Teaching Library

Every reading, interactive, analytics tool and ready-made homework assignment on the platform, in one place — filtered the way you actually choose material: by the course you teach, the topic, the level of your students, and how much mathematics they can take.

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Browser-Based Marketing Analytics Calculators & Simulators

Explore all 97 interactive analytics apps, the 250-case Business Scenarios & Classroom Datasets Directory, 42 textbook chapter syllabi (browse all chapter syllabi), and the 469-term Marketing Analytics Statistical Glossary:

  • A/B Proportion Test — Compare conversion rates between two variants. (Pre-loaded cases: StudyFlow Newsletter CTA Test; Retargeting Banner Offer Test; Loyalty Partner Offer Test; Social Post CTA Test (Raw Data))
  • Market Emergence: Agent-Based Simulator — Simulate how micro-level customer motives, social proof, and word-of-mouth cascades produce unexpected macro-level market emergence, segregation, and churn.
  • Gen AI Cost Estimator — Estimate what it actually costs to deploy LLMs in marketing — token pricing, conversation accumulation, model tier trade-offs, and ROI vs. human agents. (Pre-loaded cases: Customer Support Chatbot; Personalized Email Subject Lines; Product Description Generator; Review Sentiment Analyzer; Marketing Analytics Assistant; Ad Copy Variant Generator)
  • ARIMAX Forecasting — Forecast sales over time with external predictors like ad spend. (Pre-loaded cases: Monthly Sales with Advertising Spend; Instagram Health Supplement Shop; Mobile Game Monthly Signups)
  • ARX Forecasting (Intro) — Forecast time series with external predictors. Simplified ARIMAX without moving average terms - runs entirely in browser. (Pre-loaded cases: Ice Cream Sales & Temperature; E-commerce Revenue & Ad Spend; Influencer Daily Engagement; Political Comedy Instagram — Daily Likes)
  • Bandit vs. A/B Race — Race a classic A/B test against adaptive bandit algorithms on the same visitors. See what each one earns during the test and what each one can tell you after it.
  • Zero-Shot BART & Lexical Quant Lab — Score unstructured customer reviews against custom constructs using Zero-Shot NLI (BART-MNLI), uncover distinctive group vocabulary via Fightin' Words Dirichlet log-odds, and run key-driver regressions. (Pre-loaded cases: Canyon Creek Bistro — Multi-Location Yelp Review Triage; VeloSound Pro ANC Earbuds — Firmware v1.4 Defect Attribution; LuminaBotanicals Barrier Serum — Skin-Segment Reaction & Packaging Audit)
  • Bass Diffusion & Innovation Forecasting Lab — Forecast new product adoption curves, model post-takeoff chasm dynamics, evaluate marketing mix shocks, and simulate multi-generation technological substitution.
  • Bayes' Theorem & Screening Lab — Explore prior odds, likelihood ratios, and posterior risk through 1,000-icon natural frequency grids and probability trees.
  • Bivariate Regression (Simple) — Predict an outcome using one variable (e.g., Spend → Sales). (Pre-loaded cases: Paid Social Spend vs. Weekly Revenue; Email Creative Themes vs. Average Order Value; Likert Reaction vs. Brand Favorability; D&D Character Level vs. Gold Earned)
  • Categorization Studio — Transform continuous data into meaningful categories using binning. See how the same data tells different stories. (Pre-loaded cases: Quarterly Campaign Performance Review; Loyalty Program Tier Design; App Re-engagement Campaign Targeting; Customer Segmentation for Targeted Nurture)
  • Chi-Square Test — Check if two categorical variables are related (e.g., Device vs. Signup). (Pre-loaded cases: Holiday Email Creative Test; Channel vs. Purchase Behavior Test; Landing Page Layout Test; Loyalty Nudge Test (Raw Data); D&D Class Survival Test)
  • Chi-Square Goodness-of-Fit — Test whether categorical counts follow an expected uniform or custom benchmark distribution. (Pre-loaded cases: Zenith Beverages: New RTD Flavor Preference Test; Cloud Infrastructure Threat Timing Audit; Leadership Talent Sourcing Representation Audit)
  • Confidence Interval Simulator — Draw repeated samples from known populations to see how often confidence intervals actually capture the true parameter.
  • Classifier Comparison Lab — Compare binary classification algorithms side-by-side on training and holdout data. Select from Logistic Regression, Decision Tree, k-NN, Naive Bayes, and Linear SVM. (Pre-loaded cases: CloudMetrics SaaS — Customer Churn Prediction; NexaFlow — B2B Lead Conversion Scoring; PixelReach Ads — Click Prediction for Programmatic Bidding)
  • Basic Customer Lifetime Value — A gentler way into CLV: one marketing lever, two models, no segments or simulators. Learn what a customer is worth and why a quiet month is not always a lost customer.
  • Customer Lifetime Value (CLV) Modeling — Model customer retention and conditional spend to forecast CLV by segment. Identify high-value at-risk customers, evaluate marketing levers, and benchmark acquisition spend via CLV/CAC analysis.
