Purpose

Overall ROAS: 1.4 \u2014 The portfolio generates $1.40 in revenue per dollar spent, but only 1 of 11 campaigns exceeds target performance
Best Channel: Influencer (2.54x ROAS) \u2014 Dramatically outperforms social (0.86x), search (1.07x), and media (1.22x) channels
Critical Imbalance: 90.9% of campaigns rated \"Critically Low\" despite positive overall ROAS, indicating heavy reliance on one high-performer (youtube_blogger at 3.77x ROAS)
Cost Efficiency Gap: Influencer achieves lowest CPA ($2,755.58) while social has highest ($5,236.62), reflecting fundamental channel-level performance differences

This section documents the data preprocessing pipeline for the marketing ad spend ROAS efficiency analysis. It shows that all 308 observations (representing campaign-level metrics across 11 campaigns and 4 channels) were retained without any cleaning or filtering, indicating either pristine source data or minimal data quality issues. Understanding preprocessing integrity is critical for validating whether downstream ROAS calculations and channel performance rankings are based on complete, unbiased datasets.

Key Findings

Retention Rate: 100% (308/308 rows) - No observations were removed during preprocessing, suggesting either high initial data quality or absence of outlier/null value handling
Rows Removed: 0 - Zero filtering applied, meaning all campaign records contributed to the final analysis
Train/Test Split: Not applicable - This is a descriptive statistical analysis rather than a predictive modeling exercise, so data partitioning was unnecessary

Interpretation

The perfect retention rate indicates the preprocessing pipeline accepted all observations as valid for ROAS efficiency analysis. This means the 11 campaigns and 4 channel-level aggregations reflect the complete dataset without exclusions. However, the absence of any transformations or outlier treatment raises questions about whether extreme values (e.g., facebook_lal's 0.11 ROAS or youtube_blogger's 3.77 ROAS) were validated or simply