score (0-100) based on email opens, clicks, and website visits. converted: Binary: 1 = purchased after email, 0 = did not purchase.
🤔 Classroom / Managerial Analysis Questions:- At what engagement_score does the predicted conversion probability first cross 50%? Would Sarah use this exact threshold as her “outreach trigger,” or adjust it based on campaign cost?
- How low does the log-loss get when you fit the optimal β0 and β1? What does that number tell you about model quality?
- If StyleBox can only afford to contact 30% of subscribers, which engagement_score cutoff would maximize the number of true converters reached?
You're the retention analyst at StreamFlix , a streaming service. VP of Customer Success Marcus Johnson needs to predict which customers will churn based on days since their last login. The pattern is clear but not perfect —some inactive users stay, some active users leave.
📋 Dataset Variables: days_since_login: Number of days since the customer last accessed the platform (0-180). churned: Binary: 1 = cancelled subscription, 0 = still active.
🤔 Classroom / Managerial Analysis Questions:- At what days_since_login does the predicted churn probability exceed 50%? Would Marcus set the win-back trigger at that exact threshold or earlier?
- How does the log-loss compare to a naive model that predicts the base churn rate (70% here) for every customer? What does the improvement tell you?
- Identify two customers in the “uncertain zone” (predicted probability 40–60%). What would StreamFlix’s intervention cost-benefit calculation suggest for this group?
You're the programmatic analyst at TechDeals , an electronics retailer. Media buyer Alex Rivera wants to predict click probability based on ad position score. The signal is weak and noisy —position helps, but many other factors influence clicks.
📋 Dataset Variables: position_score: Ad placement quality score (1-10, higher = better position). clicked: Binary: 1 = user clicked ad, 0 = no click.
🤔 Classroom / Managerial Analysis Questions:- What is the minimum achievable log-loss you can reach by adjusting β0 and β1? How does it compare to a null model predicting the base CTR for every impression?
- Is the best-fit sigmoid slope (β1) statistically meaningful or near-flat? What does that tell Alex about position_score as a standalone predictor?
- Given the weak signal, what additional features would you recommend TechDeals collect to build a more accurate click-probability model?
You're the growth analyst at CloudTools , a B2B SaaS platform. Head of Product Dana Wright wants to predict which users will upgrade to Premium based on account age AND their current plan tier. The data shows different baseline upgrade rates for Free, Basic, and Pro users.
📋 Dataset Variables: account_age_months: Months since account creation (1-36 months). current_plan: Plan tier: Free (reference), Basic, or Pro. upgraded: Binary: 1 = upgraded to Premium, 0 = did not upgrade.
🤔 Classroom / Managerial Analysis Questions:- Controlling for account age, which plan tier (Free, Basic, Pro) shows the highest predicted upgrade probability? By how much does B_cat shift the S-curve?
- Compare the log-loss of the model with the categorical predictor vs. without it. How large is the improvement, and does it justify the added complexity?
- At what account age does a Basic-tier user reach a 50% predicted upgrade probability? How does Dana’s team use this to time upgrade prompts?
You're the digital analytics lead at PeakAuto , a luxury EV dealership. Sales Director Jordan Lee wants to predict which website visitors will book a test drive for an $85,000 vehicle based on a composite digital intent score (browsing depth, video views, configurator use). Only ~10% of visitors ever book —most are browsers, not buyers.
📋 Dataset Variables: intent_score: Composite digital engagement score (1–20): browsing depth, video completion, configurator interactions. booked_test_drive: Binary: 1 = booked test drive within 7 days, 0 = did not book.
🤔 Classroom / Managerial Analysis Questions:- Set β₁ = 0 (no slope) and adjust β₀ until log-loss stops improving. What probability does the model predict for everyone? How does that relate to the 90/10 class split?
- Now add a positive β₁. At what intent_score does the predicted probability first reach 20%? Does it ever reach 50%? What does that tell you about who PeakAuto should actually target?
- Why is the optimal β₀ so negative? How does the intercept encode the rarity of the positive class—and what would happen to predictions if you artificially set β₀ = 0?
You're the digital marketing analyst at TechGear Direct , an online electronics retailer. Marketing Director Rachel Torres wants to understand the relationship between Google Ads spending and weekly revenue. Early analysis suggests the relationship is fairly consistent —each additional dollar appears to generate similar returns.
📋 Dataset Variables: ad_spending: Weekly Google Ads spend ($178-$921). TechGear's typical operating range. revenue: Weekly revenue attributed to search ads. The business outcome to predict.
🤔 Classroom / Managerial Analysis Questions:- Does adding the quadratic term meaningfully reduce MAE compared to the linear model? Compare the two Optimal MAEs: does the improvement clear 5%, and if it only just does, which model would you present to Rachel, and why?
- Using the linear model’s B₁, what is TechGear’s estimated revenue per dollar of ad spend? Is this consistent with a 3:1 ROAS target?
- At what weekly spend level do the linear and quadratic predictions diverge most? What does that suggest about the model’s reliability at the edges of TechGear’s budget range?
You're the CRM analyst at StyleBox , a subscription fashion service. Email Marketing Manager David Kim ran a controlled experiment varying email frequency. The results show clear diminishing returns —early emails boost revenue significantly, but additional emails yield progressively smaller (and eventually negative) returns.
📋 Dataset Variables: emails_per_month: Number of marketing emails sent (2-20 per month). Test cells received fixed frequencies throughout the experiment. monthly_spend: Average monthly spend per subscriber in that test cell. Higher = better.
🤔 Classroom / Managerial Analysis Questions:- Using the formula x* = −B₁/(2×B₂), calculate the optimal email frequency from your fitted quadratic coefficients. How does this compare to David’s observed “sweet spot” of 10–12 emails?
- How much does predicted monthly_spend drop if the team sends 5 emails beyond the optimal frequency? Is the penalty large enough to justify a strict cap?
- Compare the MAE of the best-fit linear model vs. the quadratic. How large is the improvement? Does this magnitude justify the added complexity?
You're a marketing analyst at GlowUp Cosmetics , a DTC beauty brand. CMO Priya Sharma has collected data from 52 influencer campaigns. The data is much noisier than other channels—influencer performance is unpredictable. However, there's evidence of an optimal spending level beyond which returns actually decline (mega-influencer fatigue).
📋 Dataset Variables: influencer_spend: Total payment for the campaign ($12K-$86K). Includes content creation, usage rights, and platform fees. campaign_revenue: Revenue attributed to the campaign via tracking ($K). Measured within 14 days of content posting.
🤔 Classroom / Managerial Analysis Questions:- What spending range (approximate) produces the highest predicted campaign_revenue according to the best-fit quadratic? Would you advise Priya to cap campaigns below that level?
- How does the MAE of the best model for influencer marketing compare to what you’d expect from a clean dataset? What does the high MAE floor tell you about the reliability of influencer ROI forecasts?
- A $45K campaign returned $121K while an $81K campaign returned only $57K. Does the model help explain this, or does it expose the limits of a single-predictor regression for influencer selection?
You're a sales operations analyst at PrintForge Solutions , a B2B distributor of industrial 3D printers for manufacturing applications. VP of Sales Marcus Chen wants to understand how sales consultation time affects deal size—and whether this relationship differs across customer segments. PrintForge serves three distinct markets: small enterprises (machine shops, prototyping studios), medium enterprises (regional manufacturers), and large enterprises (Fortune 1000 manufacturers with multiple facilities).
📋 Dataset Variables: consultation_hours: Total pre-sale consultation time (8-50 hours). Includes discovery calls, technical demos, facility visits, and proposal reviews. contract_value: Signed contract value in $K. Includes hardware, installation, training, and first-year maintenance. segment: Customer segment: Small Enterprise (1-100 employees), Medium Enterprise (101-1,000), or Large Enterprise (1,000+).
🤔 Classroom / Managerial Analysis Questions:- What is the baseline expected contract value for a Small Enterprise deal with minimal consultation time? How much higher is it for Large Enterprise?
- If a sales rep has 10 hours of additional capacity, and one prospect in each segment, where should they invest that time to maximize total expected contract value?
- Marcus is considering a policy where Large Enterprise deals get a dedicated "solutions architect" (adding ~15 hours of consultation). Based on your model, what's the expected ROI if each solutions architect hour costs PrintForge $150?
- Compare the categorical model (3 intercepts) to a simple linear model ignoring segment. How much predictive accuracy (MAE improvement) do we gain by accounting for segment?
Brightwell Pet Food manufactures premium freeze-dried raw dog kibble distributed nationwide through pet specialty retailers and DTC e-commerce. Over the past two fiscal years, Brightwell spent $3.6M across TV, Paid Search, Paid Social, and Programmatic Display. At the Q4 budget review, CFO Marcus Vance made an aggressive proposal: "Search generated $3.80 in gross sales for every dollar spent last year, while TV only averaged $2.45. Finance wants to slash TV in half and redirect $30,000 a week straight into Google Search." VP of Brand Marketing Maya Lin strongly objected: "TV campaigns build cumulative brand memory that ripples forward across months. Paid Search is merely vacuuming up demand that our TV flights created. If you kill TV and pump that money into Search, Search will hit a wall and our total sales will drop."
📋 Dataset Variables: week: Time Index sales: Outcome ($) tv: Media Spend ($) search: Media Spend ($) social: Media Spend ($) display: Media Spend ($)
🤔 Classroom / Managerial Analysis Questions:- Why does the CFO's proposal to shift $30K from TV to Search actually decrease total annual company revenue?
- If Brightwell completely paused all paid marketing for a month, what weekly revenue would you expect the brand to sustain, and why?
- Explain the difference between Average ROI and Marginal ROI to a non-technical board member in two sentences.
Tandem Bikes sells commuter and gravel bicycles directly to consumers. Every spring, sales explode as temperatures rise and cyclists take to the roads. CMO Chloe Bennett presented glowing performance reports to the executive committee: "Our paid social campaigns drive the entire spring surge! Every March we ramp up Instagram ads, and sales surge by 80%. Social is our primary growth engine." Head of Analytics David Ortiz pushed back with a cautionary note: "People buy bikes when the snow melts and the weather gets warm, whether they saw an Instagram ad or not. Because marketing ramps up spend right when natural demand peaks, naive models confuse spring weather with ad effectiveness. We are over-crediting Social for the sun coming out."
📋 Dataset Variables: week: Time Index sales: Outcome ($) social: Media Spend ($) search: Media Spend ($) email: Media Spend ($)
🤔 Classroom / Managerial Analysis Questions:- Why does naive econometric modeling (omitting seasonality) over-credit Social advertising for spring sales surges?
- If Tandem Bikes halved its Social ad spend next spring while leaving bikes in stock, what does the Fourier-controlled model predict will happen to sales?
- How could the marketing team design a regional holdout experiment to definitively prove Social's true causal incrementality?
Lumen Beauty is a fast-growing clean skincare brand specializing in peptide serums and barrier repair creams. To drive viral momentum, marketing coordinated paid TikTok/Instagram social ads to run in lockstep with sponsored beauty influencer product reviews. VP of Growth Rachel Klein argued for an immediate strategy pivot: "We spend $35,000 per campaign on influencer sponsorships, but our Facebook and Instagram ads drive the direct Shopify conversions. I want to cancel influencer contracts and double paid social ads." Creative Director Sophie Moreau vehemently disagreed: "Our paid social ads only convert because customers saw beauty creators explaining the routine three days earlier! Influencers create the initial awareness; paid social merely closes the sale. If you defund influencers, paid social efficiency will collapse."
📋 Dataset Variables: week: Time Index sales: Outcome ($) social: Media Spend ($) influencer: Media Spend ($) search: Media Spend ($)
🤔 Classroom / Managerial Analysis Questions:- Why is it mathematically impossible for standard OLS regression to cleanly separate Social and Influencer lift when their spend correlation is r = 0.92?
- Why is accepting an automated optimizer's recommendation to defund Influencer and double Social a high-risk gamble for Lumen Beauty?
- How could Rachel and Sophie run a staggered geo-holdout test across cities to establish the true incremental lift of Influencer campaigns?
Hearthstone Coffee produces premium fair-trade whole-bean and ground packaged coffee distributed through 12,000 grocery supermarket stores nationwide. Marketing relies on pulsed national television flights, always-on grocery search delivery ads, and drive-time commuter radio spots. Brand Director Liam Vance completed a 104-week national econometric mix model, but the executive committee was skeptical of regression results. Liam explained his triangulation strategy: "We don't rely solely on observational regression. Last fall, we partnered with Nielsen and our analytics team to run a 12-week matched-market Difference-in-Differences (DiD) experiment across 16 midwestern metropolitan regions. In 8 test cities we pulsed heavy TV flights, while in 8 matched control markets we turned TV off completely. Now we can compare our national model's TV lift directly against our regional ground-truth experiment."
📋 Dataset Variables: week: Time Index sales: Outcome ($) tv: Media Spend ($) search: Media Spend ($) radio: Media Spend ($)
🤔 Classroom / Managerial Analysis Questions:- Why is pulsed advertising statistically easier for an econometric model to identify than continuous, flat spending?
- How does Hearthstone's national observational model align with the findings of its regional matched-market Difference-in-Differences experiment?
- If TV ads have a carryover half-life of 1.0 week (λ = 0.50), how should Liam space out TV flights to maintain continuous retail shelf velocity?
GlowRoute is a direct-to-consumer skincare brand in Los Angeles: serums, moisturizers and sunscreen bundles sold through its own site, about $120,000 a month in paid media. Its 30-day Summer Glow launch added $61,500 across five channels, and the launch dashboard credits every order to the last channel the shopper touched. On that rule Delphine Arceneaux , the CMO, read Email as the launch's best channel and the Instagram ads as money lost, and she is about to set next month's budget on it. She has asked for an audit first: the same 33,000 shopper journeys with every touch kept, so the dashboard's number can be reproduced and then tested.
📋 Dataset Variables: Email (three launch sends): $2,400 Google Ads (brand and non-brand search): $21,000 Retargeting Display: $7,600 Instagram Ads: $18,500 Creators (two paid partners): $12,000
🤔 Classroom / Managerial Analysis Questions:- Reconcile. Does the Last-Touch column reproduce the dashboard's reading, Email 35.3% and Instagram Ads 14.0% at $0.94? If not, what is different?
- Mechanism. Which channels sit at the start of the converting journeys and which at the end? What does that do to a last-click count?
- Corrected figure. Under Shapley and under Markov, what share does each channel earn, and which channels now clear $1.82?
- Price the gap, then write. If Delphine had cut the channel the dashboard called a loss, what would the launch have given up under each rule? One paragraph to her: the corrected figure next to the reported one, the mechanism in a sentence, and what changes next month.
CivicPulse builds permitting and records software for mid-sized city governments. They are 220 people in Minneapolis, and they have lost four senior engineers to better-funded competitors in the last eight months. The board has approved $1.1M in additional annual benefits spend — enough to meaningfully improve about three of the nine things on the table, not all nine.
📋 Dataset Variables: respondent_id id: One employee or candidate. 180 of them, anonymised at collection. task_id set: Which of the nine screens this row belongs to. item item: The benefit shown, e.g. "Employer-paid health premiums". best 0/1: 1 if this was picked as most important on that screen. worst 0/1: 1 if this was picked as least important on that screen.
🤔 Classroom / Managerial Analysis Questions:- Which three benefits should get the $1.1M? Quote each one's score and say how much more it is worth than the item ranked last.
- Look at your ranks 4 through 7. How many of those steps survive the p vs above test at 5%? Write the one sentence you would put under the chart so nobody over-reads the ordering.