  • Collage Engine — Visual association board for focus groups. Participants select images that "feel like" a brand or concept, building a collective mosaic that reveals shared perceptions.
  • Probability Calculator — Calculate likelihood of repeated events (Binomial/Poisson).
  • Conjoint Analysis & Market Simulation — Discover what customers truly value. Design choice-based experiments and simulate market share. (Pre-loaded cases: Smartphone Purchase Decisions: Features, Brand, and Price; Streaming Service Launch: Content, Features, and Pricing; Course Design Preferences: Format, Workload, and Assessment; Designing the Marlowe Riptide Season Membership)
  • Basic Conjoint Analysis — Simplified conjoint analysis for categorical attributes. Great for learning core conjoint concepts. (Pre-loaded cases: Basecamp Burrito — Campus Combo Meal Optimization; StreamWave — Subscription Tier & Walk-Away ("Would Not Buy") Study; SoundPulse — Wireless Earbud Lineup & "Keep Existing Headphones" Opt-Out)
  • Conjoint Study Designer — Design your own conjoint study with custom attributes and levels. (Pre-loaded cases: Smartphone Purchase Decisions: Features, Brand, and Price; Streaming Service Launch: Content, Features, and Pricing; Course Design Preferences: Format, Workload, and Assessment; Designing the Marlowe Riptide Season Membership)
  • Conjoint Experiment Simulator — Take a real choice-based conjoint study, then analyse the data it produces with your own answers inside it.
  • Count Data Regression Lab — Model non-negative integer customer counts (orders, tickets, visits) using Poisson GLM, Negative Binomial (NB2), Two-Part Hurdle Count, and Exposure Offsets. (Pre-loaded cases: Crave Food Delivery: Does the Agency's Push-Alert Cadence Really Lift 90-Day Orders?; Mosaic Analytics: Does Guided Onboarding Really Cause More Support Tickets?; Copperpaw Pet Supply: Does a $25 Acquisition Coupon Build Repeat Kibble Buyers?; Thistledown Market: Why Did the Merchandising Spreadsheet Predict −1.8 Bakery Trips?)
  • CRM Sandbox — A working B2B sales CRM for a team of four reps. Diagnose the pipeline, tell a real performance gap from luck, triage a rep's week, and build a coaching plan the records support.
  • Data Visualization Studio — Explore bivariate relationships through 8 chart types with rich narratives that teach what each chart reveals and hides. (Pre-loaded cases: Peak Performance Outdoor Retail — Weekly Revenue by Region; BrightPath Digital — Ad Spend vs. Conversions; Harbor Market — Customer Satisfaction Across Product Lines; TechFlow SaaS — Which Lead Sources Buy the Best Deals?; Nexus Arena — Competitive Esports Player Analytics; Meridian Insights — Brand Health Tracker; Luminary Electronics — Product Launch Lifecycle; Vortex Games — eSports Analytics Deep Dive)
  • Decision Tree Classifier — Build and interpret classification trees to segment customers and predict outcomes. (Pre-loaded cases: Customer Churn Prediction - FitLife Gym; B2B Lead Scoring - CloudSync SaaS; Customer Segment Classification - Artisan Coffee Co)
  • Difference-in-Differences — Estimate whether a campaign, launch or price change really moved results, by comparing the markets that got it with similar markets that didn't. (Pre-loaded cases: The Textbook App Launch: In-Store Mobile App Rollout; Hearthstone Coffee: Regional TV Campaign Flight; Pinnacle Fitness: Member App Rollout & The Selection Trap; Oakline Furniture: Showroom Remodels & The Transition Dip)
  • Continuous Distribution Calculator — Interactive probability and critical value calculator for Normal (Z), Student t, Chi-Square, and F distributions.
  • Dynamic Logistic Regression — Forecast a yes/no outcome over time from what happened last period and planned drivers, tested walk-forward against a same-as-yesterday baseline. (Pre-loaded cases: Is a Third Delivery Worth $180 a Week? Kestrel Market; Do Sell-Outs Come in Streaks? Ferngrove Fresh; Should We Stop the Flash Sales? Brightwick Candles; One Model, Two Suppliers? Kestrel Market)
  • Forecast Starter — Your first time series tool. See what a series is made of, extend it with a rule you can say out loud, then find out whether that rule beats doing nothing at all. (Pre-loaded cases: Paid Search Cost-Per-Click; SaaS Active Subscribers; Garden Centre Units Sold; DTC Apparel Revenue with Promotions)
  • Huff Gravity Model Simulator — Simulate retail trade areas using the Huff model. Place stores on a map, adjust attractiveness and distance-decay parameters, and watch market-share boundaries shift in real time. (Pre-loaded cases: Brewed Awakening — Coffee Shop Wars; FreshMart vs. MegaGrocer — Grocery Trade Areas; Where Should BankFirst Open? — New Branch Siting; SDSU College Area — Campus Coffee Battle)
  • The Hundred-Heads Test — Twenty-three years of psychic-spying research, a stranger with a weighted coin, and your own A/B test. Learn why an impressive p-value can still mean nothing.
