Customer Analytics: Cohorts, Segmentation, CLV & Churn

Instructor: Alaa Hady · Language: English or Arabic · Suggested duration: 2 days · Level: Intermediate (Level 200)

Objectives

• Engineer standardized customer lifecycle metrics: Acquisition, Activation, Retention, Churn, and Net Revenue Retention (NRR).
• Build dynamic Cohort Retention heatmaps and RFM (Recency, Frequency, Monetary) customer segmentation models in Power BI.
• Estimate Customer Lifetime Value (CLV / LTV) and CAC payback periods.
• Identify early behavioral churn indicators and design measurable retention interventions.

Outline and contents

1. Structuring customer event and subscription/transaction data for lifecycle analysis
2. Defining Customer states rigorously: New, Active, Retained, Reactivated/Resurrected, At-Risk, and Churned
3. Building Vintage Cohort Retention matrices (Customer count retention vs. Net Dollar Retention) in DAX
4. Implementing dynamic RFM (Recency, Frequency, Monetary) scoring and clustering customers into actionable personas (Champions, Loyal, At-Risk, Hibernating)
5. Market Basket & Product Affinity analysis: identifying cross-sell and attach-rate patterns
6. Calculating Customer Lifetime Value (Historical LTV vs. Predictive LTV) and LTV:CAC ratios
7. Churn diagnostics: survival curves, tenure hazards, and linking usage drop-offs to renewal risk
8. Hands-on Lab: Building an end-to-end Customer Growth, Cohort Retention & RFM Segmentation dashboard

Target audience

Marketing analysts, CRM specialists, product analysts, e-commerce managers, commercial operations, and strategy teams.

Expected outcomes

Participants can move beyond aggregate sales totals to analyze underlying customer behavior, isolate high-value segments, quantify churn risk, and guide targeted marketing/retention campaigns.