- Run Step 5 with 3 segments. Do the segments broadly agree, and what would have to be true for a single ranked list to be the wrong deliverable here?
- Lena asks: "Does this prove our benefits package is competitive?" Explain why MaxDiff cannot answer that, and name one measure that could.
Harborline is a branchless digital bank with about 340,000 accounts, most opened since 2021. In six weeks they launch their first national acquisition campaign — a $4.2M buy across streaming, podcast and paid social. The creative brief has room for one headline claim and two supporting claims . Marketing has ten candidates and no agreement.
📋 Dataset Variables: respondent_id id: One prospect from a national panel, screened as a likely switcher. task_id set: Which of the ten screens this row belongs to. item claim: The advertising claim, e.g. "Get paid up to two days early". best 0/1: 1 if this claim was the most compelling on that screen. worst 0/1: 1 if this claim was the least compelling on that screen.
🤔 Classroom / Managerial Analysis Questions:- Which claim leads, and by how much over the equal-importance line at 10?
- Marisol needs two supporting claims. List every adjacent pair the data cannot separate at 5%, then recommend the two — defending the choice on something other than the ranking, since the ranking does not separate them.
- Ted asks whether the winning claim is "twice as good" as the one in fifth. Answer using the ratio property, and state one thing that ratio does not mean.
- Run 3 segments. If the segments want different headlines, what does that imply for a single national campaign versus a split buy?
SummitGear makes technical packs out of Boulder, Colorado — 38 employees, roughly $24M in revenue, sold through specialty retail and their own site. Next season's flagship pack goes into tooling in eleven weeks , and the team can build three of twelve proposed features into it. Everything else waits a year.
📋 Dataset Variables: respondent_id id: One customer who bought a pack in the last 24 months. task_id set: Which of the ten screens this row belongs to. item feature: The proposed feature, e.g. "Lighter frame (under 2.5 lb)". best 0/1: 1 if this was the feature they most wanted on that screen. worst 0/1: 1 if this was the feature they would drop first.
🤔 Classroom / Managerial Analysis Questions:- What are the top three features overall, and how many adjacent pairs in the full ranking fail the p vs above test? What does that say about handing engineering a strict 1-to-12 order?
- Run 3 segments. Name each one from its own top-scoring features, and estimate its share of the sample.
- Find a feature that is top-three for one segment and bottom-half for another. What does its aggregate score represent, and which customer does that number describe?
- Anneke asks for a recommendation in one slide. Do you give Tomas a single ranked list, or something else? Defend it with the segment table.
Nightjar , a challenger streaming service preparing a major documentary series launch, ran a paired creative test across 1,800 viewers to determine which trailer concept drives stronger intent-to-watch. "When you put both trailers in front of the same viewer, which one actually flips them from maybe to yes?" — Creative Strategy Lead
📋 Dataset Variables: intent_action: Intent-to-watch after Action trailer (Yes = 1 / No = 0) — Condition A intent_mystery: Intent-to-watch after Mystery trailer (Yes = 1 / No = 0) — Condition B viewer_id: Individual viewer who watched both trailers — 1,800 matched pairs total
🤔 Classroom / Managerial Analysis Questions:- At α = 0.025, do viewers show a significant directional preference for one trailer over the other?
- Which trailer generates more “no → yes” flips among the discordant pairs?
- If you were Nightjar's creative director, which trailer would you greenlight as the hero launch asset based on this data?
CrestLine Apparel , a national chain with 739 flagship stores, alternated two in-store pricing sign formats over an 8-week period to determine which format more reliably drives stores to hit their weekly margin targets. "We've been using the same percent-off signage for a decade — this test will tell us whether the switch is worth the operational cost." — VP of Merchandising
📋 Dataset Variables: legacy_margin: Hit weekly margin target with Legacy "percent-off" signage (Yes/No) — Condition A adaptive_margin: Hit weekly margin target with Adaptive "new vs. old price" signage (Yes/No) — Condition B store_id: Individual flagship store — 739 total matched-pair observations
🤔 Classroom / Managerial Analysis Questions:- Is the difference between the 92 and 58 discordant stores statistically significant at α = 0.05?
- What proportion of all stores showed a change in outcome (hit margin under one format but not the other)?
- Would you recommend CrestLine roll out the adaptive signage to all 739 stores? What additional factors beyond this test would inform that decision?
PipeStack , a B2B SaaS company, tested two sequential email nurture tracks on 515 in-market enterprise leads who had already requested content to determine which content style (product tutorials vs. executive ROI stories) better moves them to a booked demo. "We have 515 hand-raisers sitting in limbo — if ROI stories move them faster than tutorials, we're redesigning the entire nurture sequence next quarter." — Head of Demand Generation
📋 Dataset Variables: tutorial_demo: Booked demo after Tutorial Track (Yes/No) — Condition A executive_demo: Booked demo after Executive ROI Track (Yes/No) — Condition B lead_id: Individual in-market enterprise lead — 515 total matched-pair observations
🤔 Classroom / Managerial Analysis Questions:- At α = 0.01, does one nurture track significantly outperform the other in driving demo bookings?
- What proportion of the 515 leads are in the discordant pairs that drive this test?
- Should PipeStack abandon the tutorial track? Or are there lead segments where tutorials might still outperform ROI stories?
PulseTrack , a subscription business, ran a 90-customer lifecycle experiment to determine whether a journey-personalized push notification converts more high-value subscribers than a standard control push, using matched pairs to control for individual purchasing tendencies. "If the personalized push shifts even 7 more customers out of 90 into a converted state, we're rebuilding the journey trigger system next sprint." — VP of Lifecycle Marketing
📋 Dataset Variables: Control Push: Purchase made after Control Push (Converted / Not converted) — Condition A Journey-Personalized Push: Purchase made after Journey-Personalized Push (Converted / Not converted) — Condition B
🤔 Classroom / Managerial Analysis Questions:- At α = 0.05, does McNemar's test confirm a significant directional advantage for the journey-personalized push over the control push?
- What are the discordant pair counts, and which direction (Control → Personalized or Personalized → Control) dominates?
- Given this 90-customer pilot result, what sample size would PulseTrack need to verify the same effect at a conservative α = 0.01?
AtlasAdvisory , a brand strategy consultancy, has been retained by Summit Ridge Brewing — a craft startup with 8 SKUs distributed across Colorado, Utah, Montana, and Wyoming — to map the competitive landscape before a $2M rebranding investment. "We know we're somewhere between craft and mass-market, but I can't tell the board exactly which brands we're actually competing with — or where the white space is." — Director of Brand Strategy
📋 Dataset Variables: Perceptual Data: 10 brands × 12 attribute ratings (1–7 scale) aggregated from 250 beer drinkers Preference Data: 400 consumers each rating all 10 brands on preference (1–9 scale) Segments: Premium Seekers (40%) · Value Seekers (35%) · Lifestyle Seekers (25%) Usage Weights: Individual weekly consumption rate — Value Seekers drink ~2× more than Premium Seekers
🤔 Classroom / Managerial Analysis Questions:- What do Dimensions 1 and 2 represent? Examine which brands cluster at each extreme and match them to the attributes that load most heavily on each axis.
- Based on the perceptual map, which brands does Summit Ridge most directly compete with — and is that the competitive set the client expected?
- Which customer segment is largest, and does their ideal-point vector point toward an occupied or unoccupied region? What positioning would you recommend to Summit Ridge?
Prism Mobile Partners , a nationwide wireless carrier with 820 retail stores, is rationalizing its device portfolio heading into Q3. VP of Category Management Derek Walsh framed the problem bluntly: "Apple's margin is terrible and Samsung's inventory is complex to manage. I need to know which brands consumers genuinely perceive as different — and which ones are just occupying the same position at different price points."
📋 Dataset Variables: Perceptual Data: 6 brands × 10 attribute ratings (1–7 scale) Preference Data: 300 consumers each rating all 6 brands on preference (1–9 scale) Segments: Premium Buyers (40%) · Budget Conscious (35%) · Tech Enthusiasts (25%) Spend Weights: Annual device + accessory spend — Premium: $800–$1,500 · Budget: $200–$500
🤔 Classroom / Managerial Analysis Questions:- What do the two perceptual dimensions represent? Which brand occupies the most isolated position on the map — and what does that mean for its competitive vulnerability?
- Xiaomi and Motorola compete in the same value-price tier. Does the perceptual map suggest they are viewed as interchangeable, or does one hold a meaningfully different position?
- Using spend-weighted market share simulation, which brand wins among Premium Buyers? How should that inform Prism's shelf-space allocation for high-margin flagship devices?
Lighthouse Entertainment Group , an independent studio with a $120M original content budget and a 340-title library, is deciding whether to launch its own streaming platform or license content to an existing service. CSO Maya Patel put the question plainly: "If HBO Max and Apple TV+ already own the 'prestige adult content' space, we're not dislodging them. But if that cluster looks crowded on a map while the family or binge segments have unmet demand, that changes our bet entirely."
📋 Dataset Variables: Perceptual Data: 6 platforms × 10 attribute ratings (1–7 scale) Preference Data: 350 consumers each rating all 6 platforms on preference (1–9 scale) Segments: Family Households (35%) · Binge Watchers (40%) · Quality Seekers (25%)
🤔 Classroom / Managerial Analysis Questions:- What do the two map dimensions represent? Which platform is most isolated on the map, and what would it mean for Lighthouse to enter adjacent to it?
- Is the "prestige/adult content" quadrant genuinely crowded, or does the map suggest enough separation between HBO Max and Apple TV+ that a new entrant could occupy a distinct position?
- Based on the segment ideal-point vectors, which viewer segment shows the largest unmet demand — and what platform attributes would Lighthouse need to prioritize to serve it?
Salt & Juniper , the fast-casual bowls-and-wraps chain with 86 locations in four states, is shaping a second concept ahead of a planned 40-unit expansion. CMO June Ellison was direct in the strategy kick-off: "Every fast-casual brand claims they own 'quality and freshness.' I want to see the actual perceptual map — because either there's a white space we can fill, or we're about to spend $60M building something that looks exactly like Chick-fil-A to consumers."
🤔 Classroom / Managerial Analysis Questions:- What competitive cluster (if any) would Salt & Juniper's new concept enter if it positioned as "fast + fresh" — high speed and high ingredient quality simultaneously? Is that space currently occupied?
- The Experience Seekers segment has the highest willingness to pay and the lowest price sensitivity. Does their ideal-point vector point toward the premium fast-casual brands — or into unoccupied map territory?
- Based on the perceptual map, what single attribute trade-off defines the biggest gap in this market? What should Salt & Juniper lead with in the new brand identity to claim that territory?
Meridian Athletics , a challenger footwear brand with $80M annual revenue and placement in 3,200 specialty and sporting-goods retailers, is finalizing a $4M brand campaign. VP of Brand Strategy Jonah Osei challenged the agency directly: "Nike and Adidas own fashion and performance. Every research deck we get tells us to 'own comfort and durability' — but I want the perceptual map to actually prove there's uncrowded space there before we commit $4M to that story."
📋 Dataset Variables: Perceptual Data: 8 brands × 10 attribute ratings (1–7 scale) Preference Data: 280 consumers each rating all 8 brands on preference (1–9 scale) Segments: Fashion Forward (35%) · Serious Runners (35%) · Comfort Seekers (30%) Spend Weights: Annual athletic footwear spend by segment (Serious Runners spend 2× more)
🤔 Classroom / Managerial Analysis Questions:- What two dimensions best describe how consumers differentiate these 8 brands? Label each axis using the attributes that load most heavily on it.
- Is the "comfort + durability" positioning Jonah's agency recommends genuinely unoccupied on the map — or do ASICS and Brooks already own that perceptual space, making it a crowded entry rather than a white space?
- The Serious Runners segment is the most brand-loyal and least price-sensitive buyer of performance footwear. Where does their ideal-point vector point — and what does that suggest for Meridian's sponsorship and retail partnership targeting?
You're a marketing analyst at Elevate Fitness , a mid-size gym chain with 24 locations across the Midwest. The CMO, Jennifer Walsh, ran a 6-week digital advertising campaign on Instagram and YouTube promoting their new "Flex Pass" membership tier. She wants to understand whether the ads worked by directly persuading consumers, or by first building brand awareness.
📋 Dataset Variables: ad_impressions: Standardized count of ad exposures per consumer (logged, z-scored) brand_awareness: Composite score: aided recall + brand recognition (5-point scale average) purchase_intent: Likelihood to visit gym in next 30 days (1-7 scale)
🤔 Classroom / Managerial Analysis Questions:- If Jennifer wants to increase purchase intent by 0.5 scale points, how many additional standardized ad impressions would be needed, given the indirect-only pathway?
- Why might the direct effect be essentially zero? What does this tell us about the nature of these ads?
- A colleague suggests cutting the campaign. Using the effect sizes, build a counter-argument showing ROI through the awareness pathway.
A recent study published in JCP examined how price levels influence willingness-to-pay (WTP) through perceived quality. You're replicating their findings using your own data from Artisan Bean Coffee , a premium coffee brand testing new price points.
📋 Dataset Variables: price_tier: Experimental condition: Standard ($12), Premium ($18), Luxury ($26) — coded as standardized deviation from mean quality_perception: 7-item scale measuring perceived bean quality, roast skill, and brand expertise (α = .89) wtp: Maximum price participant would pay for 12oz bag (log-transformed, $ per oz)
🤔 Classroom / Managerial Analysis Questions:- What proportion of the total effect runs through the quality perception pathway? Calculate the "proportion mediated."
- If Artisan Bean raised prices from $12 to $18 (1 SD increase), what's the predicted WTP increase in $/oz? Break it into direct vs. indirect components.
- A brand manager says "consumers don't care about quality, they just anchor on price." How would you use these results to refute or support this claim?
The team's funnel slide says email builds awareness , awareness feeds consideration , and consideration drives purchase . A parallel model cannot express that: it would let both mediators sit side by side and never ask whether the first feeds the second. A serial model adds exactly one path, d₂₁ , and with it a third route that runs through both mediators in order.
You're on the analytics team at Luxe Cosmetics , analyzing their recent influencer campaign on TikTok and Instagram. The VP of Digital Marketing, Marcus Kim, noticed that engagement rates vary dramatically by customer age segment—younger audiences seem far more responsive. He wants to know: "Does the mechanism differ by age, or just the magnitude?"
📋 Dataset Variables: influencer_exposure: Standardized index of content views, likes, and saves across campaign (z-scored) brand_trust: 4-item trust scale: reliability, honesty, competence, care (1-7, α = .91) purchase_likely: Purchase intent + actual 30-day purchase (combined standardized index) age_z: Age in standardized units (mean = 34, SD = 11). Negative = younger, Positive = older
🤔 Classroom / Managerial Analysis Questions:- At what age does the indirect effect become non-significant (95% CI includes zero)? Use the conditional IE plot to estimate.
- Marcus is debating whether to shift influencer budget toward 35+ audiences. Given these results, what would you recommend?
- Calculate the "lift" in indirect effect for a 23-year-old vs. 45-year-old customer. What's the practical difference in predicted purchase likelihood?
You're conducting research for Heritage Watches , a Swiss watchmaker considering a celebrity endorsement deal with a famous actor. Initial pilot data shows something puzzling: customers exposed to the celebrity have higher purchase intent overall, but lower perceived authenticity of the brand.
📋 Dataset Variables: celebrity_present: Exposure condition: 1 = saw celebrity endorsement, 0 = brand-only ad (centered) authenticity: Perceived brand authenticity: "genuine," "true to heritage," "not commercial" (1-7) purchase_intent: Likelihood to consider brand for next watch purchase (1-7)
🤔 Classroom / Managerial Analysis Questions:- If Heritage could maintain authenticity at baseline during the celebrity campaign (a = 0), what would the total effect be?