  • Data Import Wizard — Upload a messy CSV/Excel file, see exactly what each tool expects, fix common formatting problems with guided one-click fixes, and export a clean dataset. Teaches tidy-data habits as you go.
  • Independent t-test (Welch's) — Compare averages of two independent groups (e.g., Control vs. Treatment). (Pre-loaded cases: Holiday Subject Line Lift; Loyalty Upsell Experiment; Refer-a-Friend Revenue Pilot; Streaming Hours Cohort Experiment; Warrior vs Rogue Damage Output)
  • K-Means Clustering — Segment customers into distinct groups based on behavior. (Pre-loaded cases: Customer RFM Segmentation; Email Campaign Performance; Product Portfolio Positioning; Snackbar Attitudes & Preferences; Marlowe Riptide — Who Are Our Fans, Really?)
  • k-Prototypes Clustering — Cluster mixed attributes (Numbers + Categories) like Spend + Region. (Pre-loaded cases: Customer Segmentation with Mixed Attributes; Product Portfolio Analysis; Lead Scoring Clusters)
  • Latent Class Segmentation — Probabilistic customer segmentation using LCA/LPA. Discover hidden segments with soft class membership. (Pre-loaded cases: Customer Preference Survey; Media Consumption Patterns; Purchase Behavior Profiles)
  • Logistic Regression — Predict binary outcomes like Churn vs. Retain. (Pre-loaded cases: Influencer Posts: What Makes a Story Drive a Sale?; B2B Email Sequences: Predicting Demo Bookings; Digital Promo A/B Test: High Incentive vs Standard Offer; Subscription Churn: Predicting Customer Attrition; Event Marketing: Predicting Registration Conversion; RFM-Based Purchase Prediction: Who Buys Next?; SignalPath MarTech: Which Leads Actually Close?; Will They Come Back? Predicting Season-Membership Renewal)
  • Log-Loss Classification Lab — Manually calibrate logistic regression by minimizing log-loss. (Pre-loaded cases: Email Campaign Conversion - StyleBox Fashion; Customer Churn Analysis - StreamFlix; Display Ad Click-Through - TechDeals; Premium Upgrade Prediction - CloudTools SaaS; Luxury EV Test Drive Booking - PeakAuto)
  • MAE Calibration Lab — Manually fit linear & quadratic models by minimizing error. (Pre-loaded cases: Search Ad Campaign ROI - TechGear Direct; Email Frequency Optimization - StyleBox Subscription; Influencer Campaign Performance - GlowUp Cosmetics; Industrial 3D Printer Sales - PrintForge Solutions)
  • Magnetic Poetry Board — Collaborative word-tile board for focus groups. Participants build evocative phrases from themed word packs to surface associations that direct questions never reach.
  • Market Basket Analysis — Find which products are bought together, and tell a real pattern from a coincidence, before you plan a bundle or a cross-sell. Confidence flatters popular items and lift flatters rare ones.
  • Marketing Mix Modeling Lab — Estimate what each marketing channel really contributes to sales, including carryover and diminishing returns, and find a better way to split the same budget. (Pre-loaded cases: The Saturated Cash Cow: Brightwell Pet Food; The Seasonality Trap: Tandem Bikes; The Influencer Collinearity Dilemma: Lumen Beauty; Triangulating National MMM with Regional Tests: Hearthstone Coffee)
  • Markov Chain Attribution — Sequence-based attribution. Analyze how the order of touches acts as a bridge to conversion. (Pre-loaded cases: Case F: GlowRoute Summer Glow — Auditing a Last-Click Dashboard)
  • MaxDiff (Best-Worst Scaling) Analyzer — Rank messages, features or benefits on a ratio scale that sums to 100. Forces trade-offs where rating scales let everything look important. (Pre-loaded cases: Which Benefits Actually Attract Talent? — CivicPulse; Which Message Leads the Campaign? — Harborline Bank; Prioritising the Backpack Roadmap — SummitGear)
  • McNemar Test — Analyze "switchers" in paired yes/no data (e.g., before/after purchase). (Pre-loaded cases: Streaming Creative Test: Emotional Trailer Swap; Flagship Retail Signage: Price Messaging at Store Level; B2B Nurture Tracks: Paired Tutorial vs. Executive Story; Lifecycle Push Pilot: Paired Observations (Raw CSV Mode))
  • Positioning Analysis Lab — Visualize brand positioning using multidimensional scaling. Map competitive landscapes. (Pre-loaded cases: Repositioning in a Crowded Beer Market; Smartphone Portfolio Strategy at a National Carrier; Streaming Platform Entry Strategy; Fast-Casual Concept Positioning Before a 40-Unit Expansion; Athletic Shoe Brand Campaign — Claiming Defensible Territory)
  • Mediation Visualization — Estimate and visualize direct, indirect, and total effects in mediation, moderated mediation, parallel, and serial models. (Pre-loaded cases: Full Mediation: Digital Advertising Campaign; Partial Mediation: Premium Pricing Study; Serial Mediation: The Funnel, Taken Literally; Moderated Mediation: Influencer Marketing × Age; Suppression Effect: Celebrity Endorsement Paradox; Parallel Mediation: Service Quality Paths to Loyalty; Social Proof: How Reviews Drive E-Commerce Purchases; Moderated Mediation: Sales Training ROI × Tenure; Parallel Mediation: Mobile App UX → Retention)
  • Missing Data Lab — Inject MCAR, MAR, and MNAR missingness into marketing datasets, apply four imputation treatments, and see exactly how each method distorts your regression slopes and chi-square results.
