Purpose

This section documents the data preprocessing pipeline for the marketing attribution analysis, showing how raw customer and campaign data was prepared for Shapley Value attribution modeling. Perfect retention indicates no data loss during cleaning, which is critical for ensuring the attribution analysis covers the complete customer base and produces reliable revenue allocation insights across channels and campaigns.

Key Findings

Retention Rate: 100% (2,205 rows preserved) - No observations were removed during data quality checks or mapping processes
Rows Removed: 0 - The dataset required no filtering for missing values, duplicates, or invalid records
Train/Test Split: N/A - Data was processed as a complete cohort rather than partitioned, appropriate for attribution analysis which requires full customer journey visibility

Interpretation

The perfect retention rate indicates exceptionally clean source data with no missing mapped values or quality issues requiring exclusion. This is favorable for attribution modeling, as the Shapley Value algorithm operates on the complete 2,205-customer population, ensuring that revenue attribution across all five campaigns and four channels reflects the true customer base without selection bias. The absence of train/test splits aligns with attribution analysis objectives, which prioritize comprehensive channel-mix evaluation over predictive generalization.

Context

While 100% retention suggests high data quality, this metric alone does not reveal potential issues in feature engineering, outlier handling, or data consistency across the