campaign-analytics

Multi-touch attribution quantifies channel contributions by applying first, last, linear, time-decay, and position-based models to ordered touchpoint journeys. You supply campaign data with conversion status and revenue, then run the attribution_analyzer.py script to generate comparative attribution outputs.

Funnel analysis and ROI calculations require campaign journey data containing ordered touchpoints, conversion status, and revenue. Once formatted as a campaigns JSON file, the scripts process this data to perform segment comparisons, benchmark KPIs, and calculate metrics like ROAS, CAC, CPA, and CPL.

Yes, you can compare attribution models and benchmark ROAS without external Python packages. The campaign analytics scripts rely exclusively on Python's standard library to calculate ROI, ROAS, CAC, CPA, and CPL, then benchmark results against KPIs across channels, devices, and segments.

The best way to analyze ad performance and optimize budget allocation is by applying multi-touch attribution and ROI calculations to multi-channel campaign data. This reveals true channel contributions to revenue, enabling smarter, reproducible budget shifts across devices and segments.

Yes, multi-touch attribution works for segment comparison in multi-channel campaigns. The framework analyzes ad performance across channels, devices, and segments, comparing attribution models and benchmarking funnel KPIs to reveal how specific audience segments contribute to overall revenue.