  • MLR with Interactions — Model non-linear effects and interaction terms. (Pre-loaded cases: Ad Spend × Seasonality Interaction; Email Frequency Quadratic Effect; Price × Quality Interaction; D&D: Level × Class Interaction)
  • Model Drift Lab — Watch a prediction model degrade over time as customer data shifts. Diagnose feature drift with PSI and KS statistics, retrain to recover, and learn when to set monitoring alerts. (Pre-loaded cases: BrightCart — E-Commerce Customer Churn; AdVantage — Digital Ad Click-Through Rate; FreshBrew — Coffee Subscription Retention; MediaMax — B2B SaaS Lead Scoring)
  • Multinomial Logit — Predict choice among 3+ options (e.g., Brand A vs B vs C). (Pre-loaded cases: Marketing Funnel: Channel, Frequency, and Customer Journey Stage; Brand Choice: Price Sensitivity, Demographics, and Channel Preference; Esports Engagement: Platform, Membership, and Player Behavior; Ideal Convergence: Single Continuous Predictor)
  • Multiple Regression — Predict a numeric outcome using multiple drivers. (Pre-loaded cases: EDM Events: Lead Time, Ticket Type, and Revenue Per Patron; Customer Valuation: RFM Metrics and Segment Labels)
  • Neural Network Playground — Build and visualize neural networks to solve complex marketing classification problems.
  • Neural Network Lab — Train a neural network on your customer file, watch it learn, and find out on customers it never saw whether it beats a regression you can explain. (Pre-loaded cases: Is the AI Worth It for the Save Offer? Ridgeline Roasters; Which 40,000 Get the Catalog? Copperline Outfitters; A Big Network on a Small Table: Tallyhop; Who Should See Premium 4K First? Kinoscope; Lift the Cap to $60k? Wrenfield Mattress)
  • Rank-Based Tests — Compare groups by rank when the average is not a safe summary: skewed spend, ratings, or one extreme customer. Covers Mann-Whitney, Wilcoxon signed-rank and Kruskal-Wallis. (Pre-loaded cases: The whale order; Store layout satisfaction; App redesign, same users; Time to first purchase by channel)
  • Odds-O-Matic — Calculate and visualize probability, odds, odds ratios, and relative risk for two customer segments with meeple pictographs. (Pre-loaded cases: Airline Loyalty: Miles Loan Uptake; Email Campaign: Personalized vs. Generic Subject Lines; Retail: Loyalty Members vs. Non-Members; SaaS: Demo vs. No-Demo Conversion; Social Ads: Video vs. Static Image CTR)
  • One-Sample Hypothesis Test — Test whether a single sample mean or conversion rate meets a business standard, target SLA, or industry benchmark. (Pre-loaded cases: Aura Athletics DTC Ad Conversion Benchmark; SaaS Enterprise Customer Acquisition Cost (CAC) Audit; Metro Delivery Speed Guarantee Audit; Merchant Dispute Rate Regulatory Compliance)
  • One-Way ANOVA — Compare averages across 3+ groups (e.g., 4 different landing pages). (Pre-loaded cases: Streaming Launch: Creative Concepts and Revenue Per Thousand Households; Flagship Retail Promotions: Pricing Stories and Gross Margin; B2B Nurture Tracks: Product Tutorials vs. Executive ROI Stories; Lifecycle Messaging Experiment (Raw Observations))
  • Overfitting Explorer — Explore bias-variance tradeoff through polynomial regression. Compare training vs. holdout performance across marketing scenarios. (Pre-loaded cases: Social Media Ad Campaign Performance; Price Elasticity Study; Email Campaign Optimization)
  • Oversampling Calibration Lab — Discover how oversampling the rare class during model training inflates predicted probabilities, and apply the King-Zeng correction to restore calibration.
  • Paid Search Sandbox — A Google Ads account nobody has ever audited. 300 keywords, a live auction you can move, and a budget to defend. Find the waste, tell a rank problem from a budget problem, and commit to a plan.
