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Eppo

A warehouse-native experimentation platform that runs analysis directly on top of your data warehouse rather than a proprietary event store.

What Is Eppo?

Eppo is an experimentation platform built on the warehouse-native thesis: your data already lives in Snowflake, BigQuery, Redshift, or Databricks, and experiment results should be computed there rather than duplicated into a vendor's event store. Eppo provides feature flags, randomization, a metric catalog, and a statistics engine that issues SQL to your warehouse.

Also Known As

  • Warehouse-native experimentation
  • "The dbt-friendly experimentation tool"
  • Eppo Experimentation
  • Eppo Feature Flagging (the flag product within the suite)

How It Works

A growth data scientist defines a metric in Eppo's catalog — for example, "7-day retained users" — backed by a SQL definition against the warehouse. An engineer randomizes users via Eppo's SDK. Exposures are logged to the warehouse as events. Eppo then runs SQL against the warehouse to compute lift, confidence intervals, and guardrails, surfacing results in the Eppo UI without ever extracting the underlying data.

Best Practices

  • Invest in metric definitions the way you'd invest in dbt models — version-controlled, peer-reviewed, documented.
  • Use CUPED or similar variance reduction techniques when supported; Eppo implements them and they materially shrink required sample sizes.
  • Align the experimentation metric catalog with your exec KPI definitions so there's no "the dashboard says X, the experiment says Y" conflict.
  • Keep exposure events lightweight and well-typed to avoid warehouse cost blowouts.

Common Mistakes

  • Letting metric definitions drift between the experimentation layer and the BI layer. The whole point of warehouse-native is shared truth.
  • Underestimating warehouse query costs. A busy experimentation program can rack up meaningful Snowflake bills.
  • Running too few experiments to justify the platform — Eppo shines when you have real volume.

Industry Context

Eppo is most popular in data-mature SaaS/B2B and consumer companies with strong analytics engineering functions. Ecommerce/DTC adopts it when the team already lives in the warehouse. It's less common in pure marketing/CRO shops that don't have warehouse infrastructure.

The Behavioral Science Connection

Warehouse-native experimentation attacks the "two sources of truth" fallacy. When experimentation and BI use different numbers, organizations fall into narrative-driven decision making — whichever number supports the preferred story wins. Unified data forces intellectual honesty.

Key Takeaway

Eppo is the experimentation platform for data-mature organizations that want experiment results computed on the same warehouse powering their business intelligence.

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