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Amplitude Experiment

Amplitude's experimentation product, offering feature flags and A/B testing natively integrated with Amplitude's product analytics.

What Is Amplitude Experiment?

Amplitude Experiment is the experimentation module within Amplitude, the product analytics platform. It offers feature flags, A/B testing, and multivariate testing, with tight integration to Amplitude's event taxonomy — experiments can use any existing Amplitude event as a metric without redefining it.

Also Known As

  • Amplitude Experiment (product name)
  • Amplitude Feature Flags
  • Amplitude Experimentation

How It Works

A product team using Amplitude for analytics installs the Amplitude Experiment SDK. They wrap a new feature in a flag, define the primary metric as an existing Amplitude event ("signup_completed"), and launch. Exposures and events flow into the same Amplitude instance, and experiment results surface in the Experiment UI using Amplitude's stats engine.

Best Practices

  • Leverage the existing Amplitude taxonomy for metrics — don't redefine events just for experiments.
  • Use Amplitude cohorts as segmentation for experiment targeting (e.g., target only users in the "Power User" cohort).
  • Keep an eye on event volume costs; experimentation layers on additional exposure events.
  • Integrate Amplitude Experiment results into your standard Amplitude dashboards so experiment outcomes are visible alongside regular product metrics.

Common Mistakes

  • Duplicating metric definitions between analytics and experimentation, defeating the integration benefit.
  • Under-configuring exposure tracking and ending up with experiments that show no data.
  • Using Amplitude Experiment when your team doesn't use Amplitude Analytics — the integrated pitch falls apart.

Industry Context

Amplitude Experiment is most common in SaaS/B2B and consumer apps where Amplitude is already the analytics standard. It's less common in ecommerce (which often uses GA4 or Shopify analytics) and lead gen. The tight analytics integration is its main differentiator.

The Behavioral Science Connection

Integrated analytics-plus-experimentation reduces the cognitive cost of turning insight into experiment. When the hypothesis-generation and validation layers share the same data, teams go from insight to test in hours instead of weeks. Over time, this compounds into dramatically more learning per quarter.

Key Takeaway

Amplitude Experiment is the experimentation layer on top of Amplitude analytics — pick it if your team already lives in Amplitude and wants metrics consistency across analytics and experiments.

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