In This Article

  1. Introduction
  2. RFM Analysis
  3. Customer Lifetime Value
  4. Customer Segmentation
  5. Churn Prediction
  6. Business Applications
  7. Conclusion

Introduction

Customer analytics is the practice of collecting, analyzing, and applying customer data to understand customer behavior, predict future actions, and optimize marketing and business decisions. It transforms raw customer data into actionable insights.

RFM Analysis

RFM is a simple but powerful technique for customer segmentation based on purchase behavior.

DimensionDefinitionHigh Value Means
Recency (R)How recently did they buy?More recent = more engaged
Frequency (F)How often do they buy?More frequent = more loyal
Monetary (M)How much do they spend?Higher spend = more valuable

RFM Scoring

Assign scores (1-5) for each dimension, then combine for segments:

  • 555 (Champions): Best customers, buy often, spend big
  • 511 (At-Risk): Were good, haven't bought recently
  • 155 (Big Spenders): High value but not frequent
  • 111 (Lost): Haven't bought in long time

Customer Lifetime Value (CLV)

CLV estimates the total value a customer will bring over their entire relationship with your business.

Simple CLV = Average Order Value × Purchase Frequency × Customer Lifespan

CLV with Margin = (Avg Order × Frequency × Lifespan) × Profit Margin

Why CLV Matters

  • Acquisition decisions: How much to spend to acquire customers
  • Retention investments: Justify retention spending
  • Resource allocation: Focus on high-CLV segments
  • Business valuation: Customer base value

Example

If avg order = ₹2,000, frequency = 4/year, lifespan = 5 years, margin = 30%:

CLV = ₹2,000 × 4 × 5 × 0.30 = ₹12,000

You could justify spending up to ₹12,000 to acquire this customer.

Customer Segmentation

Segmentation Bases

  • Demographic: Age, gender, income, education
  • Geographic: Location, region, urban/rural
  • Behavioral: Purchase patterns, usage, loyalty
  • Psychographic: Lifestyle, values, interests
  • Value-based: CLV, profitability

Techniques

  • K-means clustering: Group similar customers
  • RFM segmentation: Behavioral segments
  • Persona development: Qualitative profiles

Churn Prediction

Predicting which customers are likely to leave allows proactive retention efforts.

Churn Indicators

  • Decreasing purchase frequency
  • Lower order values
  • Reduced engagement (opens, clicks, visits)
  • Support complaints
  • Not responding to offers

Prediction Techniques

  • Logistic regression
  • Decision trees
  • Random forests
  • Survival analysis

Business Applications

  • Personalized marketing: Right message to right customer
  • Retention programs: Target at-risk customers
  • Cross-sell/upsell: Recommend based on behavior
  • Loyalty programs: Design rewards based on value
  • Pricing optimization: Segment-specific pricing
  • Channel optimization: Right channel for each segment

Conclusion

Key Takeaways

  • RFM: Simple, powerful segmentation by Recency, Frequency, Monetary
  • CLV: Total value of customer relationship—guides acquisition and retention
  • Segmentation: Group customers for targeted strategies
  • Churn prediction: Identify at-risk customers before they leave
  • Customer analytics enables data-driven marketing decisions
  • Focus resources on high-value customers
  • Combine with clustering techniques for deeper insights