  • Paired t-test — Compare before/after scores for the same people (e.g., Pre vs. Post). (Pre-loaded cases: Global Streaming Launch Creative Test; Retail Promotions Pricing Test; B2B Nurture Content Tracks)
  • Panel Dynamic Logistic Regression — Separate habit from loyalty in customers' repeat purchases with pooled and random-effects dynamic logits, and price a free trial. (Pre-loaded cases: Will a Free Bottle Start a Habit? Thistledown Market; Do $5 Credits Bring Lapsed Customers Back for Good? Crave; Does the VIP Coupon Really Triple Purchases? Fernhill Pet Co.; Habit or Loyalty, or Neither? Saltmarsh Books)
  • Pearson Correlation — Check if two continuous metrics move together (e.g., ad spend vs. revenue). (Pre-loaded cases: Paid Social Spend vs Trial Signups; Awareness vs Consideration – Q3 Brand Tracker; Net Promoter Score vs Loyalty Retention; Full-Funnel KPI Matrix – Finding the Leak; Email Send Volume vs Revenue – and the Third Column)
  • Planogram Lab — Merchandise a 2D retail shelf or cooler with physical facings, stacking, and depth capacity, diagnose stockout censoring and zone endogeneity in 52 weeks of POS history, and test your plan against four benchmark plans in a 13-week quarter simulation. (Pre-loaded cases: Cooler Wars — Quad Mart at Halbrook State University; Aisle 9 — Copperpaw Pet Supply (6-Ft Dry Dog Food Gondola))
  • Praxis Sim — Competitive marketing business simulation. Teams make pricing, advertising, distribution, and R&D decisions against AI-controlled competitors in a shared market.
  • Price Optimization & Pricing Strategy Sandbox — Estimate demand curves, find revenue- and profit-maximizing prices, and compare pricing strategies side-by-side. Upload your own data or explore built-in scenarios. (Pre-loaded cases: Bean & Brew: Premium Coffee Subscription Box; TaskFlow Pro: B2B Project Management SaaS; SkyWest Regional: LAX–SFO Route Optimization; Urban Threads: Seasonal Discount Strategy; IronCore Fitness: Monthly Membership Pricing; StreamWave: Premium Streaming Service Launch; Alder & Vine Spa: Pricing Experiments via Daily Deals; HarvestTable: Premium Condiment Shelf-Price Test; Marlowe Riptide: What's the Right Single-Game Ticket Price?; AdArchy: A Card Game Born of Spite)
  • Programmatic Ad Auction Simulator — Step inside the 100ms real-time bidding auction: see Quality Scores, Ad Rank math, DSPs and SSPs, and why programmatic outperforms legacy media buying.
  • Propensity Score Matching — Match treatment and control groups to estimate causal effects. (Pre-loaded cases: Loyalty Program Enrollment Effect on Annual Spend; Email Marketing Campaign Impact on Conversion; Display Ad Exposure Effect on Brand Awareness; Influencer Content Exposure Effect on Purchase Conversion; Free Trial Activation Effect on Paid Subscription; Premium Coupon Effect on Purchase Conversion; Brand Event Attendance Effect on Post-Event Spend; App Feature Adoption Effect on 30-Day Retention)
  • Qualitative Analyzer — Manually code and analyze interview transcripts or focus groups.
  • Regression Residual Diagnostics — Audit the LINE assumptions of a simple or multiple regression (numeric and categorical predictors) with Residuals vs. Fitted, Normal Q-Q, Scale-Location, Cook's Distance and residuals-by-predictor plots, on a case study or your own CSV. (Pre-loaded cases: Ad spend and sales revenue; Client size and B2B deal value; Influencer reach and orders; Weekly ad spend and sales; Store sales by ad spend and region; B2B deal value by headcount, industry and renewal)
  • Marketing Resource Optimizer — Allocate limited budgets or resources across channels to maximize ROI.
  • Response Model Lab — Name the response shape, pick a form, then calibrate it by hand against MAE or MSE.
  • Retention & Survival Curves — See how long customers stay, which sign-up cohorts hold on best, and how honest your churn rate really is. Cohort tables and Kaplan-Meier curves that count the customers who have not left yet.
  • A/B Sample Size Planner — Plan n for comparing two groups.
  • Sample Size Calculator (Regression) — Plan n for correlation/regression.
  • Sample Size Calculator (Single) — Plan n for one mean or proportion.
  • Multi-Arm A/B Calculator — Plan n for A/B/C/D tests.
  • Sampling Visualizer — See how different sampling methods work.
  • Scale Reliability & Factor Analysis — Check whether survey items or behavioral telemetry hang together, find the dimensions underneath them, and build scale scores you can defend.
  • Selection Probability Lab — Explore selection probabilities.