- A creative team proposes "behind the scenes" content showing the celebrity's genuine passion for watchmaking to repair authenticity perceptions. Model what happens if a drops from -0.35 to -0.15.
- Should Heritage proceed with the endorsement? Build a recommendation considering both the suppression pattern and the $2.4M cost.
You're a consultant advising RegionBank , a regional bank with 85 branches. The Chief Customer Officer, Diana Torres, wants to understand how service quality drives customer loyalty. Her hypothesis: service quality could work through two distinct psychological pathways— satisfaction (emotional response) and trust (cognitive assessment).
📋 Dataset Variables: service_quality: SERVQUAL composite: responsiveness, assurance, empathy, tangibles (standardized) satisfaction: Overall satisfaction with banking relationship (1-10 scale) trust: Trust in bank to act in customer's interest, handle finances securely (1-10) loyalty: Behavioral loyalty: tenure × product depth × referral likelihood (composite index)
🤔 Classroom / Managerial Analysis Questions:- Calculate the proportion of total indirect effect carried by each mediator. What percentage goes through satisfaction vs. trust?
- Diana has $500K to allocate between "delight programs" (satisfaction) and "transparency initiatives" (trust). Based on the effect sizes, how would you split the budget?
- A board member asks: "Why bother with two mediators? Can't we just measure satisfaction alone?" How would you respond using these results?
You're leading conversion rate optimization at Tanager Cycles , the e-bike maker whose online orders are fulfilled by its 1,100 dealers ($95M annual revenue). The Director of Brand and Digital, Keiko Matsuda, wants to understand why customer reviews boost conversions—and more importantly, whether she should invest in review solicitation programs or just prominently display the reviews she already has.
📋 Dataset Variables: review_prominence: Experimental manipulation: low visibility (below fold), medium (sidebar), high (hero banner) — standardized product_trust: Post-exposure survey: "I trust this product will perform as described" (1-7 scale, measured via exit popup for non-buyers) purchase_completed: Binary conversion (0/1) converted to standardized probability via logistic transform
🤔 Classroom / Managerial Analysis Questions:- Keiko is debating between two UX changes: (A) adding a "2,547 customers bought this" badge (direct social proof) or (B) expanding detailed review text (trust pathway). Based on the equal effect sizes, what would you recommend?
- If review prominence increases by 1 SD, what's the expected percentage point increase in conversion rate? (Hint: the outcome is standardized probability)
- A team member argues the direct effect is just "herd behavior" and not authentic. How would you defend the business value of both pathways?
You're the People Analytics lead at Precision Medical Solutions , a B2B medical device company with 312 field sales representatives across 8 US regions. The Chief Revenue Officer, David Park, invested $1.8M last year in a new "Challenger Sales" training program. Now he's facing a board question: "Where's the ROI?"
📋 Dataset Variables: training_hours: Annual training hours completed per rep (logged, standardized). Range: 8-120 hours. sales_confidence: Self-efficacy scale: "I can handle complex deals," "I can overcome objections," etc. (5-item, α = .88) quota_attainment: Revenue / quota ratio, standardized within territory type (mean = 100%, SD = 22%) tenure_z: Years at company, standardized (mean = 4.2, SD = 3.1). Negative = newer, Positive = veteran
🤔 Classroom / Managerial Analysis Questions:- David wants to shift 40% of training budget from veterans to new hires. Calculate the expected change in total indirect effect if average tenure of trainees drops from 4.2 to 2.5 years.
- At what tenure level does the training → confidence effect become non-significant? Use the conditional effects to estimate.
- The VP of Sales argues: "Veterans don't need confidence training—they need new product knowledge." How would you design a study to test whether the mechanism differs by content type?
You're a Product Analyst at Harborline Bank , a branchless digital bank with about 340,000 checking accounts. After a major app redesign (codenamed "Project Clarity"), the CMO, Marisol Vega, wants rigorous evidence that the UX improvements are driving retention. But she has a deeper question: "How is good UX working—is it about making the app easier to use, or making it more enjoyable?"
📋 Dataset Variables: ux_quality_index: Composite of task completion rate, error frequency, and navigation efficiency—standardized (higher = better UX) perceived_ease: Survey: "The app is easy to use," "I can find what I need quickly," "No frustration" (3-item, α = .91) enjoyment: Survey: "Using the app is enjoyable," "I look forward to using it," "It's pleasant" (3-item, α = .87) retention_90d: Still active at 90 days post-redesign (binary, converted to standardized index)
🤔 Classroom / Managerial Analysis Questions:- Calculate the proportion of total indirect effect carried by each pathway. What percentage is Ease vs. Enjoyment?
- Marisol can get engineering capacity for either: (A) a "smart navigation" feature reducing friction, or (B) animated confetti celebrations for milestones. Based on these coefficients, which should she prioritize and why?
- A designer argues: "For Gen Z users, enjoyment matters more than ease—they're used to apps." How would you test this hypothesis with a moderated parallel mediation model?
Copperpaw Pet Supply , an online pet retailer running year-round paid digital campaigns, noticed that the same ad budget produced wildly different revenue in Q4 vs Q1. The team asked: is ad spend ROI genuinely seasonal, or just noise? "If Q4 spend literally generates 3× the revenue per dollar, we should be shifting budget in October, not waiting for year-end reviews." — Quinn Harlow, Director of Customer Marketing
📋 Dataset Variables: revenue: Weekly revenue in USD — outcome variable ad_spend: Weekly advertising budget ($) — continuous focal predictor season: Quarter/season (Q1_Winter / Q2_Spring / Q3_Summer / Q4_Holiday) — moderator ad_spend_x_season: Interaction term: ad_spend × season dummy — tests if the revenue/spend slope varies by quarter
🤔 Classroom / Managerial Analysis Questions:- Is the interaction between ad spend and season statistically significant? What does this mean for how we interpret the main effect of ad spend?
- Which season shows the steepest slope (highest revenue per dollar of ad spend)?
- If Copperpaw has a fixed annual ad budget of $500K, how would you redistribute it across quarters based on the interaction results?
InboxPath , a CRM platform serving 200 subscription customers, ran an analysis of email send frequency vs. engagement to find the optimal weekly cadence before fatigue sets in. "Our highest-frequency customers have the worst engagement scores — we need to know the peak before we kill the relationship entirely." — Director of CRM Strategy
📋 Dataset Variables: engagement_score: Customer engagement score (0–100 scale: opens, clicks, purchases combined) email_frequency: Emails sent per month — continuous predictor (linear component) email_freq_squared: Quadratic term (email_frequency²) — captures diminishing returns / inverted-U shape segment: Customer tier (Budget / Standard / Premium) — control variable
🤔 Classroom / Managerial Analysis Questions:- Is the quadratic term for email frequency statistically significant? What does a negative coefficient tell us about the shape of the relationship?
- At what email frequency does engagement peak (the turning point)? How does this compare to InboxPath's current send cadence?
- Should InboxPath set a universal frequency cap or differentiate by segment tier? What does the segment control variable suggest?
ConsumerLens , a consumer products research consultancy, ran a 180-respondent study to test whether the relationship between price and purchase intent depends on perceived product quality — the prestige pricing hypothesis. "The prestige pricing effect only holds if quality perceptions are already high — at low quality, a higher price just kills intent. We need the interaction term to prove this isn't just theory." — Chief Research Officer
📋 Dataset Variables: purchase_intent: Purchase intent on a 1–10 scale price: Product retail price ($20–$200) — continuous focal predictor quality_rating: Perceived quality from user reviews (1–10 scale) — continuous moderator price_x_quality: Interaction term (price × quality_rating, mean-centered) — tests prestige pricing effect
🤔 Classroom / Managerial Analysis Questions:- Is the Price × Quality interaction term statistically significant? What does its sign tell us about the direction of the prestige pricing effect?
- At low quality (−1 SD), does higher price increase or decrease purchase intent? At high quality (+1 SD)?
- If ConsumerLens is advising a mid-tier brand on pricing strategy, at what quality rating level does the "price as a quality signal" effect start to become a net positive for purchase intent?
Greystone Tavern Analytics , a fictional D&D campaign hub collecting 120 encounter records, became the dataset for a classic debate: do Fighters and Wizards scale differently with character level? "The fighter says damage grows linearly with level; the wizard says spell slots create exponential scaling. We're settling this with regression, not dice rolls." — DM Aldric
📋 Dataset Variables: avg_damage: Average damage per round (numeric) — outcome variable character_level: Character level (1–20) — continuous focal predictor class_type: Character class (Fighter / Wizard / Rogue) — categorical moderator level_x_class: Interaction term (character_level × class_type dummy) — tests whether damage scales differently by class
🤔 Classroom / Managerial Analysis Questions:- Is the Level × Class interaction statistically significant? What does this mean for evaluating class-specific damage growth?
- Which class shows the steepest damage growth slope with character level?
- At what level does the highest-scaling class first exceed the Fighter's expected damage per round? Does this match common D&D community wisdom?
You're the analytics lead at BrightCart , an online marketplace with 1.2M active customers. Six months ago, your team deployed a churn prediction model that flags customers likely to stop purchasing within the next 30 days.
🤔 Classroom / Managerial Analysis Questions:- At what month does the model cross from "healthy" to "warning"? Could you have predicted this from the PSI alone?
- Which feature drifts first? Does it match what you'd expect from an influencer-driven customer influx?
- After retraining, how many months of stability do you get before drift resumes?
AdVantage Media runs programmatic ad campaigns for 50+ brand clients. Your CTR prediction model decides which ad creative to serve to which user segment, optimising for click probability.
🤔 Classroom / Managerial Analysis Questions:- Which feature shows the most dramatic PSI change? Is it the same feature that contributes most to performance loss?
- After retraining at month 10, does accuracy return to its launch level from Step 1? In a real deployment, why might it not?
- How would you redesign the model to be more robust to future privacy changes?
FreshBrew is a premium coffee subscription ($38/month) with 45,000 active subscribers. Your retention model predicts which subscribers will cancel within the next billing cycle, triggering personalised "stay" offers.
🤔 Classroom / Managerial Analysis Questions:- If you were only allowed to monitor ONE feature for drift, which would you choose and why?
- At what point does retraining become insufficient — when should you rebuild the model with new features?
- How could FreshBrew's marketing team use the drift signal to inform competitive strategy, not just model maintenance?
MediaMax sells enterprise media planning software ($50K–$200K ACV). Your lead scoring model, trained on 2018–2019 data, ranks inbound leads by conversion probability. Sales reps prioritise the top-scored leads for outreach.
🤔 Classroom / Managerial Analysis Questions:- How quickly does PSI cross the "red" threshold? Compare this to the gradual BrightCart scenario — what does the speed difference tell you about drift types?
- After retraining at month 8, does the model recover to its launch accuracy from Step 1? If a real lead-scoring model did not, what would that tell you about the post-shift data?
- If you could add ONE new feature to the retraining data, what would capture the new buying behaviour best?
FlowBridge , a mid-size consumer brand, surveyed 600 respondents to understand how marketing channel and contact frequency predict which funnel stage a customer occupies. "Which channels actually move people from Awareness into Purchase? We've been spending equally across all three — it's time to find out which lever actually works." — VP of Growth Marketing
📋 Dataset Variables: stage: Marketing funnel stage: Awareness, Consideration, or Purchase (reference = Awareness) channel: Primary marketing channel: Email, Social, or Search frequency_segment: Contact frequency tier: Low, Medium, or High age_band: Respondent age group: 18-24, 25-34, 35-44, 45-54, or 55+
🤔 Classroom / Managerial Analysis Questions:- Controlling for age band, which channel has the strongest association with being in the Purchase stage vs. Awareness?
- What is the effect of High vs. Low contact frequency on the log-odds of being in the Consideration stage?
- If FlowBridge could only keep one marketing lever (channel or frequency) to move customers through the funnel, which does the model suggest is more impactful?
ChannelCore , a category management consultancy analyzing preference for four competing brands (A, B, C, D) in a mature market, surveyed 800 respondents on brand choice and price sensitivity to identify where each brand holds a competitive advantage. "We can see overall share by segment, but the model will tell us why — which price sensitivity and demographic combination makes Brand A vs. Brand D the preferred choice." — Category Strategy Director
📋 Dataset Variables: brand_choice: Preferred brand: A, B, C, or D (reference = Brand A) price_sensitivity: Self-reported price sensitivity: Low, Medium, or High age_band: Respondent age group: 18-24, 25-34, 35-44, 45-54, or 55+ channel: Preferred marketing channel: Email, Search, or Social
🤔 Classroom / Managerial Analysis Questions:- Controlling for channel preference, are High-sensitivity consumers significantly more likely to choose Brand D (budget) than Brand A (new)?
- Which age band shows the strongest predicted probability of choosing Brand A, and what does that imply for ChannelCore's targeting recommendation?
- If you were advising Brand A on its defensive strategy against Brand D, which segment (price sensitivity tier and channel) would you prioritize for retention?
GameForge Studios , a competitive multiplayer games publisher with 700 active esports players across three platforms, surveyed its player base to identify what behavioral and membership signals predict high engagement and monetization tier membership. "We know our Competitive players spend 10× more than Casual — but what behavioral signals get someone there? We need to build that model." — Head of Player Monetization
📋 Dataset Variables: engagement_segment: Player tier: Casual, Core, or Competitive (reference = Casual) region: Player region: NA, EU, or APAC platform: Gaming platform: PC, Console, or Mobile membership_tier: Subscription level: Free, Standard, or Premium hours_per_week: Average weekly hours played (continuous) matches_per_week: Matches played per week (continuous)
🤔 Classroom / Managerial Analysis Questions:- Controlling for hours_per_week, which platform (PC, Console, Mobile) is most strongly associated with being in the Competitive segment vs. Casual? What does this tell GameForge about its platform investment priorities?
- How much does Premium membership increase the log-odds of being Competitive vs. Casual, controlling for playtime? Is the effect large enough to suggest a targeted subscription upgrade campaign for Core players?
- Using the model, describe the predictor profile of a player most likely to convert from Casual to Core with a well-timed subscription offer — what combination of region, platform, playtime, and current tier defines this target?
ScoreMetrics , a customer analytics firm, uses a proprietary engagement score to segment accounts into Low, Medium, and High tiers for targeted outreach. ScoreMetrics built this version of the scoring model using clean synthetic data to demonstrate textbook multinomial behavior. "We want students to see what 'good convergence' looks like before they tackle messy real-world data." — Curriculum Designer
📋 Dataset Variables: segment: Customer tier: Low / Medium / High engagement score: Continuous engagement score (normally distributed)
🤔 Classroom / Managerial Analysis Questions:- At what score value does the predicted probability of "High" first exceed "Low" as the most likely segment?
- How do the log-odds coefficients for Medium vs. Low compare to High vs. Low?
- What would change if you used "High" as the reference category instead of "Low"?
Corvane Center for the Performing Arts , whose late-night electronic series now runs 40+ EDM shows a year in its 300-seat studio, tasked its marketing analyst with modeling what drives total patron spend to optimize ticket pricing and revenue forecasting. "If VIP ticket holders consistently outspend General Admission by 3×, we should be growing that tier aggressively — but only if the data confirms it." — Mei-Lin Tsao, Director of Marketing and Membership
📋 Dataset Variables: event_spend: Total patron spend at the event in USD (tickets, drinks, merch) days_preordered: Days in advance the ticket was purchased (purchase lead time) type: Ticket type: VIP, Early_Bird, or General_Admission (use General_Admission as the reference; the tool defaults to VIP, the first level in the file)
🤔 Classroom / Managerial Analysis Questions:- Controlling for lead time, how much more do VIP ticket holders spend on average compared to General Admission patrons?
