MktPraxis
Interactive Textbook Module Syllabus

Data-Driven Segmentation

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.

⏱️ ~65 min reading & interactives 📑 12 sections 🎓 Level: Advanced undergraduate 📐 Math: Formulas shown 📚 Courses: Marketing Analytics, Predictive Analytics & ML 🏷️ Topics: Segmentation & clustering
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.
Open in Textbook Reader (Sign In) Request Free Instructor Preview

Chapter Section Outline (12 Sections)

Topics and managerial frameworks covered in Data-Driven Segmentation:

  1. 1. Why Segment?
  2. 2. Segmentation Bases: Where the Data Comes From
  3. 3. Geodemographic and Psychographic Classification Systems
  4. 4. Preparing Data for Clustering
  5. 5. K-Means: The Workhorse
  6. 6. Beyond K-Means: Mixed Data and Probabilistic Segments
  7. 7. Choosing K: How Many Segments?
  8. 8. Profiling Your Segments
  9. 9. Evaluating Segment Quality
  10. 10. Segmentation in the Age of AI
  11. 11. Case Application — Ironleaf Nutrition Customer Segmentation
  12. 12. Segmentation in Practice

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:

Elbow & Silhouette Explorer

Students adjust K (number of clusters) via a slider and see both the elbow plot (WCSS) and silhouette scores update in real-time. Uses a pre-generated RFM-style dataset with known cluster structure.

Teaches: there is no single "correct" K — elbow and silhouette often suggest different values, and business judgment fills the gap.

Segment Profile Comparator

Displays a radar (spider) chart comparing 4 pre-built customer segments across 5 standardized variables. Students toggle segments on/off to visually identify what distinguishes each group.

Teaches: segments are defined by relative differences across variables, and visual profiling is essential for naming and communicating segments.

Paired Browser-Based Analytics Calculators (3)

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

👥 K-Means Clustering

Segment customers into distinct groups based on behavior.

Pre-loaded Business Scenarios: 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 Business Scenarios: 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 Business Scenarios: Customer Preference Survey · Media Consumption Patterns · Purchase Behavior Profiles