  • Sentiment Lab — Analyze customer emotion using VADER polarity and the NRC-VAD lexicon. Score text on valence, arousal, and dominance, and map sentiments onto Russell's circumplex model. (Pre-loaded cases: Reddit Posts: University Enrollment System Feedback; Influencer Swimwear Brand: Customer Review Analysis; Water Bottle Brand Competitive Intelligence)
  • Shapley Value Attribution — Cooperative Game Theory. Assign fair credit to channels based on their marginal contribution. (Pre-loaded cases: Case F: GlowRoute Summer Glow — Auditing a Last-Click Dashboard)
  • Survey Data Quality & Scrubbing Lab — Audit raw Qualtrics exports for speeders, contextual matrix straightlining, failed attention-check traps, reverse-worded contradictions, and open-text gibberish, while checking whether scrubbing distorts sample demographics. (Pre-loaded cases: Bramblecup Nitro Cold Brew Campus Survey; GlowRoute Skincare Viral Giveaway Survey; SignalPath B2B Customer Advisory Panel)
  • Sales Territory Optimizer — Optimize sales territory assignments using geospatial analysis and workload balancing.
  • Theme Extractor — Automatically discover hidden topics in reviews using AI. (Pre-loaded cases: Instagram Influencer Comment Analysis; Streaming Service: Subscriber Feedback Analysis; Premium Coffee Brand: Customer Reviews Analysis)
  • Classification Threshold Lab — Interactively explore how the classification threshold affects profit vs. cost trade-offs in binary ML models. Find the optimal T* that maximises net profit. (Pre-loaded cases: FreshBox — DTC Snack Subscription; StreamFlow — Streaming Service; Kiteline Analytics — Enterprise-Tier B2B Analytics SaaS)
  • Tiered Demand Curve Estimator — Estimate per-tier demand curves and the full own/cross-price elasticity matrix when you sell multiple vertically-differentiated products (e.g., Basic / Premium / Elite) to one market. Uses Berry (1994) aggregate logit inversion with optional 2SLS for endogeneity. (Pre-loaded cases: Nightjar: Weekly Plan Sign-ups After Free Trials; Regional Gym Chain: Weekly Memberships Priced From Costs; Business Software Company: Monthly Trial Conversions)
  • Transformation Lab — Read your residuals, find out whether re-scaling a variable is actually warranted, compare the candidates honestly, and leave with the formula for Excel, Sheets, Python, R, SPSS or Stata. (Pre-loaded cases: Two Years of Weekly Paid Search; Where Does Reach Stop Paying?; What Does a 10% Price Cut Actually Buy?; The Control Case; The Slide Looks Different. The Model Does Not.)
  • TURF Analysis — Pick the set of flavors, channels or messages that reaches the most people, not the set with the highest individual scores. The three most popular items often share one audience. (Pre-loaded cases: Grocery Freezer Slotting — Frost & Fold Creamery; National Acquisition Messaging — Harborline Bank; Add-On Channel Bundle & Contract Constraint — StreamBox; Employee Benefits Portfolio & Segment Equity — CivicPulse)
  • Understanding Distributions — The discrete and continuous distributions marketing analysts actually use, each built around a real decision: staffing a chat queue, setting a free-shipping threshold, judging whether an A/B result is worth shipping.
  • Understanding Regression — Ten short demos: where R² comes from, correlation as a slope, reference categories, interactions, ANOVA, omitted variables, and multicollinearity.
  • Univariate Analyzer — Explore a single variable's distribution, outliers, and summary stats. Always start here. (Pre-loaded cases: Influencer Campaign Performance Analysis; Customer Satisfaction Survey Analysis; E-commerce Transaction Analysis; Email Marketing Campaign Analysis; Customer Lifetime Value Analysis; Social Media Post Analytics; A/B Test Results — Messy Data Challenge!; PetPost Subscriber Health Analysis)
  • UTM Campaign Tracking Lab — Tag a campaign, run it for 30 days, and see how much of your dashboard was telling the truth.
  • Web Analytics Sandbox — A safe GA4-style analytics account with two years of real traffic. Read the reports, segment the data, diagnose the funnel, and find the measurement problems hiding in it.

Interactive Textbook Modules (Public Syllabi & Section Outlines)

  • A/B Testing & Marketing Experiments (55 min, 15 sections) — Design, run, and interpret randomized experiments — the marketer's most powerful causal tool. Master proportion tests, multi-variant designs, sequential testing, multi-armed bandits, and Bayesian methods used by the world's leading experimentation platforms.
  • B2B Market Research (57 min, 10 sections) — Master the unique challenges of business-to-business research: DMU mapping, expert interviews, win/loss analysis, small-universe survey design, B2B segmentation, value-in-use pricing, digital B2B data sources, and GenAI applications.
  • Biometric Research in Marketing (50 min, 9 sections) —
  • Communicating with Data (95 min, 15 sections) —
  • Conjoint Analysis & Choice Modeling (54 min, 9 sections) —
  • Continuous Distributions & The Normal Model (22 min, 9 sections) — Understand the smooth curves governing business metrics. Master Probability Density Functions, standard normal Z-scores, the Empirical Rule, Student's t-distribution, and heavy-tailed lognormal revenue models — with practical applications in search advertising and customer valuation.