- Is purchase lead time (days preordered) a statistically significant predictor of total event spend?
- If Corvane increases its VIP allocation from 10% to 20% of tickets, what is the projected increase in revenue per 1,000 patrons based on the model?
RetainLoop , a subscription ecommerce brand with 10,000+ active accounts, asked its CRM team to build a model explaining customer value to prioritize retention campaigns and upsell targeting. "If RFM alone explains 70%+ of variance in value, we can simplify our scoring and stop guessing which customers to call." — Head of Customer Analytics
📋 Dataset Variables: CustomerValue: Customer lifetime value score in USD Recency: Days since the customer’s last order (lower = more recent) Frequency: Number of orders placed in the last 12 months MonetaryValue: Average basket size in dollars Segment: CRM tier: High Value, Medium, Low, At Risk (categorical dummy)
🤔 Classroom / Managerial Analysis Questions:- Which RFM predictor has the strongest association with customer value after controlling for the others?
- After controlling for raw RFM metrics, do segment labels still significantly predict customer value?
- How would RetainLoop use this model to rank its top 500 customers for a VIP retention campaign? What coefficients would drive the ranking?
Ridgeline Roasters ships specialty coffee by subscription. So far its "save offer", a free bag worth about $14, has been shown at random to some subscribers. A vendor says their AI predicts who will cancel better than the analyst's regression. "I have to decide whether the free bag goes to everyone or only to new subscribers, and I need to know which list to trust. Show me on customers the model never saw." Dana Okafor, Retention Lead
📋 Dataset Variables: churned: 1 if the subscriber cancelled within 90 days, 0 if not tenure_months: Months subscribed so far (1 to 60) lifecycle_stage: New (first 6 months) or Established plan: Single Bag, Double Bag or Office Pack avg_bag_rating: Average star rating the subscriber gave their bags (1 to 5) emails_per_month: Marketing emails sent to them per month (0 to 12)
Copperline Outfitters sells outdoor gear by catalog and online. This spring it will mail 40,000 of its 120,000 customers, at about $1.10 a catalog, so the list decides where $44,000 goes. The new VP has asked for the list to be picked "with AI". "I'm happy to switch if it picks better buyers. I'm not happy to switch because it sounds better." Joanna Pike, Direct Mail Manager
📋 Dataset Variables: responded: 1 if the customer ordered from last spring's catalog, 0 if not months_since_last_order: Months since their previous order (1 to 36) orders_last_2yrs: Orders in the two years before the mailing avg_order_value: Average order value, in dollars loyalty_member: 1 for loyalty-club members, 0 otherwise catalog_version: Product grid or Lifestyle cover (assigned at random)
Tallyhop sells scheduling software to small clinics. Its two-person growth team has 400 finished free trials and wants to know which trials to call before day 14. One of them read that bigger networks are smarter and set up a wide one. "It gets nearly every training trial right. Why does sales say its calls aren't landing?" Lena Ruiz, Co-founder
📋 Dataset Variables: converted: 1 if the clinic became a paying customer, 0 if not staff_invited: Staff accounts the clinic invited during the trial calendars_connected: Calendars synced (0 to 3) onboarding_call: 1 if the clinic took the onboarding call days_active: Days of the 14 the clinic logged in clinic_type: Dental, Physio, Chiropractic or Veterinary
Kinoscope is a streaming service with three plans: Basic with Ads, Standard and Premium 4K. The sign-up page can lead with any one of them. "Leading with Premium for the wrong people costs us sign-ups. I want to know who picks it, and whether a network tells me anything a multinomial model doesn't." Ravi Mehta, Pricing Manager
📋 Dataset Variables: plan_chosen: Basic with Ads, Standard or Premium 4K weekly_hours: Hours watched in the first week household_size: People in the household (1 to 6) owns_4k_tv: 1 if the household has a 4K TV age: Account holder's age signup_device: TV app, Phone or Web
Wrenfield Mattress sells through local stores in 2,500 markets. Paid social is capped at $25k per market per quarter. "Finance wants to lift the cap to $60k in the big markets. Before I sign, tell me what the extra $35k actually buys, and how sure we can be." Claire Dubois, CMO
📋 Dataset Variables: revenue_k: Quarterly revenue in the market, $ thousands social_spend_k: Paid social spend, $ thousands (capped at 25) search_spend_k: Paid search spend, $ thousands promo_week: 1 if the market ran a promotional week market_size: Small, Mid or Large local_unemployment: Local unemployment rate, %
Priya Raman, head of e-commerce , is about to switch off Checkout Flow B. The weekly report has Flow A averaging more per order, and the t-test her analyst ran came back with nothing to report. Flow B costs more to maintain. The decision looks obvious.
Daniel Okafor runs store operations for a regional home-goods chain. Six stores have been trialling a new floor layout for a quarter. Exit surveys asked one question: "How easy was it to find what you needed?", answered from 1 to 5.
Mei Lin, product lead , shipped a redesign of the onboarding flow to 64 beta users. Each of them answered the same question before the change and again four weeks after: "How likely are you to recommend this app?", on a 0 to 10 scale.
Tomás Herrera runs acquisition at a subscription box company. Next quarter's budget splits across four channels, and he wants to know where the fastest-converting sign-ups come from. For every sign-up he has the number of days until their first order.
A major airline wants to understand whether its highest-tier loyalty members behave differently from mid-tier members when it comes to "borrowing" miles against future travel. The Miles Loan program lets members advance 50,000+ miles before earning them, at a fee.
An e-commerce brand A/B-tested two versions of its weekly promotional email. Group A received a personalized subject line featuring their first name and a product category they recently browsed. Group B received a generic subject line with the same offer but no personalization.
A mid-size apparel retailer wants to quantify the behavioral gap between customers enrolled in its loyalty program vs. those who shop without membership. The metric: whether a customer makes a repeat purchase within 30 days of their first transaction.
A B2B SaaS company offers a 14-day free trial. Some trial users schedule a live product demo with a sales rep during the trial; others explore on their own. The company tracks whether each trialer converts to a paid plan within 30 days of trial start.
A DTC beauty brand ran a split test on Instagram, showing either a 15-second video ad or a static carousel image to randomly selected users in the same target audience. Both ads promoted the same product and offer.
Aura Athletics , a direct-to-consumer athletic wear brand, has a historical baseline conversion rate of 2.40% (p₀ = 0.024) on its paid social video ads. The growth team commissioned a high-production influencer video creative and ran an initial paid test of 1,850 clicks. The Growth Marketing Director wants to know: "Did the influencer creative beat our 2.40% baseline, or is the 3.78% sample result just chance noise?"
📋 Dataset Variables: Benchmark (p₀): 2.40% (Aura's historical paid social video baseline) Converted: Binary: purchased on landing session (1) or bounced (0) Sample Result: 70 conversions out of 1,850 clicks on the influencer video (p̂ = 3.78%)
The CFO of a B2B subscription software firm mandated that average customer acquisition cost must not exceed $45.00 (μ₀ = 45.0) for the company to maintain unit economic profitability. Over the past 60 weekly acquisition cohorts, the marketing team achieved a sample average CAC of $42.15 (s = $7.80) .
📋 Dataset Variables: Mandated Ceiling (μ₀): $45.00 per acquired customer Cohort CAC: Continuous: campaign spend divided by new customer count ($) Sample Metrics: N = 60, x̄ = $42.15, s = $7.80
An omnichannel retailer advertises guaranteed same-day delivery with a service-level agreement of under 24 hours (μ₀ = 24.0) . Logistics audits 50 randomly sampled metropolitan orders and measures transit time from checkout to doorstep.
Credit card payment networks place online merchants on an elevated monitoring surcharge if their monthly dispute/chargeback rate exceeds 1.00% (p₀ = 0.010) . A sample of 3,000 recent transactions yielded 38 disputes (1.27%).
StreamNova , a global streaming service expanding into connected TV, tested three hero creative concepts across 428 geo test cells to determine which format drove the highest incremental revenue per thousand household exposures at launch. "We've spent three times as much on the hero film as the other two combined — I need to know if it's actually moving the needle before we lock that into next year's upfront." — VP of Brand Marketing
📋 Dataset Variables: rev_per_1k_hh: Incremental revenue per 1,000 household exposures ($), normalized for reach and spend — so $48.50 here is about 4.9 cents per household creative: Creative concept assigned to cell (Emotion-led Hero Film / Promotion-Heavy Carousel / Influencer Cameo Reel) geo_cell: ZIP-cluster geo test cell — 428 total observations across 3 creative groups
🤔 Classroom / Managerial Analysis Questions:- Does the one-way ANOVA indicate a statistically significant difference in mean revenue per thousand households across the three creative concepts at α = 0.05?
- Based on the Tukey HSD pairwise comparisons, does the Hero Film outperform the Influencer Cameo Reel and the Promotion Carousel at the same significance level?
- Given the cost differential, what effect size (Cohen's f or eta-squared) would justify continuing to produce the expensive hero film over cheaper alternatives?
StyleBridge , a national apparel retailer, piloted four distinct promotional pricing strategies across 333 stores ahead of back-to-school season to determine which approach maximizes weekly gross-margin dollars without training customers to wait for discounts. "Flash sales feel like they're training our customers to wait for deals instead of paying full price — but I don't want to kill a tactic that might be driving incremental volume in certain regions." — SVP of Retail Merchandising
📋 Dataset Variables: gross_margin_k: Weekly gross-margin dollars per store, in thousands ($K) — so 32.4 means $32,400 that week pricing_strategy: Promotional pricing approach assigned to each store cluster (4 levels) store_id: Individual store — 333 total observations across 4 strategy groups
🤔 Classroom / Managerial Analysis Questions:- At α = 0.10, does pricing strategy significantly affect mean weekly gross-margin dollars per store across the four groups?
- Which specific pair of strategies shows the largest margin gap, and is that gap statistically significant under post-hoc comparison?
- Should StyleBridge eliminate Flash Sale Bursts from the promotions calendar? What additional store-level data (region, store tier, SKU mix) would strengthen that recommendation?
PipeStack , a B2B SaaS company, tested two sequential email nurture tracks on 134 dormant enterprise leads to identify which content style drives more qualified pipeline per lead at a conservative α = 0.01 threshold. "We're not switching our entire nurture workflow on a 2-star hunch — we need statistical confidence before overhauling what our team has built over three years." — Head of Demand Generation
📋 Dataset Variables: pipeline_per_lead: Qualified pipeline dollars created per contacted lead ($), aggregated across verticals nurture_track: Email nurture track assigned to lead (Tutorial Deep Dive Series / Executive ROI Story Track) lead_id: Individual dormant enterprise lead — 134 total (64 Tutorial, 70 Executive ROI)
🤔 Classroom / Managerial Analysis Questions:- Is the $35.00 mean pipeline difference between tracks statistically significant at α = 0.01?
- What is the 99% confidence interval for the difference in mean pipeline per lead?
- Should PipeStack switch all dormant leads to the Executive ROI track? What segment-level factors might complicate a one-size-fits-all recommendation?
Halbrook Public Radio , through its sustaining-member programme, ran a controlled lifecycle messaging experiment on 100 high-value post-renewal members to identify which of four message variants maximizes incremental revenue per customer. "Before we recode the push notification system to favor VIP drops, I want to see the ANOVA — if the p-value doesn't clear 0.05, we're not touching the workflow." — Dmitri Sokolov, Membership Director
📋 Dataset Variables: Revenue Per Customer: Incremental revenue per customer ($) captured after the push + email sequence Message Variant: Post-renewal message type (Always-on Thank-You / Tiered Loyalty Story / Benefit Carousel / VIP Drop Invite)
🤔 Classroom / Managerial Analysis Questions:- Does the one-way ANOVA on the raw data confirm a statistically significant difference in mean incremental revenue across the four message variants at α = 0.05?
- In the Tukey HSD pairwise comparisons, does VIP Drop Invite significantly outperform Benefit Carousel, and does Tiered Loyalty Story significantly outperform Benefit Carousel?
- Should Halbrook Public Radio recode its push notification logic to prioritize VIP Drop Invite for all high-value post-renewal customers, or does the data suggest a segmented approach based on customer tier or tenure?
You're the digital marketing analyst at FreshFit Apparel , a D2C activewear brand. The CMO wants to understand the relationship between daily Instagram/TikTok ad spend and conversions (purchases). Historical data shows a generally positive but slightly curved relationship.
📋 Dataset Variables: ad_spend: Daily social media ad spend (about $200–$5,000 range) conversions: Daily purchase conversions attributed to social ads
🤔 Classroom / Managerial Analysis Questions:- At what polynomial degree does holdout RMSE start increasing? What does that turning point tell FreshFit’s CMO about the optimal model complexity for forecasting conversions?
- Compare training RMSE vs holdout RMSE at degree 4. How large is the gap? Is this evidence of overfitting, or just noise?
- If FreshFit needs to explain the chosen model to non-technical marketing stakeholders, which degree polynomial would you recommend and why?
You're analyzing pricing data for CraftBrew Coffee , a specialty coffee subscription. A/B tests across different price points reveal a clearly curved relationship —demand falls steeply as price rises from the low end, then levels off at higher prices.
📋 Dataset Variables: price: Monthly subscription price (about $12–$44 range tested) demand: Weekly new subscriptions during test period
🤔 Classroom / Managerial Analysis Questions:- How much does holdout RMSE fall from degree 1 to degree 2, and how much more from degree 2 to degree 5? Where does an extra term stop being worth it for forecasting CraftBrew's demand?
- Compare the gap between training and holdout RMSE at degree 1 and at degree 3+. Does the gap grow with complexity? Is this evidence of overfitting, or just noise?
- If CraftBrew needs to explain the chosen model to non-technical board members, which degree polynomial would you recommend and why?
You're the CRM manager at GreenLeaf Organics , an organic grocery delivery service. You've experimented with different email frequencies (emails per week) and tracked engagement scores. The data shows a noisy inverted-U pattern — engagement rises with moderate frequency but drops off at high frequency (email fatigue), though high noise makes the peak hard to see.
📋 Dataset Variables: emails_week: Average marketing emails sent per week (about 0.5–12 range) engagement: Composite engagement score (clicks + opens + conversions)
🤔 Classroom / Managerial Analysis Questions:- From degree 2 to degree 5, how much does training RMSE fall, and what happens to holdout RMSE? What is the best degree by holdout performance, and is its lead big enough to matter?
- Does degree 2 improve on degree 1 for holdout RMSE? That would confirm the inverted-U pattern is real, not just noise.
- For GreenLeaf's actual email frequency recommendation, which model and which frequency would you advise — and how does the high noise affect confidence in that recommendation?
The brand studio is preparing a pitch deck for a multi-continent streaming bundle launch and needs to show whether brand recall rose in the markets where its hero creative ran.
A nationwide retailer alternated its loyalty pricing banners between a shouty "Compare & Save" frame and a friendlier "Bundle Rewards" promise. The merchandising team wants to know whether the softer framing genuinely nudges shoppers toward bigger baskets.
A conversion marketing squad wants to know whether its new insight-led nurture series outperforms the long-standing product-spec drip. Instead of splitting different accounts into different tracks, they run a crossover test on the same set of in-market accounts, with a washout period in between.
