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Finally figuring out which features actually move the needle

Original: A Guide to Measuring Feature Contribution to KPIs

May 29, 2025
12 min read
Guide
Advanced
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

Product Analysts
Product Managers
Data Scientists

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

Loops (product analytics platform) Experimental design frameworks Propensity scoring methods Causal graph analysis tools

Topics Covered

causal-analysis feature-contribution kpi-measurement experimentation