Finally figuring out which features actually move the needle
Original: A Guide to Measuring Feature Contribution to KPIs

Summary
A guide to using four causal analysis techniques to measure the contribution of product features and marketing initiatives to key business metrics and KPIs. Covers experimental design, quasi-experimental methods, propensity scoring, and causal graphs to establish causality and quantify feature impact.
Who This Is For
Key Takeaways
- Learn to distinguish between correlation and causation when analyzing feature performance
- Master four specific causal analysis techniques: conversion drivers, retention drivers, engagement drivers, and feature funnel analysis
- Understand how to use experimental design, quasi-experimental methods, and propensity scoring to establish true causality
- Build frameworks to measure actual feature impact on KPIs rather than relying on misleading correlations
- Apply causal graphs to map relationships between user actions and business outcomes
Tools & Technologies
Topics Covered
Ready to dive deeper?
Read Full Article on dataanalysis.substack.com