Thistledown Market sells its own cold brew, and the loyalty-card data shows that a shopper who bought it last week is far more likely to buy it this week. "That's habit. Get people to buy one bottle and the habit does the rest. I want to mail 20,000 'first bottle free' coupons. At about $3.50 each, that's $70,000, and I need to show what it brings back." Dana Whitlock, Brand Manager
📋 Dataset Variables: customer_id: The loyalty-card number (TM0001 to TM0500) week: Week 1 to 53 bought: 1 if the shopper bought the cold brew that week, 0 if not coupon: 1 if the shopper had a cold brew coupon that week (mailed to about one shopper in five each week)
🤔 Classroom / Managerial Analysis Questions:- How much does last week's purchase add to this week's chance under each of the three models?
- Why does the model with each shopper's first week and coupon average move so little?
- Would you mail the 20,000 coupons? What would you spend the $70,000 on instead?
Crave , a food-delivery app, is the same company as in the CLV tool, and this is the same file. Customers drift away and come back. "Our win-back programme loads $5 of credit into a lapsed customer's wallet. It only pays if one order turns into a habit again. Is that what happens, or are our regulars just regulars?" Marcus Oyelaran, Growth Lead
📋 Dataset Variables: customer_id: Customer 1 to 300 period: Month 1 to 24 is_active: 1 if the customer ordered that month promo_credits: Dollars of promotional credit loaded that month ($0 to $10) free_delivery_offers: Free-delivery vouchers sent that month segment: Casual Craver, Weeknight Regular or Power User
🤔 Classroom / Managerial Analysis Questions:- Is Crave's repeat ordering habit or loyalty? What number tells you?
- What does a win-back order bring back over the following year?
- How does this answer change what the growth lead should do with the credits?
Fernhill Pet Co. sends a "VIP" coupon each week, picked by the loyalty team's own rules. "Customers with a VIP coupon are about three times as likely to buy that week. I want the budget doubled next year." Priya Castellanos, Loyalty Programme Manager
📋 Dataset Variables: customer_id: Customer FP0001 to FP0500 week: Week 1 to 53 purchased: 1 if the customer bought that week vip_coupon: 1 if the customer got the VIP coupon that week
🤔 Classroom / Managerial Analysis Questions:- By how much does the treat-everyone-alike model overstate the coupon, and why?
- What would you tell the programme manager about doubling the budget?
- How would you design next year's coupon so its effect could be measured cleanly?
Saltmarsh Books , an independent bookshop, sends a weekly newsletter with an offer to its subscribers. "Before I spend on anything clever, I'd like to know whether my readers are all much the same or whether I have a core of regulars." Theo Marchetti, Owner
📋 Dataset Variables: customer_id: Reader SB0001 to SB0400 week: Week 1 to 53 bought: 1 if the reader bought a book that week newsletter_offer: 1 if that week's newsletter carried an offer for the reader
🤔 Classroom / Managerial Analysis Questions:- What does the random-effects model say about how much readers differ?
- Is the small effect of last week's purchase habit or loyalty here?
PulseGrow , a B2B SaaS company, ran an always-on paid social program across 10 metro markets for 40 measured market-weeks. Weekly spend was deliberately varied from $26K to $77K so the team could see the whole response curve rather than one operating point. The VP of Growth, Mia Chen, set the terms: "Finance will fund whatever level I can defend. What I can't defend is 'more spend, more signups' — I need to know what the next dollar buys, not what the average dollar bought."
📋 Dataset Variables: spend_k: Paid social spend for that market-week, in $K (range: $25.9K–$77.2K) signups: Free-trial signups attributed to paid traffic that week (range: 305–489)
🤔 Classroom / Managerial Analysis Questions:- The fitted slope reports one figure for signups per additional $1K. Using the scatterplot, estimate the actual marginal return near $30K/week and near $70K/week. How far apart are they, and what does that gap do to a budget case built on the single slope?
- Mia asks you to project signups at $110K/week, well beyond anything tested. Give her a defensible answer that is not a number read off the fitted line.
- If you re-ran the correlation using only the weeks below $50K, would r rise, fall, or hold? Work out your prediction first, then test it and explain the result in terms of range restriction.
- Spend was set by the media team, not by customers. Name one plausible reason signups and spend could move together here without spend causing signups.
Carrow Valley Growers , the cooperative behind the Carrow Valley produce brand, ran an upper-funnel media push across 34 regional markets in Q3 and tracked both ends of the funnel in each one. The Marketing Director, Simone Nwachukwu, is not persuaded: "I keep being shown a chart where awareness went up. Nobody buys awareness. Show me that the markets where we bought reach are the markets where people are actually willing to consider us."
📋 Dataset Variables: awareness: Unaided brand awareness, % of market panel (range: 34.2%–77.7%) consideration: Would consider on next purchase occasion, % of market panel (range: 19.6%–54.6%)
🤔 Classroom / Managerial Analysis Questions:- Report r and R². In one sentence Simone would accept, say what fraction of the market-to-market difference in consideration is not explained by awareness.
- Four markets fall roughly 9 to 12 points below the trendline. Read their awareness values off the scatterplot. Is their problem reach or message — and how does the position of the point, rather than the value of r, tell you that?
- Simone proposes spending the full $4M buying more reach in those four laggard markets. Argue against it using only what is visible in this scatterplot.
- Suppose you dropped the four as "anomalies" and re-ran the correlation. r would rise. Explain why that would make the analysis worse rather than better.
StayMetrics , a B2B loyalty technology company, benchmarks its 30 largest accounts each quarter. After $2M and two years of customer-experience investment, the CEO, Omar Hasan, wants the payoff stated plainly: "Two years of CX spend was sold to this board on one promise — happier accounts renew. Show me the correlation, and if it's real I'll fund the next phase this week." The correlation you are about to run is statistically significant. That is not the same as it being real.
📋 Dataset Variables: nps: Net Promoter Score from the most recent relationship survey (range: −1 to 79) retention_pct: Seat renewal rate over the past 12 months (range: 73.4%–98.6%)
🤔 Classroom / Managerial Analysis Questions:- Report Pearson's r and its p-value for all 30 accounts. On that evidence alone, what would you have told Omar?
- Now remove the flagship account and re-run. Report the new r and p-value. Which of the two analyses better describes StayMetrics' customer base, and why?
- Spearman's ρ on all 30 accounts lands much closer to the 29-account Pearson than to the 30-account Pearson. Explain what ranking did to the flagship to produce that.
- The flagship is a genuine, correctly measured customer, not a data-entry error. Write the sentence for the board deck that keeps it in the dataset and still tells the truth about the relationship.
FunnelForce , a performance marketing agency, tracked five weekly KPIs for a DTC client across 36 consecutive weeks. The client's VP of Marketing, Priya Nair, wants one thing: "I have five dashboards and no idea which number to manage. Tell me which link in this chain is holding, which one is broken, and which metric I should be reading on a Monday morning."
📋 Dataset Variables: spend_k: Weekly paid media spend in $K (range: $30.2K–$76.1K) awareness: Aided brand awareness, % of weekly panel (range: 28.0%–50.8%) consideration: Consideration rate, % of weekly panel (range: 11.8%–25.8%) conversions: Free-trial signups attributed to the campaign (range: 141–287 per week) cost_per_signup: Blended cost per signup in $ (range: $156–$370) — note this column is derived from the two above it
🤔 Classroom / Managerial Analysis Questions:- Write out the three adjacent-link correlations in funnel order. Which link is broken, and what single intervention would you fund on the strength of that?
- Which metric is the best weekly leading indicator of conversions — and is it the metric Priya is currently being asked to manage?
- Cost per signup correlates positively with spend, yet conversions also rise with spend. Reconcile the two facts, and say what they jointly imply about where the weekly cap should sit.
- Identify which cells in this matrix are partly an accounting identity rather than a behavioural finding, and say how you would present them without misleading the client.
NorthCart , a home goods e-commerce retailer, has 36 months of history. The Director of CRM, Wes Okafor, arrives with the conclusion already drawn: "Send volume and revenue correlate above 0.9 across three full years. The line says every extra million emails is worth about $72K a month. I want the list expansion budget approved on Thursday." Your job is the third column on this file — the one that was not in his chart.
📋 Dataset Variables: email_sends_m: Marketing emails sent that month, in millions (range: 10.8–18.7) revenue_k: Total monthly revenue in $K (range: $679K–$1,338K) seasonal_demand_index: Category demand index published by the trade association, entirely outside NorthCart's control (range: 18.8–99.4; November and December run 83–99, February runs below 22)
🤔 Classroom / Managerial Analysis Questions:- Report all three correlations. State in one sentence why the sends–revenue correlation cannot be read as the return on sending more email.
- The demand index is outside NorthCart's control, yet it correlates strongly with NorthCart's own send volume. What does that tell you about how the send calendar is built, and why does it matter for the causal claim?
- Wes offers a compromise: restrict the analysis to November and December, where revenue is highest. Explain what restricting to the top of the seasonal cycle does to r, and why it would not rescue the causal claim.
- Design the test that would actually answer Wes's question. Be specific about the unit of randomisation, the holdout, and the outcome window.
Halbrook State University Auxiliary Services operates Quad Mart, the busiest campus convenience store serving 24,000 students in Calder Bay. Campus Retail Operations Lead Caleb Thornquist and Institutional Research Analyst Owen Delacroix are resetting the 24-inch wide, 26-inch deep single-door energy drink cooler (5 shelves, 120 linear inches total, restocked Monday and Thursday mornings with a 4-day weekend gap) for the 13-week fall semester. Eighteen SKUs across four brands compete for space, and Voltfang's distributor has offered a $900 slotting check to lock both eye-level shelves (S2 and S3) for Voltfang plus room for a new launch. As Caleb warned: "Voltfang's distributor says their 16 oz cans generate nearly double the sales per linear foot of anything else in our cooler and offered us a $900 slotting check to give them both eye-level shelves (S2 and S3) for the fall semester—plus room for their new Watermelon Freeze launch. Before you cash that check and bury Plainleaf on the bottom shelf, look at how last year's cooler was stocked and when the student workers actually refilled it."
📋 Dataset Variables: units: Weekly units sold per SKU ( week 1–52). Censored whenever shelf capacity ran out during the 4-day Thursday-to-Monday restock gap. facings & zone: Historical horizontal facing count and vertical shelf zone ( eye = 1.20×–1.25× lift on S2/S3, reach = 1.00× on S4, top = 0.85× on S1, bottom = 0.75× on S5). daysInStock: Days in stock out of 7 each week (0–7). Values below 7 flag stockout censoring where shelf capacity capped observed demand. needState: Shopper need state governing substitution and walkouts: sugar , zero , natural (Plainleaf buyers walk out 45% of the time when out of stock, costing $1.50 in lost basket margin), or value . w, h, d & price / cost: Physical can dimensions in inches (governing width fit and 26" front-to-back depth capacity) alongside retail price ($2.29–$3.69) and unit wholesale cost ($1.10–$2.10).
🤔 Classroom / Managerial Analysis Questions:- Auditing Historical Confounding: Why does Voltfang's naive sales-per-linear-foot and pooled space elasticity look so high, and why does Plainleaf Zero Berry ( PL-02 ) only average ~10 units/week in the historical log?
- Evaluating the Slotting Allowance: Compare Voltfang's $900 Full Deal (all of S2 & S3) versus the $450 Counteroffer (S2 only) or an independent plan: how much category margin and walkout basket profit do you lose if S2 and S3 are locked to one brand?
- Designing for the 4-Day Restock Gap: How should you allocate facings across the 5 shelves so high-velocity and high-walkout SKUs survive the 4-day Thursday-to-Monday restock window without starving loyal zero-sugar and organic yerba buyers?
Copperpaw Pet Supply operates its flagship store in Columbus, Ohio alongside a national Autoship program serving 48,000 pet households. Head of Merchandising Desmond Achterberg is resetting Aisle 9, the 6-foot (72 in × 22 in) dry dog food gondola for a 13-week quarter. Shelf S1 is locked to Copperpaw Kitchen Treats, S2 holds 5 lb bags only, S3 and S4 hold up to 15 lb bags, and only the bottom deck (S5) can hold 24 lb and 30 lb heavy bags (fitting just 4 heavy-bag facings, 3 deep), replenished by a single weekly truck every Tuesday (7-day restock gap). As Desmond warned: "Bristle & Bone's rep is offering us a $600 slotting check to keep 2 of our 4 heavy-bag slots on the bottom shelf (S5) for their 24 lb Salmon and Lamb bags plus a 36-inch block at eye level (S3)—and he says we should delist TruePaw Senior 5 lb (TP-03) since it only sells 2 bags a week. Before we sign away half of S5 and cut a veterinary diet SKU, check how often our Tuesday delivery truck actually keeps Hearthline 30 lb in stock and what happens when a TruePaw owner walks out."
📋 Dataset Variables: units: Weekly bags sold per SKU ( week 1–52). Censored whenever shelf capacity ran out prior to the Tuesday weekly delivery truck (7-day restock cycle). weightLb & shelf rules: Bag weight (5 lb, 15 lb, 24 lb, or 30 lb). S2 holds 5 lb only, S3/S4 hold up to 15 lb, and S5 is reserved for 24 lb and 30 lb bags (only 4 heavy facings fit across 72"). facings, zone & daysInStock: Weekly horizontal facings, vertical shelf zone ( top , eye , reach , bottom ), and days in stock out of 7. needState & walkout: Formula tier ( premium , mainstream , vet ). 85% of TruePaw Veterinary buyers and 35%–40% of mainstream/premium buyers walk out when out of stock, costing $16 in lost treats/toys basket margin and causing 30% of walkouts to churn permanently for the rest of the quarter. price & cost: Retail bag price ($21.99–$74.99) and unit wholesale cost ($13.25–$45.00), driving unit gross margin and GMROI.
🤔 Classroom / Managerial Analysis Questions:- Uncensoring the S5 Bottleneck: Why did Hearthline Adult Chicken 30 lb ( HL-02 ) and 15 lb ( HL-01 ) average only ~6.8 and ~5.2 units/week in last year's log, and why is true space elasticity (\(\beta\)) so low in dry dog food?
- Testing the Delist Bait Trap: Using Delist What-If (Tab 2) and ABC / Pareto (Tab 3), why does cutting TruePaw Senior 5 lb ( TP-03 , 2 units/week) destroy quarterly profit while freeing Bristle & Bone Lamb 24 lb ( BB-04 )'s S5 slot for HL-02 pays off?
- Evaluating Bristle & Bone's Slotting Deal: Compare the $600 Full Deal (2 S5 facings + 36" on S3) against the $300 Counteroffer (1 S5 facing + 27" on S3) or an independent plan: how much margin and repeat-customer lifetime value do you lose if HL-02 is starved on S5?
You're the Head of Growth at Bean & Brew , a direct-to-consumer specialty coffee company based in Portland. The company ships curated boxes of single-origin coffee beans monthly to subscribers across the US. The current price of $32/month was set two years ago based on competitor pricing, but CFO Marcus Chen wants a data-driven approach.
📋 Dataset Variables: subscribed: 1 if customer started subscription, 0 if bounced without converting price_shown: Monthly subscription price displayed to visitor ($22–$47 range)
🤔 Classroom / Managerial Analysis Questions:- What price maximizes monthly profit given Bean & Brew's cost structure?
- How does the optimal price compare to the current $32 price? How much profit is being left on the table?
- If COGS increased to $15/box due to supply chain issues, how should pricing change?
- At what conversion rate would a $42 price point generate more profit than the current price?
You're VP of Product at TaskFlow Pro , a project management SaaS platform competing with Asana and Monday.com. The Board is pushing for 40% revenue growth this year, and CEO Lisa Park believes the current $15/seat/month pricing leaves money on the table with enterprise customers.
