MktPraxis
Interactive Textbook Module Syllabus

Customer Analytics: The Metrics That Drive Growth

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.

⏱️ ~45 min reading & interactives 📑 12 sections 🎓 Level: Core undergraduate 📐 Math: Interpret output 📚 Courses: Sales & CRM Analytics, Marketing Analytics, Digital & Social Marketing 🏷️ Topics: Customer metrics & lifetime value, Descriptive statistics
Full Interactive Chapter (Account Required) The complete chapter reading, embedded live mini-widgets, inline Check-Your-Understanding questions, and progress tracking are available to enrolled students and verified instructors.

Chapter Section Outline (12 Sections)

Topics and managerial frameworks covered in Customer Analytics: The Metrics That Drive Growth:

  1. 1. Why Customer Metrics Matter
  2. 2. Math Foundations: Percentage Change & Rates
  3. 3. Customer Acquisition Metrics
  4. 4. Retention & Loyalty Metrics
  5. 5. Customer Profitability Metrics
  6. 6. Engagement & Activity Metrics
  7. 7. Cohort Analysis: Metrics Over Time
  8. 8. Benchmarking & Competitive Context
  9. 9. Building a Metrics Hierarchy
  10. 10. The Single-Metric Trap
  11. 11. Case Application — PetPost Dashboard Review
  12. 12. Formula Reference Card

Interactive Concept Mini-Widgets Embedded in This Chapter (2)

Students manipulate these interactive visual simulations directly inside the chapter reading before answering gated check-in questions:

Cohort Retention Curve Explorer

Students edit month-on-month retention rates (or pick a preset) and watch a line chart of cohort retention decay. Stat cards report the share surviving at month 12, the biggest single-month drop and when losses settle.

KPI Hierarchy Builder

Students drag metric cards into 4 tiers (North Star, Leading Indicators, Operational Metrics, Vanity/Irrelevant), or click (Enter/Space) a card and then a tier. Contextual feedback on each placement.

Paired Browser-Based Analytics Calculators (5)

This chapter connects directly to these full MktPraxis analytical apps (with pre-loaded business case scenarios, CSV upload, and reproducible Python export):

💎 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.

🎯 Logistic Regression

Predict binary outcomes like Churn vs. Retain.

Pre-loaded Business Scenarios: 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

🔁 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 Business Scenarios: 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

📊 Univariate Analyzer

Explore a single variable's distribution, outliers, and summary stats. Always start here.

Pre-loaded Business Scenarios: 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

⏳ 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.