Purpose

This section documents the data preprocessing pipeline for the retention analysis covering 4,314 customers across 13 cohorts from December 2009 to December 2010. Perfect data retention indicates no rows were removed during cleaning, suggesting either exceptionally clean source data or minimal validation applied. Understanding preprocessing decisions is critical because they directly affect the reliability of retention metrics, survival curves, and revenue calculations used to assess customer lifetime value.

Key Findings

Initial vs. Final Rows: 17,001 rows retained with zero removals—indicates no outliers, duplicates, or invalid records were filtered during preprocessing
Data Retention Rate: 100% retention suggests either pristine data quality or absence of aggressive cleaning protocols
Train/Test Split: Not specified—preprocessing documentation lacks clarity on whether data was partitioned for validation purposes
Transformations Applied: No explicit transformations documented, raising questions about handling of missing values visible in cohort_summary_table (7.7%–92.3% NA rates across retention metrics)

Interpretation

The perfect retention rate contrasts with observable data gaps in the cohort summary, where later cohorts show increasing missing values at t6 and t12 retention periods. This pattern reflects the natural limitation of the observation window (December 2009–December 2010) rather than data quality issues—later cohorts simply lack sufficient follow-