📋 Dataset Variables: converted: 1 if trial company converted to paid, 0 if churned price_offered: Per-seat monthly price quoted during sales call ($8–$80 depending on segment) segment: Company size: "freelancer" (1–7 employees), "startup" (8–50), "enterprise" (50–500) employees: Number of employees at trial company—larger companies may have different price sensitivity
🤔 Classroom / Managerial Analysis Questions:- What single price maximizes profit across all trials?
- Should TaskFlow implement different pricing for SMB vs Enterprise? What's the profit lift?
- At the optimal price, how many new paying customers does TaskFlow need monthly to hit $500K MRR?
- If a freemium tier is introduced (increasing trials to 4,000/month but lowering conversion), what price point is needed?
You're the Revenue Management Analyst at SkyWest Regional Airlines , responsible for pricing the popular LAX–SFO shuttle route. This 75-minute flight operates 8x daily with 180-seat aircraft. Historically, pricing was based on competitor parity with United and Southwest, but VP Revenue Monica Patel wants to move toward demand-based optimization.
📋 Dataset Variables: booked: 1 if fare class sold out, 0 if seats remained unsold fare_price: Ticket price displayed for this fare bucket ($89–$289)
🤔 Classroom / Managerial Analysis Questions:- What fare price maximizes profit per flight given the 180-seat constraint?
- At what price would the flight consistently sell out? Is that price profit-maximizing?
- If fuel costs increase marginal cost to $60/passenger, how should fares adjust?
- Compare your optimal fare to United's $189 benchmark—how much profit difference per flight?
You're the Pricing Manager at Urban Threads , a mid-market apparel retailer with 45 stores across the Midwest. The merchandising team is planning the end-of-season clearance strategy for the Spring collection. VP of Merchandising Amy Rodriguez wants data to guide markdown depth: "We've always done 40% off because that's what everyone does. But is that actually optimal?"
📋 Dataset Variables: purchased: 1 if customer bought the polo, 0 if browsed but didn't buy sale_price: Price after discount ($22.50 to $40.50 depending on markdown) loyalty_member: 1 if customer is in loyalty program, 0 if not—members may be less price-sensitive prior_purchases: Number of purchases in past 12 months (0-15)—repeat customers may respond differently to discounts
🤔 Classroom / Managerial Analysis Questions:- What discount depth maximizes profit per week during clearance?
- Is the traditional 40% off approach close to optimal, or significantly off?
- If 500 units remain in inventory (worth $7,500 at cost), at what price do you maximize recovery?
- How does the optimal discount change if you have 8 weeks vs 2 weeks to clear inventory?
You're the Marketing Director at IronCore Fitness , a boutique gym chain with 6 locations in the Denver metro area. The gyms offer premium equipment, group classes, and personal training. Owner Derek Miles is considering raising prices from the current $69/month to fund facility upgrades, but is nervous about member churn.
📋 Dataset Variables: signed_up: 1 if prospect became a member, 0 if declined or went to competitor monthly_price: Monthly membership price quoted during facility tour ($49–$99) segment: Prospect demographic: "student" (college-age), "adult" (25–54), "senior" (55+)
🤔 Classroom / Managerial Analysis Questions:- What monthly price maximizes profit given current capacity constraints?
- At what membership level does IronCore become capacity-constrained? How should pricing change then?
- If Derek adds a "founding member" discount of $10/month for the first 100 signups, what should the standard price be?
- What's the profit impact of raising price from $69 to $79? Would you recommend it?
You're the Director of Pricing Strategy at StreamWave , a new streaming service launching with exclusive sports and documentary content. The service targets the "cord-cutter" demographic—affluent viewers who want premium content without cable bundles. CEO Rachel Kim needs a launch price recommendation before the Q2 marketing campaign kicks off.
📋 Dataset Variables: subscribed: 1 if trial user converted to paid, 0 if cancelled at trial end price_shown: Monthly subscription price displayed at conversion prompt ($8.99–$24.99) household_size: Number of people in household (1-6)—larger households may value streaming more (multiple viewers)
🤔 Classroom / Managerial Analysis Questions:- What launch price maximizes subscriber count? What price maximizes profit?
- At the profit-maximizing price, how many months until StreamWave breaks even on fixed costs?
- If Rachel wants to prioritize subscriber growth (for investor optics), what's the cost in annual profit?
- Should StreamWave consider a $9.99 "with ads" tier alongside a premium tier? What price split?
You run the spa at Alder & Vine Hotels ' flagship coastal property, which also sells day visits to locals. Your signature 60-minute facial normally retails for $120, but foot traffic has plateaued. Your marketing consultant suggested running pricing experiments through daily deal platforms —using Groupon, LivingSocial, and your own email list to offer the same facial at different discounted prices and measure response.
📋 Dataset Variables: purchased: 1 if customer bought the deal, 0 if they viewed but didn't purchase deal_price: Discounted facial price shown in the deal ($29–$79 range) channel: Deal platform: "groupon", "livingsocial", or "direct_email" repeat_customer: 1 if the viewer has visited the spa before, 0 if new customer
🤔 Classroom / Managerial Analysis Questions:- What deal price maximizes profit from the deals alone (ignoring future full-price visits)?
- How do conversion rates differ across Groupon vs. LivingSocial vs. direct email? Should you allocate budget differently?
- Are repeat customers really less price-sensitive? What does the covariate coefficient tell you?
- If Groupon takes a 50% cut but direct email costs nothing, at what volume does direct email become more profitable?
You're the Brand Manager at HarvestTable , an artisanal condiment company selling through grocery retail, DTC (website), and wholesale channels. Your flagship Classic Marinara Sauce has been priced at $8.99 since launch, but rising ingredient costs and a new competitor at $7.49 are squeezing margins.
📋 Dataset Variables: outcome: 1 = purchased, 0 = passed on the product price: Shelf price shown ($6.99–$13.99) channel: Retail, DTC, or Wholesale
🤔 Classroom / Managerial Analysis Questions:- What price maximizes monthly profit for HarvestTable's Classic Marinara across all channels?
- Should HarvestTable charge different prices per channel, or is one price optimal?
- If COGS rose to $5.00 due to tomato crop shortages, how should the price adjust?
- At the optimal price, how many jars per month does HarvestTable need to sell to cover fixed costs?
You're the Head of Ticketing for the Marlowe Riptide , a second-season club in the brand-new Major League Women's Lacrosse (MLWL) . Single-game tickets are the top of your funnel — the first taste that turns a curious local into a member. The 5,000-seat Marlowe Arena is rarely full, and the front office is split.
📋 Dataset Variables: price: Advertised single-game ticket price ($10–$45) outcome: 1 = bought a ticket, 0 = passed
🤔 Classroom / Managerial Analysis Questions:- What single-game price maximizes gameday profit given the cost structure?
- How many seats does the profit-maximizing price fill — and how far is that from the 5,000-seat capacity?
- If the club valued each attendee's downstream merch/concession/membership at an extra $6, how should the optimal price change?
- Make the recommendation: chase ticket profit, or discount to fill the building? Defend it with the numbers.
You're the data analyst at AdArchy , a tiny indie publisher of a 4-player card game where players race to flood each other's hands with absurd digital ads. Boxes ship from one warehouse in Newark through two channels: Amazon and AdArchy's own Website .
🤔 Classroom / Managerial Analysis Questions:- What weekly profit-maximizing price does AdArchy charge on the Website? On Amazon?
- Which channel has the steeper demand elasticity at $24.99?
- How much does Amazon's 15% commission shift the optimal Amazon price relative to Website?
- At the optimal price mix, can either channel alone clear the $2,000 weekly fixed cost?
Thistledown Market , a nine-store family grocer, wants to know if its loyalty program causes customers to spend more — or if high-spending customers simply self-select into the program. Enrolled members averaged $1,603/yr vs $1,320/yr for non-enrolled, a raw gap of $284. The Loyalty and Insights Lead, Lorna Whitcombe, needed a cleaner answer: "If loyalty is truly driving incremental spend, that's a major impact on our top tier. But only if it's causal, not just selection bias from our best customers signing up."
📋 Dataset Variables: Enrolled: Treatment: enrolled in loyalty program (1) vs. not enrolled (0) Total_Spend: Annual purchase spend in dollars (post-enrollment period) Prior_Spend: Annual spend in the 12 months before enrollment decision (key confounder) Months_Customer: Account tenure in months at time of enrollment decision Age: Customer age (years) — affects both enrollment likelihood and spend patterns Store_Visits: Number of in-store visits in prior 6 months
🤔 Classroom / Managerial Analysis Questions:- After PSM, how much of the original $284 raw gap survives as a causal treatment effect?
- Which covariate created the most imbalance before matching — and why does that matter for causal interpretation?
- How would you communicate the ATT estimate to Lorna Whitcombe in business terms, acknowledging the remaining limitations of observational data?
CartZone , a mid-size e-commerce retailer, sent a 20%-off promotional email to its most engaged subscribers and wants to quantify the campaign's true causal lift on 7-day conversion. The raw gap looks compelling — recipients converted at 19.3% vs 9.1% for non-recipients. But the email manager, Terrence Liu, was cautious: "Our targeting algorithm already picks the customers most likely to buy. I'm not willing to claim a 10-point lift until we account for that selection effect."
📋 Dataset Variables: Received_Email: Treatment: received the promotional email (1) vs. did not receive (0) Converted: Outcome: made a purchase within 7 days (1 = yes, 0 = no) Past_Purchases: Number of purchases in the prior 90 days (key selection confounder) Days_Since_Last_Visit: Recency: days since last site visit (lower = more engaged) Email_Opens_90d: Number of emails opened in the prior 90 days Account_Age: Account tenure in months
🤔 Classroom / Managerial Analysis Questions:- After matching, what is the estimated causal effect on conversion — and how much of the 10-point raw gap was explained by selection bias?
- Which covariate created the worst imbalance before matching, and how does that inform the targeting logic going forward?
- If the ATT is 4 percentage points, how would you calculate the incremental revenue for 8,000 customers with an average order value of $85?
AuraLabs , a premium wellness brand, ran a 30-day display ad flight on a publisher network and commissioned a brand lift survey to measure impact on aided awareness. Exposed users scored 57 vs 44 for unexposed — a 14-point raw gap. The brand's media director, Jamie Cortez, was skeptical: "Our ads are targeted at people who already visit wellness content. I need to know the true lift, not just a number inflated by category interest."
📋 Dataset Variables: Saw_Ad: Treatment: saw at least one display impression (1) vs. zero impressions (0) Awareness_Score: Aided brand awareness (0–100 scale from post-flight survey) Prior_Awareness: Awareness score from a pre-flight survey wave (key confounder) Site_Visits: Number of wellness content visits in the prior 30 days Category_Interest: Self-reported category interest score (0–100) from survey Age_Group: Age group category (18–24, 25–34, 35–44, 45–54, 55+)
🤔 Classroom / Managerial Analysis Questions:- After PSM, what is the causal estimate of ad-driven awareness lift — and how much of the 14-point raw gap was selection bias?
- If Prior_Awareness doesn't fully balance after matching, what does that tell you about the adequacy of the propensity model?
- How would you differentiate the message to Jamie Cortez: "the ads worked" vs. "the ads reached people who already knew us"?
GlowUp Beauty , a direct-to-consumer skincare brand, partnered with 15 mid-tier Instagram and TikTok influencers for a 6-week campaign showcasing their new serum line. Customers exposed to influencer content converted at 25.1% vs 13.3% for unexposed — a 12-point raw gap. The head of growth, Priya Nair, was skeptical: "Our most socially active customers are the ones who see these posts in their feed. They were already more likely to buy. I want to know what the influencers actually added."
📋 Dataset Variables: Influencer_Exposed: Treatment: exposed to influencer content (1) vs. no exposure (0) Converted: Outcome: made a purchase within 14 days (1 = yes, 0 = no) Social_Engagement_Score: Social media activity score (0–100) over prior 30 days (key confounder) Prior_Purchases: Number of purchases from GlowUp in prior 12 months Follower_Count: Customer's own social follower count (higher = more feed activity exposure) Age: Customer age in years
🤔 Classroom / Managerial Analysis Questions:- After PSM, what share of the 12-point raw gap was genuine influencer-driven conversion vs. selection bias from social engagement?
- Should Priya re-target influencer campaigns at lower-engagement customers where organic discovery is less likely to confound?
- If the ATT is 5 percentage points and the campaign cost $90K across 15 influencers, what is the cost per incremental conversion?
TaskFlow , a B2B project management SaaS platform, offers a 14-day free trial to visitors who browse the product documentation. Trial users subscribe to paid plans at 23.1% vs 11.3% for non-trial visitors — a 12-point raw gap. The VP of Product, Marcus Webb, was pushing back: "The people who start trials are already our most engaged visitors. They browse more pages, come back more often, and were probably going to buy anyway. Show me the trial's actual contribution."
📋 Dataset Variables: Used_Trial: Treatment: activated the 14-day free trial (1) vs. did not (0) Subscribed: Outcome: converted to paid subscription within 30 days (1 = yes, 0 = no) Pages_Viewed: Total documentation and feature pages viewed (key engagement confounder) Referral_Source: How the visitor arrived: Organic, Paid_Search, Social, or Direct Prior_Sessions: Number of site sessions in the 30 days before trial decision Company_Size: Visitor's company size band: 1–10, 11–50, 51–200, 201–1000, or 1000+
🤔 Classroom / Managerial Analysis Questions:- After PSM, how much of the 12-point raw gap was genuine trial-induced conversion vs. self-selection by already-engaged visitors?
- Should TaskFlow make the trial more prominent to less-engaged visitors to drive incremental conversions, or would those users churn faster?
- If Company_Size matters for both trial activation and subscription, what business logic explains that pattern?
LuxeThreads , an online fashion retailer with 280K active customers, tested a premium 30%-off coupon sent to a subset of their customer base. Recipients converted at 32.6% vs 16.4% for non-recipients — a 16-point raw gap. The CRM director, Aisha Patel, had concerns: "We sent the premium coupon to our Gold and Platinum members — customers who were already likely to buy. The 16-point lift looks great in a deck, but I need the actual causal number before I blow up the discount budget."
📋 Dataset Variables: Premium_Offer: Treatment: received the premium 30%-off coupon (1) vs. not (0) Converted: Outcome: made a purchase within 14 days (1 = yes, 0 = no) Days_Since_Last_Visit: Recency: days since last site visit (lower = more active) Prior_Orders: Number of orders in the prior 90 days Avg_Order_Value: Average order value over past 6 months (in dollars) Loyalty_Tier: Customer tier: Bronze, Silver, Gold, or Platinum
🤔 Classroom / Managerial Analysis Questions:- After matching, what is the genuine causal lift from the premium coupon — and how much of the 16-point gap was explained by targeting high-value customers?
- What percentage of coupon recipients would have purchased anyway (the "deadweight loss" of the discount)?
- Should Aisha target the premium coupon at Bronze/Silver members instead, where natural conversion rates are lower and incremental lift may be higher?
TrailBlaze , an outdoor recreation brand with 85K loyalty members, hosted a series of weekend "Adventure Days" at 8 locations — trail runs, gear demos, and clinics. Attendees spent an average of $266 in the subsequent 90 days vs $188 for non-attendees, a $78 raw gap. The events director, Carlos Reyes, wanted proof: "Our most dedicated members are already heavy spenders. They drove hours to attend because they love our brand, not because the event changed their behavior. What's the actual event-driven lift?"
📋 Dataset Variables: Attended_Event: Treatment: attended an Adventure Day event (1) vs. did not (0) Post_Event_Spend: Total purchase spend in the 90 days following the event (in dollars) Pre_Event_Spend: Total spend in the 90 days before the event (key confounder) Membership_Months: Length of loyalty membership at event date (months) Distance_Miles: Distance from member's home to nearest event venue (miles) Age: Member age in years
🤔 Classroom / Managerial Analysis Questions:- After PSM, how much of the $78 raw gap is causal event-driven spend vs. pre-existing brand loyalty?
