attribution-modeling

Multi-touch attribution reconciles conflicting platform revenue numbers by distributing order value across touchpoints. This Skill computes first-click, last-click, linear, time-decay, and Markov models to reveal true channel contributions and resolve inflated ROAS claims.

Multi-touch attribution matters because ad platforms claim full credit for conversions, creating conflicting data. By modeling first-party conversion paths, you identify actual channel performance and allocate monthly ad spend accurately instead of relying on skewed platform reports.

Yes, you can apply Markov chain attribution to Shopify, WooCommerce, BigCommerce, or headless stores. The Skill processes your first-party touchpoint data to calculate the Markov model alongside linear and time-decay models for comprehensive per-channel revenue comparison.

To normalize UTMs and validate capture, apply strict formatting rules to your tracking parameters and enforce first-party touchpoint hygiene. This reduces dark or direct traffic, ensuring the multi-touch attribution models receive clean, reliable data for accurate revenue integrity checks.

Last-click attribution assigns 100% of order revenue to the final touchpoint, while time-decay attribution distributes credit across all touchpoints with a 7-day half-life weighting earlier interactions less. Comparing them highlights where standard platform reporting overvalues closing channels.

Run a revenue integrity check by summing the attributed revenue across all channels for each model. The resulting report ensures the total approximates actual order revenue and highlights large discrepancies, providing reliable data for budget allocation conversations.