E-commerceRetentionAnalysis

Deep analysis of customer transaction data to identify retention patterns, segment behaviours, and quantify the impact of first purchase timing on lifetime value.

Impact

Identified 3 distinct customer segments with unique retention patterns
Discovered optimal re-engagement timing windows

The Challenge

E-commerce businesses couldn't predict which customers would return or what drove repeat purchases beyond basic demographics.

The Approach

Applied cohort analysis, RFM segmentation, and survival modeling to transaction data. Analysed purchase frequency, monetary value, and time-between-purchase patterns across segments and geographies.

The Outcome

Revealed that customers making their second purchase within 45 days have 3.2x higher lifetime value. Identified "Champions" segment representing 8% of customers but 35% of revenue.

What I worked on

Data cleaning and cohort structuring
Behavioral segmentation analysis
Statistical modeling and visualisation
Insight synthesis and reporting

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