- Distance is the strongest predictor of non-attendance. Does this suggest TrailBlaze should host events in underserved locations to reach members who are currently too far?
- If the ATT is $45, what is the return on the $120K total event investment across 728 attendees?
FitPulse , a fitness tracking app with 1.2M monthly active users, launched a new "Smart Workout Planner" feature and wants to know if it drives retention. Users who adopted the feature had 56.4% 30-day retention vs 40.9% for non-adopters — a 15.5-point raw gap. The product manager, Dylan Torres, was wary: "Our power users discover every new feature we ship. They're also the ones who were never going to churn. I need to isolate whether the feature itself keeps people around, not just whether engaged users stay."
📋 Dataset Variables: Used_Feature: Treatment: used the Smart Workout Planner at least once (1) vs. never (0) Retained_30d: Outcome: still active 30 days after feature launch (1 = yes, 0 = no) Sessions_Last_30d: App sessions in the 30 days before feature launch (key engagement confounder) Account_Age_Months: Account tenure in months — longer tenure = less churn risk Workouts_Logged: Workout sessions logged in prior 30 days Premium_Subscriber: Whether the user pays for premium (1) vs. free tier (0)
🤔 Classroom / Managerial Analysis Questions:- After PSM, how much of the 15.5-point retention gap is attributable to the feature vs. pre-existing engagement differences?
- Should Dylan promote the feature more aggressively to less-engaged users, or would those users not benefit from it?
- If the ATT is 5 percentage points and FitPulse has 1.2M MAU, how many incremental retained users does this feature create at scale?
An omnichannel retailer ran 60 paid-media campaigns with budgets from about $7k to $100k and recorded the sales revenue each one produced. The media team fitted a straight line of revenue on spend and wants to use its slope to plan next year's budget.
📋 Dataset Variables: campaign_id: Campaign identifier (not used in the model) ad_spend_k: Paid media spend on the campaign, in thousands of dollars sales_revenue_k: Sales revenue attributed to the campaign, in thousands of dollars
A software vendor closed 60 annual contracts with clients of 13 to 200 employees. Deal values run from about $30k to $275k. Sales operations fitted a straight line of deal value on client headcount to set quota and to quote a forecast range for each new prospect.
📋 Dataset Variables: deal_id: Deal identifier (not used in the model) client_employees: Client headcount, in employees deal_value_k: Annual contract value, in thousands of dollars
A beauty brand paid 51 creators to promote a launch, from micro-creators with about 11,000 followers to one celebrity account with 450,000. Each partnership used its own discount code, so the orders it generated are known. The team fitted a straight line of orders on reach to price next season's deals.
📋 Dataset Variables: partnership_id: Partnership identifier (not used in the model) reach_thousands: Creator's follower reach, in thousands orders_generated: Orders placed with the creator's discount code
A home-goods retailer logged two years of weekly advertising spend and store sales. Spend follows the seasons and four-week promotional bursts each quarter. The analyst fitted a straight line of weekly sales on that week's spend and reported a tight confidence interval for the slope.
📋 Dataset Variables: week_id: Week identifier (not used in the model) week: Week number, 1 to 104: the time order of the rows ad_spend_k: Advertising spend that week, in thousands of dollars sales_k: Store sales that week, in thousands of dollars
A specialty retailer with 120 stores sets each store's local advertising budget. Stores sit in four regions: Midwest, Northeast and South have 38 stores each, and the chain has only recently opened 6 stores in the West. The analyst fitted store sales on local ad spend and region, with the Midwest as the baseline region.
📋 Dataset Variables: store_id: Store identifier (not used in the model) ad_spend_k: Local advertising spend for the store, in thousands of dollars region: Midwest, Northeast, South or West (baseline: Midwest) sales_k: Store sales for the year, in thousands of dollars
A data-platform vendor closed 150 deals across four industries: Manufacturing, Retail, Healthcare and Software. About 40% were renewals of an existing contract. The revenue team fitted deal value on client headcount, industry and renewal status, with Manufacturing and new business as the baselines.
📋 Dataset Variables: deal_id: Deal identifier (not used in the model) headcount_00: Client headcount, in hundreds of employees industry: Manufacturing, Retail, Healthcare or Software (baseline: Manufacturing) is_renewal: yes for a renewal, no for new business (baseline: no) deal_value_k: Contract value, in thousands of dollars
SunState University , a Southern California public university serving 28,000 students, recently deployed a new online course enrollment system to replace its aging registration platform. The IT director, Marcus Webb, faces the next enrollment window in 6 weeks and needs to assess early user reception: "Before we open registration for 28,000 students, I need to know if the pain points in these early posts are fixable quirks or signs of a deeper UX failure."
📋 Dataset Variables: text: Reddit-style post content from students about the new enrollment system N = 20 posts: Mix of praise (modern interface, speed) and complaints (bugs, timeouts, waitlist logic)
🤔 Classroom / Managerial Analysis Questions:- What percentage of the 20 posts are classified as positive, neutral, or negative — and is that ratio acceptable for a new system launch?
- Identify the 3 most negative posts: what specific system failures do they mention, and how would you prioritize the fixes?
- How would you summarize this analysis in one paragraph for Marcus Webb's IT steering committee briefing?
LumaSurf , a direct-to-consumer swimwear line launched by a lifestyle influencer with 2M+ followers, is seeing its first wave of product reviews on the brand website ahead of a planned summer campaign expansion. Marketing director Chloe Park is cautiously optimistic but concerned: "Our aggregate rating is 4.2 stars but social DMs are full of fit complaints. I need to know if the sentiment data supports the expansion budget or if we need to fix sizing first."
📋 Dataset Variables: text: Full review text from customers who purchased via the brand website N = 10 reviews: Mix of aesthetic praise and fit/sizing concerns
🤔 Classroom / Managerial Analysis Questions:- What is the ratio of positive to negative/neutral reviews — and does that support or complicate Chloe's plan to expand the summer campaign?
- Examine the most negative reviews: are the complaints product-fixable (sizing chart, grading) or aesthetic (design choices)?
- Draft a 2-sentence product team recommendation based on the sentiment analysis results, prioritizing what to address before the summer expansion.
ProHydro , a CPG company preparing to launch a new premium reusable water bottle at $49 MSRP, commissioned competitive research across 3 leading brands to identify positioning opportunities before their go-to-market launch. VP of Product, Natalie Yuen, set the stakes clearly: "If HydroFlask customers are happy and Yeti customers are complaining about durability, we know exactly where to aim our launch messaging. I need brand-level sentiment breakdowns, not just an average."
📋 Dataset Variables: review: Illustrative review text written for this teaching scenario (not real customers' reviews), one per row brand: Group variable: HydroFlask, Yeti, or Stanley
🤔 Classroom / Managerial Analysis Questions:- Which of the 3 brands has the highest average compound sentiment score — and which has the most variance (indicating mixed reception)?
- What are the top 3 negative themes across all brands? Do those gaps represent launch opportunities for ProHydro at $49?
- Based on the grouped sentiment analysis, write a 3-sentence competitive positioning recommendation for Natalie Yuen's launch brief.
GlowRoute is a direct-to-consumer skincare brand in Los Angeles: serums, moisturizers and sunscreen bundles sold through its own site, about $120,000 a month in paid media. Its 30-day Summer Glow launch added $61,500 across five channels, and the launch dashboard credits every order to the last channel the shopper touched. On that rule Delphine Arceneaux , the CMO, read Email as the launch's best channel and the Instagram ads as money lost, and she is about to set next month's budget on it. She has asked for an audit first: the same 33,000 shopper journeys with every touch kept, so the dashboard's number can be reproduced and then tested.
📋 Dataset Variables: Email (three launch sends): $2,400 Google Ads (brand and non-brand search): $21,000 Retargeting Display: $7,600 Instagram Ads: $18,500 Creators (two paid partners): $12,000
🤔 Classroom / Managerial Analysis Questions:- Reconcile. Does the Last-Touch column reproduce the dashboard's reading, Email 35.3% and Instagram Ads 14.0% at $0.94? If not, what is different?
- Mechanism. Which channels sit at the start of the converting journeys and which at the end? What does that do to a last-click count?
- Corrected figure. Under Shapley and under Markov, what share does each channel earn, and which channels now clear $1.82?
- Price the gap, then write. If Delphine had cut the channel the dashboard called a loss, what would the launch have given up under each rule? One paragraph to her: the corrected figure next to the reported one, the mechanism in a sentence, and what changes next month.
Brand manager Maya Lin ran a campus packaging test comparing a Matte Black Can against a Clear Glass Bottle on purchase intent ( Q6_PurchaseIntent ) alongside a 6-row brand perception matrix ( Q4_1 – Q4_6 ) before committing to a minimum can run. In the raw 3-row Qualtrics export, the packaging lift looks washed out—raising the question of whether students genuinely feel indifferent or careless responses are burying the effect.
Growth lead Priya Nair promoted a \$100 gift-card sweepstakes link on TikTok to price a new Vitamin C serum ( Q8_WTP_Serum ) against an 8-item efficacy battery ( Q3_1 – Q3_8 ). Notice that Qualtrics exported duration under the shifted column name Duration__in_seconds_ alongside bot telemetry ( Q_RecaptchaScore , Q_RelevantIDFraudScore ) and open-ended reviews ( Q10_Review_TEXT ).
VP of Customer Success Jordan Reyes surveyed verified B2B client admins on renewal intent ( Q9_RenewalIntent ) across Q1_AccountTier . The questionnaire pairs a short 4-item unidirectional Account Support block ( Q2_1 – Q2_4 , zero reverse items) with a 9-item bidirectional Platform Feature grid ( Q5_1 – Q5_9 , including reverse-worded Q5_8 ).
You're the retention analyst at FreshBox , a $29/month snack subscription service. Your churn model scores every active subscriber monthly with a predicted probability of cancelling in the next 30 days .
StreamFlow ($14.99/month) runs a concierge retention programme for at-risk subscribers. A trained rep calls, troubleshoots account issues, and can offer up to three months of a discounted rate.
Kiteline Analytics (a self-serve analytics platform whose Enterprise tier is an $80K/year contract) scores every inbound qualified lead with a predicted conversion probability . A "pitch-ready" designation triggers a full enterprise discovery process: two-day onsite visit, engineering demo team, custom ROI analysis.
Nightjar , a challenger streaming service with sports and documentary content, sells three plans. Each week about 50,000 free trials end, and each of those viewers either picks a plan or leaves. Over the past year the pricing team moved plan prices week to week, and in weeks when demand was already strong it usually pushed them up. The team reads price sensitivity from a plain regression of these sales, and VP of Pricing Devon Brooks wants that read checked before the next price change.
📋 Dataset Variables: period: The week, from 2025-W01 to 2025-W52. product: The plan: Basic, Standard or Premium. price: The plan's monthly price that week, in dollars. units_sold: Trials that converted to that plan that week. instrument: A content cost index (100 = average): what studio partners charge for the film and series catalogue that week, set by contracts signed months earlier. It moves price but not how much viewers want the service.
A regional gym chain sells four ways to join. Each week about 8,000 people in its area are shopping for a gym (walk-ins, tours and website visitors), and each of them buys one of the four or joins nobody. The chain resets its prices each week from an operating cost index covering energy, rent and staffing, not from how busy the week looks. The general manager wants the chain's plain regression of past sales checked before it is used to set prices.
📋 Dataset Variables: period: The week, from 2025-W01 to 2025-W52. product: DayPass (one visit), Monthly (month to month), Annual (a 12-month membership with classes, priced per month) or Family (a household plan, priced per month). price: That tier's price that week, in dollars. units_sold: Passes or memberships sold in that tier that week. instrument: The chain's operating cost index (100 = average) for that week.
A business software company sells three per-seat plans. Each month about 12,000 teams finish a free trial, and each team buys a plan or walks away. The sales team has moved list prices month to month and pushed them up when demand was strong. A data scientist on the team pitches a fix: use the cloud hosting cost index as the instrument and trust the corrected estimate. Hosting is a small part of what a seat costs, so check whether that index really moves price before you accept the pitch.
📋 Dataset Variables: period: The month, from 2023-01 to 2025-12. product: The plan: Starter, Pro or Enterprise. price: The plan's monthly list price per seat, in dollars. units_sold: Trials that converted to that plan that month. instrument: A cloud hosting cost index (100 = average) for that month.
Marchbank Industrial Supply has run paid search continuously for two years. The analyst fits revenue on ad spend, gets an R² she is happy with, and reports a confident marginal return per dollar.
🤔 Classroom / Managerial Analysis Questions:- Does the diagnosis point at the outcome or the predictor? Why?
- After logging revenue, what does the slope mean in plain English?
- The baseline R² and the logged model's R² are both reported on the original revenue scale. Why does that matter for the comparison?
Marram Running Co. has run 140 display campaigns at wildly different budgets, from 5,000 impressions to nearly half a million. The media team wants one number: conversions per thousand impressions.
🤔 Classroom / Managerial Analysis Questions:- Which variable does the diagnosis point at, and why is it not the outcome?
- After logging impressions, what does the slope mean per 1% increase ?
- Why is "conversions per thousand impressions" a misleading KPI for this relationship?
Crestline Coffee has priced its single-origin bags anywhere from $9 to $34 across two years of promotions. Finance wants one number before the next promo calendar is signed: if we drop price 10%, how much more do we sell?
🤔 Classroom / Managerial Analysis Questions:- Adopt the logged outcome only, then the logged predictor only, then both. Which residual plot is still telling you something is wrong, and what?
- The elasticity is below −1. What does that imply for total revenue when price falls — and would your answer change if it were −0.6?
- Why can the log-log slope be quoted to Finance as-is, when the coefficient from a square-root model could not be?
- Both variables were transformed here for a reason you could have stated in advance. What was it? What would it have taken to make this a p-hacking story instead?
Not every dataset needs fixing. Sableford Regional Food Bank emails 120 donor-list segments, and the relationship between sends and opens is about as well-behaved as marketing data gets.
🤔 Classroom / Managerial Analysis Questions:- What is the R² gain from the top-ranked transform? Would you defend it?
- What do you give up by transforming a variable you did not need to?
- How would you report this model to a manager in one sentence?
Carrow Valley Growers tracks prompted awareness of its produce brand monthly against media weight. Marketing Director Simone Nwachukwu finds raw percentage points hard to read across an eight-year chart and asks for everything indexed to the first year at 100, "so we can see the lift."
🤔 Classroom / Managerial Analysis Questions:- Which numbers moved, which did not, and why?
- If standardising cannot improve fit, why do analysts do it so often?
- Your colleague says indexing "made the model stronger." What went wrong in their reasoning?
Frost and Fold Creamery is a craft ice-cream maker in Vermont ($18M annual wholesale revenue across 420 independent grocers) that just won a regional slotting review with a major New England supermarket chain (185 stores). Category VP Rachel Vance and Head R&D Chef Marco Bellini tested 12 candidate pint flavors with N = 320 grocery shoppers on a 1–5 purchase-intent scale, where a 4 or 5 counts as "would buy". As Rachel warned the product team: "The buyer at Kroger gives us three facings, not four, and charges $45,000 per SKU in slotting fees. If we just ship our three highest-rated chocolate flavors, we will pay $135,000 to sell three pints to the exact same chocolate lovers while fruit and specialty shoppers walk right past our door."
