Proxy Metrics in Experiments

Proxy metrics in experiments are faster, more frequent, and less noisy signals used to make experiment decisions when they are logically and historically linked to a slower downstream KPI.

Key points

• Statsig argues that revenue can be too sparse and noisy to use as the primary decision metric for every experiment, even when it remains the ultimate business KPI ^[src-031].
• A useful proxy sits up-funnel from the target outcome, fires more often, and has a stable historical correlation with the downstream KPI ^[src-031].
• Proxy metrics should be less exposed to external shocks such as holidays or marketing pulses and should reduce noise enough to create faster evidence ^[src-031].
• The article’s example is using clicks on “Add to cart” as a faster signal for purchase conversion when historical analysis supports the relationship ^[src-031].
• The downstream KPI should not disappear; Statsig recommends keeping it as a guardrail while deciding the experiment on the validated proxy ^[src-031].

Related entities

• Statsig

Related concepts

• A/B Test Acceleration
• Experiment Statistical Power
• Experiment Iteration Loop
• A/B Testing Mindset

Source references

• ^[src-031] Yuzheng Sun — “Speeding up A/B tests with discipline” (2025-06-24)