  • Cross-Cultural & International Marketing Research (65 min, 12 sections) — Navigate the hard problems of multi-country research: emic vs. etic constructs, survey translation beyond GenAI, cultural response styles, hidden contextual variables (device, platform, privacy norms), cross-cultural qualitative methods, and designing multi-country research programs that produce genuinely comparable data.
  • Customer Analytics: The Metrics That Drive Growth (45 min, 12 sections) — A practical survey of the KPIs marketers use to acquire, retain, and grow customers — from CAC and churn to NPS and cohort analysis. Learn to calculate each metric, interpret it in context, and avoid the single-metric trap.
  • Customer Valuation: From RFM Scoring to Lifetime Value (56 min, 11 sections) — Progress from simple descriptive metrics through RFM scoring to full CLV modeling. Learn when each approach is 'good enough' and when to upgrade — using real tools at every step.
  • Data-Driven Positioning (55 min, 11 sections) — Learn to build and interpret perceptual maps using Multidimensional Scaling, uncover the attributes that drive consumer choice with Conjoint Analysis, and translate data into positioning strategies your brand can own.
  • Data-Driven Segmentation (65 min, 12 sections) — Move beyond intuition-based groupings to data-driven market segmentation. Compare K-Means, K-Prototypes, and Latent Class methods on real marketing data — and learn to profile, evaluate, and present segments that your team can actually act on.
  • Data-Driven Targeting (55 min, 11 sections) — Move from 'who are our segments?' to 'which customers should we act on?' Build classification models that predict individual-level outcomes, evaluate them with confusion matrices and cost-sensitive metrics, and set decision thresholds that maximize business value — not just accuracy.
  • Data Exploration for Marketing Analytics (55 min, 11 sections) — Before you model, you explore. This module builds the habits and intuitions that separate analysts who get lucky from those who consistently find insight — using real marketing data and professional tools.
  • Data Preparation and Basic Analysis (44 min, 8 sections) — From raw survey data to findings: data cleaning (speeders, bots, straightliners), coding, missing data, descriptive statistics, cross-tabulation, hypothesis testing, correlation, regression, and AI-powered analysis — with a full case application.
  • Defining the Outcome (45 min, 14 sections) — Every technique for validating a model assumes the outcome is already correctly defined — which makes the definition the one decision no diagnostic can check. Learn why a label is a choice rather than a fact, what changes between contractual and non-contractual settings, how censoring and involuntary events distort a target, why a successful intervention corrupts its own label, and why the quantity worth predicting is often the effect of acting rather than the outcome itself.
  • Digital & Social Analytics: Measuring Performance Across Channels (69 min, 12 sections) — A practical guide to the metrics that matter across web, search, social, email, and paid media. Learn to calculate, interpret, and connect channel-level KPIs — and avoid the vanity-metric trap.
  • Discrete Distributions & Expected Value (26 min, 10 sections) — Model count-based business phenomena with precision. Master discrete random variables, Expected Value, Binomial conversion models, and Poisson arrival queues — while diagnosing when real customer behavior violates theoretical independence.
  • Experimental Design & Causality (38 min, 9 sections) — Correlation vs. causation, experimental anatomy (IV/DV/control), factorial designs, threats to internal and external validity, A/B testing, quasi-experiments, and how AI is transforming experimentation in 2026.
  • Feature Engineering (50 min, 14 sections) — Two analysts, the same data, the same algorithm — and a 13-point gap in accuracy. The difference was what they showed the model. Learn to turn a raw behavioural export into a modeling table on purpose: collapsing event logs to the customer grain, applying the recency-frequency-intensity template to every behaviour, encoding categories without leaking the outcome, and enforcing a temporal line that no feature may cross.
  • Forecasting & Time Series (55 min, 11 sections) — Learn what makes time series data fundamentally different, see the components hiding inside any series, check whether those components are stable enough to forecast, and build models that combine a series' own history with external business drivers. Progress from simple baselines through autoregression to ARX and ARIMAX.
  • Introduction to Marketing Analytics (50 min, 13 sections) — The Chapter 1 for marketing analytics: what the field is, where data comes from, how to think about variables and measurement, the four types of analytics, the methods landscape, and the critical-thinking habits that separate signal from noise.
  • Marketing Attribution & Budget Optimization (50 min, 11 sections) — Learn why giving the right marketing channel credit for a sale is one of the hardest — and highest-stakes — problems in analytics. Progress from simple rule-based models through Shapley Values and Markov Chains, then translate attribution insights into budget optimization decisions using the Resource Allocation tool.
  • The Marketing Data Landscape (52 min, 10 sections) — A unified taxonomy of marketing data — behavioral vs. attitudinal, the mindset-behavior divide, constructs vs. proxies, the modern data stack, measurement levels, and data structures. The conceptual foundation for everything that follows.
  • Marketing Research in the Era of Big Data and AI (42 min, 10 sections) — Why primary research design matters more than ever: big data tells you what happened, AI predicts what's next, but neither can tell you why or what questions to ask. How the discipline has transformed, the integration model, and what the modern researcher role looks like.