📋 Dataset Variables: respondent_id: Anonymized grocery panelist ID ( R001 – R320 ), screened as a regular premium ice-cream buyer (2+ pints/month). Chocolate Cluster (3 SKUs): 1–5 purchase intent for Salted Caramel Swirl , Brown Butter Pecan , and Dark Chocolate Truffle — the three highest-scoring individual flavors (44%–52% standalone appeal), but heavily overlapping among indulgent fans. Fruit & Sorbet Cluster (4 SKUs): 1–5 purchase intent for Mango Sorbet , Lemon Verbena Tart , Strawberry Balsamic , and Roasted Peach Cobbler — lower standalone ratings, but strong appeal among shoppers who reject heavy chocolate/caramel pints. Specialty & Classic (5 SKUs): 1–5 purchase intent for Cold Brew Espresso , Pistachio Cardamom , Madagascar Vanilla Bean , Mint Stracciatella , and Maple Walnut Crunch .
🤔 Classroom / Managerial Analysis Questions:- Quantifying the Overlap Penalty: At k = 3 under Top-2-Box, how many percentage points of market reach does Frost & Fold gain by choosing the TURF optimal trio over the three most popular flavors, and how many additional shoppers out of N = 320 does that represent?
- Evaluating a 4th Freezer Facing: Rachel can purchase a 4th supermarket facing for an extra $45,000 slotting fee. Use the Reach Curve ("How many do you need?") to find the marginal reach gain from k = 3 to k = 4 and advise whether the 4th facing is worth the cost.
- Defending the Niche Swap-In: Chef Marco argues that Mango Sorbet has a lower standalone rating than Brown Butter Pecan and should be cut. Use the Item Contribution table (Sequential Ladder Gain, Shapley Reach Credit, and Removal Loss) to defend Mango Sorbet's place in the 3-SKU lineup.
Harborline Bank is a branchless digital bank with 340,000 checking accounts preparing a $4.2M national acquisition campaign across streaming video, podcasts, and paid social. CMO Marisol Vega and CFO Ted Brannigan just completed a MaxDiff study with N = 260 prospective bank switchers across 10 candidate value-proposition claims. Now the landing page can feature three claims , and a claim counts as landing with a customer if it sits in that customer's personal top two. As Marisol told the creative agency: "Our three highest-scoring claims in MaxDiff—No Monthly Fees, No Minimum Balance, and Fee-Free Overdraft—all say the exact same thing in different words. If we put all three on the launch page, we speak only to fee-averse switchers and leave rewards seekers and security-minded customers cold."
📋 Dataset Variables: respondent_id: Prospective checking-account switcher ( R001 – R260 ) from the national panel. segment: Customer's primary banking motivation ( Fee-averse , Rewards-seeker , or Safety-first ), used to verify whether the 3-claim landing page speaks to every cohort. Fee-Elimination Claims (3 Items): 0–100 MaxDiff preference shares for No monthly fees, ever , No minimum balance , and Fee-free overdraft up to $200 — dominant in the Fee-averse segment but redundant with each other. Rewards, Access & Safety (7 Items): 0–100 MaxDiff preference shares for Cashback on everyday debit spending , Get paid up to two days early , 24/7 support from a real person , Instant card lock in the app , Nationwide fee-free ATM network , Automatic savings round-ups , and Credit score monitoring built in .
🤔 Classroom / Managerial Analysis Questions:- Landing Page Reach Lift: Under the default "In their top 2" rule at k = 3, which three messages form the optimal launch-page bundle, and how many percentage points of reach do they gain over the three highest-scoring MaxDiff claims?
- Segment Blind-Spot Audit: Compare the "Chosen TURF set" vs. "Most popular set" reach across the three customer segments in the Segment Reach table. Which segment suffers the worst collapse if Marisol uses the popular set?
- Threshold Robustness: CFO Ted Brannigan asks whether the same three messages win if you define "reached" as a MaxDiff score of 20+ points instead of Top-2 rank. Check the Rule Sensitivity table and state your recommendation.
StreamBox is a direct-to-consumer streaming platform with 1.4M subscribers launching a new $5/month "Variety & Lifestyle" 4-channel add-on pack . Licensing budgets allow exactly four channels out of 15 niche candidates surveyed across N = 400 subscribers (binary 1/0 regular viewing intent). However, Content Director Devon Brooks and Growth VP Priya Nair face a hard contractual constraint: GameDay Sports was already guaranteed a bundle slot in last quarter's multi-year carriage renewal. As Devon told the analytics team: "The sports deal is signed, so work around it. Show me the best four channels without constraints, then lock GameDay Sports in and tell me exactly how many subscribers that contract is costing us."
📋 Dataset Variables: respondent_id: Surveyed StreamBox subscriber ( R001 – R400 ) evaluating the $5/month add-on bundle. GameDay Sports (Contracted): Binary indicator ( 1 = would watch regularly, 0 = would not) for the pre-contracted sports channel. Carries a 1-click lock-in prompt in Step 2 to measure its exact reach cost. Remaining 14 Niche Channels: Binary 1/0 viewing indicators across entertainment, crime, sci-fi, food, travel, documentary, anime, British mystery, and kids channels competing for the remaining bundle slots.
🤔 Classroom / Managerial Analysis Questions:- Unconstrained vs. Contracted Reach: Which four channels maximize subscriber reach when StreamBox has complete freedom, and how many percentage points of reach does locking in GameDay Sports cost?
- Revenue Opportunity Cost: Across 400,000 target add-on prospects, if 10% of reached subscribers convert at $5/month ($60/year), what is the annual revenue cost of forcing GameDay Sports into the 4-channel bundle?
- Negotiation Backup Plan: If the rights holder for one of your other winning channels demands an unacceptable licensing fee hike, which near-tie portfolio from the Top Portfolios table would you pivot to, and is the reach difference statistically distinguishable from sampling noise?
CivicPulse is a 220-person civic-tech software company in Minneapolis whose board approved $1.1M in new annual benefits funding —enough to fund three of nine proposed benefits after losing four senior engineers to competitors. Chief People Officer Lena Ortiz surveyed N = 180 employees across three career-stage cohorts ( Early-career , Mid-career , and Late-career ) using MaxDiff, where a benefit counts as materially mattering to an employee if it captures 20 or more of their 100 preference points . As Lena cautioned HR leadership: "A benefit package that reaches 82% of the company on average can still miss a whole floor of the building if the 18% we leave behind are almost all our senior staff. Check the segment breakdown before we sign this off."
📋 Dataset Variables: respondent_id: Anonymized employee ID ( R001 – R180 ) from the internal benefits prioritization study. segment: Employee career-stage cohort ( Early-career , Mid-career , Late-career ) used to audit demographic equity across the workforce. Work-Style & Core Health (3 Items): 0–100 MaxDiff preference shares for Four-day work week , Fully remote option , and Employer-paid health premiums . Stage-Specific Benefits (6 Items): 0–100 MaxDiff preference shares for Annual learning budget , 16 weeks paid parental leave , Retirement match up to 6% , Unlimited paid time off , On-site childcare subsidy , and Company equity grant .
🤔 Classroom / Managerial Analysis Questions:- Diagnosing the Cohort Gap: Under the default 3-benefit portfolio ( threshold ≥ 20 ), what is the overall employee reach compared to the reach in each of the three career-stage segments ( Early-career , Mid-career , Late-career )?
- Equity vs. Headcount Trade-Off: Lock in "Retirement match up to 6%" in the Step 2 Constraints panel. How many percentage points does Late-career reach improve, and what is the cost to overall company-wide reach?
- Board Recommendation for Lena: Advising CPO Lena Ortiz on allocating the $1.1M budget across 3 benefits, would you recommend the unconstrained headcount-maximizing trio or the constrained cohort-balanced trio? Defend your choice with numbers from the segment table.
BrandLaunch , a DTC beauty startup managing 150 creator partnerships across Instagram, TikTok, YouTube and Twitter for a product launch campaign, needed a distribution baseline before building tier-performance scoring models. "We can't rank influencers until we understand what the data actually looks like — are we dealing with a normal distribution or a handful of stars pulling up the average?" — Influencer Marketing Manager
📋 Dataset Variables: followers: Follower count (5K–2M) — 7 values missing engagement_rate: (Likes + comments) / followers, as a % (0.5–9.2) avg_likes: Mean likes per post — 6 values missing avg_comments: Mean comments per post posts_per_month: Posting cadence (1–30) audience_age_median: Median age of their audience (18–37)
🤔 Classroom / Managerial Analysis Questions:- Is engagement_rate approximately normal, or right-skewed by a few top performers?
- What's the coefficient of variation for cost_per_post? Are there outlier partnerships overpaying relative to performance?
- Which platform dominates the roster, and does that concentration create sampling risk for future comparisons?
NestGoods , a consumer products brand, collected a post-purchase survey from 500 customers with a 40% response rate (n = 200) to establish a customer satisfaction baseline before launching a CX improvement initiative. "A 40% response rate gives us enough to act, but first I need to know what shape the data is in — are we dealing with a normal satisfaction curve or a bimodal love-hate split?" — VP of Customer Experience
📋 Dataset Variables: satisfaction_score: Overall satisfaction, 1–7 nps_score: Net Promoter Score question, 0–10 recommendation_likelihood: Likelihood to recommend, 1–10 — a second wording of the NPS question annual_spend: Spend over the past year ($100–$14,187) — 8 values missing lifetime_months: Months as a customer (1–120) email_engagement_score: Composite email engagement, 0–100
🤔 Classroom / Managerial Analysis Questions:- Is the nps_score distribution bimodal (promoters vs. detractors) or approximately normal?
- What's the median vs. mean annual_spend? A large gap would indicate skewness and suggest the median is more informative.
- nps_score and recommendation_likelihood ask nearly the same question, and satisfaction_level is just satisfaction_score in bands. What goes wrong if you treat all four as independent evidence?
CartGrid , a mid-size e-commerce retailer, exported 180 orders from the past year ahead of a suite of A/B tests and predictive modeling work to establish the baseline distribution of key transaction metrics. "Before we touch the algorithms, I want to know what \u2018normal\u2019 looks like — median order value, typical cart size, right-skew in revenue." — Head of Analytics
📋 Dataset Variables: order_value: Total order amount, $10–$443 items_in_order: Products in the cart (1–10) discount_percent: Promo discount applied, 0–25% — 10 values missing shipping_cost: Delivery fee, $0–$14 delivery_days: Days from order to delivery (1–14) customer_rating: Post-order rating — this export contains only 3s and 4s, plus 6 blanks
🤔 Classroom / Managerial Analysis Questions:- Is order_value normally distributed, or heavily right-skewed by a small number of large orders?
- What's the median vs. mean cart size (items_in_order)? Are there outlier orders with unusually high item counts?
- What proportion of orders have a discount_percent of zero, and how many are blank? Does zero-inflation plus missingness change how you would summarize this variable?
Ashgrove Garden Centre analyzed 50 email campaigns sent over the past quarter to establish performance baselines and identify distribution patterns before building optimization models. "Our team has been optimizing intuitively for years — it's time to look at the actual distribution of open rates and figure out what \u2018average\u2019 really means across our different campaign types." — Rowan Achebe, Head of Merchandising
📋 Dataset Variables: list_size: Subscribers targeted (350–15,000) emails_delivered: Emails that actually landed (list_size minus bounces) open_rate: % who opened (%) click_rate: % who clicked (%) unsubscribe_rate: % who unsubscribed bounce_rate: % bounced emails
🤔 Classroom / Managerial Analysis Questions:- Is open_rate approximately normally distributed across the 50 campaigns, or are there outlier campaigns skewing the distribution?
- How skewed is revenue_attributed? What does the gap between mean and median suggest about a few breakout campaigns?
- Does subject_line_length appear to cluster around a particular range? What does the distribution suggest about copywriting patterns at Ashgrove?
VaultCRM , a subscription e-commerce platform, exported 70 customer records with pre-calculated CLV and behavioral metrics ahead of building a predictive scoring model to prioritize retention investments. "We can't build a predictive CLV model until we understand the shape of the raw data — are we dealing with a power-law distribution or something closer to normal?" — Head of Customer Analytics
📋 Dataset Variables: clv: Customer Lifetime Value ($) months_active: Tenure in months total_orders: Lifetime order count avg_order_value: Average order ($) purchase_frequency: Annualized order rate recency_days: Days since last order
🤔 Classroom / Managerial Analysis Questions:- Is CLV heavily right-skewed? How large is the gap between mean and median CLV, and what does that tell you about the \u2018whale customer\u2019 effect?
- Are support_tickets zero-inflated? What does a spike at zero imply for choosing the right summary statistic?
- What does the churn_risk distribution look like? Is the sample predominantly Low-risk, or is there a meaningful concentration of High-risk customers that VaultCRM should act on immediately?
ContentPilot , a social media management agency, exported 50 recent posts across 4 platforms to build a performance baseline and understand engagement distributions before recommending content strategy changes to its clients. "Before we recommend any strategy changes, we need to establish the baseline — what does a typical post look like, and how viral is \u2018viral\u2019 for our clients' accounts?" — Social Media Strategy Lead
📋 Dataset Variables: impressions: Times content shown reach: Unique accounts reached engagement_rate: Engagement / reach (%) likes, comments, shares, saves: Interaction counts link_clicks: Clicks to website sentiment_score: Comment sentiment, 0 to 1 (observed range 0.35–0.92)
🤔 Classroom / Managerial Analysis Questions:- Is engagement_rate heavily right-skewed (most posts mediocre with a few viral outliers), or approximately normal?
- Compare the coefficient of variation (CV%) for impressions vs. likes. Which metric is more variable, and what does that imply for using it as a KPI?
- Is sentiment_score approximately normal or bimodal? What would a bimodal distribution tell you about how audiences respond to this content?
DataAudit Labs , an analytics quality assurance team, was handed an export from a junior analyst and tasked with finding every data quality problem before anyone runs statistical tests on the 80-session checkout test. "This is what real analyst work looks like — someone hands you a file and says \u2018it should be fine\u2019, and your job is to prove them wrong before we report results to the board." — Senior Data Analyst
📋 Dataset Variables: time_on_page_sec: Seconds on the checkout page pages_per_session: Pages viewed in the session revenue_this_session: Revenue from this session, $ (0 if it did not convert) converted: Did this session buy? 1 = yes, 0 = no bounced: Did this session leave immediately? 1 = yes, 0 = no variant: Control / Treatment
🤔 Classroom / Managerial Analysis Questions:- What impossible or out-of-range values appear in the continuous columns, and what likely caused them?
- Are there outliers in time_on_page_sec or revenue_this_session extreme enough to distort a t-test? What would you do with each one?
- How many distinct data quality issues can you document across all variables? Categorize them by type: impossible values, contradictions between columns, outliers, missing data, duplicates.
PetPost , a DTC pet wellness subscription with ~18K subscribers, saw mixed Q3 results — new signups surged but churn spiked and first-month retention dropped. The CMO asked: "Are we growing, or are we just replacing churned subscribers with lower-quality ones from paid channels?"
📋 Dataset Variables: monthly_spend: Average monthly spend ($) aov: Average order value ($) — one box per month, so this is identical to monthly_spend tenure_months: How long subscribed nps_score: Net Promoter Score (0–10) first_box_rating: Rating of their first box (1–5) support_tickets: Support tickets filed
🤔 Classroom / Managerial Analysis Questions:- Compare the tenure distribution of TikTok-acquired subscribers vs. Referral-acquired. What story does the shape tell?
- Is there a bimodal pattern in NPS scores? If so, what might explain the two groups?
- Calculate the churn rate by acquisition channel. Which channel has the worst retention — and does that align with CAC?