  • Ethics in Marketing Research (45 min, 9 sections) — The academic-corporate ethics gap, informed consent in the digital age, surveillance capitalism, landmark case studies (Facebook, Microsoft Tay, Cambridge Analytica, Clearview AI), AI-specific ethical challenges, and building an ethical research framework.
  • The Marketing Research Process (65 min, 12 sections) — Learn the seven-step research process that turns business uncertainty into actionable insight. Covers problem definition, research design types (exploratory, descriptive, causal), primary vs. secondary data, and ethics — with integrated discussion of how generative AI is transforming (and complicating) each step in 2026.
  • Marketing Response Models (45 min, 12 sections) — Turn "these two things are related" into an equation with numbers in it. Learn the eight shapes a marketing response can take, the three math models that cover most of them, and how to calibrate parameters by hand against a loss function — the manual version of what regression will later automate for you.
  • Model Comparison and Judgment (55 min, 14 sections) — Three models all pass the holdout test. Now which one ships? Accuracy collapses on rare outcomes, AUC compares rankings while precision–recall describes the campaign, and a model can rank beautifully while lying about magnitudes. Learn to compute the baseline first, compare fairly, check calibration before multiplying probabilities by money, and choose the metric from the decision.
  • Neural Network Basics for Marketers (65 min, 14 sections) — Every marketing platform now sells 'AI-powered' predictions, and many of them are neural networks. Learn what a network actually computes (a stack of tiny logistic regressions), how it learns by walking downhill, how to tell when it has memorized a small customer file and stop it in time, how to test honestly whether it beats a regression you could explain, what permutation importance and partial dependence do and do not tell you, and why no model should be trusted beyond the data it saw.
  • One-Sample Estimation & Hypothesis Testing (22 min, 10 sections) — Bridge the gap from sample metrics to population truth. Master Central Limit Theorem mechanics, frequentist confidence intervals, one-sample z and t tests, and effect sizes — while diagnosing why Big Data collapses p-values into statistical illusions.
  • Prediction Fundamentals (65 min, 14 sections) — You can fit a model to data you already have. That is not the same as predicting data you have never seen — and the difference is where most marketing models quietly fail. Learn the holdout discipline, how to spot data leakage, why complexity stops helping, how regression automates hand-calibration outright, and why a coefficient misleads when a driver of the outcome is left out.
  • Pricing Analytics (59 min, 12 sections) — Master quantitative pricing methods: demand curves, price elasticity, Van Westendorp PSM, Gabor-Granger, conjoint-based WTP, price optimization models, pricing experiments, and behavioral pricing effects.
  • Probability Foundations, Decision Trees & Risk (35 min, 11 sections) — Master the quantitative logic of uncertainty. Learn marginal, joint, and conditional probability, Bayes' Theorem, and Expected Monetary Value — while building the reflexes to spot base-rate fallacies in automated algorithms and generative AI tools.
  • Product & Retail Analytics: Measuring What Sells (50 min, 12 sections) — A practical guide to the metrics that separate profitable products from shelf-space squatters — from margin mix and SKU velocity to pricing elasticity and promotional lift. Learn to calculate each metric, interpret it in retail context, and avoid the margin-share trap.
  • Qualitative Research Methods (55 min, 10 sections) — Focus groups, depth interviews, ethnography, netnography, online qualitative platforms, qualitative coding and thematic analysis, mixed-method workflows, and quality standards — with GenAI's role in each.
  • Sales Analytics: From Pipeline to Revenue (54 min, 12 sections) — A practical guide to the metrics that drive B2B and professional selling — from pipeline coverage and win rates to lead scoring and forecast accuracy. Learn to calculate each metric, interpret it in sales context, and avoid the activity-outcome trap.
  • Sampling & Data Collection (39 min, 9 sections) — Probability vs. non-probability sampling, sample size determination, non-response bias, data collection methods, AI-era sampling challenges, data quality screening, and ethical considerations in sampling.
  • Scaling & Advanced Measurement (50 min, 9 sections) —
  • Secondary Research & Secondary Data (40 min, 8 sections) — Internal and external secondary data sources, government and syndicated data providers, digital trace data, evaluating source quality, and using GenAI for secondary research.
  • Simple Linear Regression & Residual Diagnostics (26 min, 10 sections) — Master the foundational predictive engine of business analytics. Learn Ordinary Least Squares derivation, slope and intercept interpretation, R-squared, hypothesis testing, and the core LINE residual diagnostic plots — with real applications in omnichannel ad saturation.
  • Statistical Inference for Marketing (58 min, 14 sections) — Learn how to draw reliable conclusions about entire customer bases from the imperfect slice of data you actually have. Master sampling distributions, confidence intervals, hypothesis testing, and the critical-thinking framework that separates rigorous analysis from statistical theater.
  • Survey Design & Measurement (35 min, 9 sections) — Question types, Likert and semantic differential scales, reliability (Cronbach's α), validity, common question biases, questionnaire structure, and how GenAI is reshaping survey design in 2026.