Heap Experiments
Heap's experimentation capability, leveraging its autocapture analytics to run and analyze A/B tests without extensive instrumentation.
What Is Heap Experiments?
Heap Experiments is the experimentation layer within Heap, a product analytics tool known for autocapture — the ability to record every user interaction automatically without manual instrumentation. Heap Experiments uses this autocapture foundation to let teams define experiment metrics retroactively, from events they didn't have to plan for in advance.
Also Known As
- Heap Analytics Experiments
- Autocapture-based experimentation
How It Works
A team running Heap already has all user interactions captured. They launch an experiment and, instead of pre-defining metrics, they can analyze any behavior that occurred during the experiment — even metrics nobody thought to track beforehand. This "retroactive metric definition" is Heap's differentiator.
Best Practices
- Still define a primary metric before launch; retroactive analysis is a bonus, not a substitute for a hypothesis.
- Use autocapture for secondary and guardrail metrics where the cost of instrumentation would otherwise be prohibitive.
- Validate that autocapture is actually capturing what you think it is — edge cases (iframes, SPA route changes) can miss events.
- Coordinate with analytics leadership so retroactive definitions don't proliferate into a mess of redundant metrics.
Common Mistakes
- Treating autocapture as a reason to skip the hypothesis step — it isn't.
- Defining five post-hoc "wins" on the same experiment and ignoring the multiple comparisons problem.
- Not validating autocapture coverage on newer SPA or React-heavy apps.
Industry Context
Heap Experiments is most common in SaaS/B2B and consumer web apps where the Heap autocapture model already fits. It's less common in ecommerce (which favors GA4 or Shopify) and lead gen. Autocapture trades instrumentation effort for data volume costs.
The Behavioral Science Connection
Retroactive metric definition is a double-edged behavioral tool. It unlocks genuinely useful post-hoc analysis, but it also enables hypothesis fishing — running experiments and declaring whichever metric happened to move as "the" result. Strong analysis discipline is a prerequisite for using autocapture responsibly.
Key Takeaway
Heap Experiments leverages autocapture to enable retroactive metric analysis — powerful for exploratory work, dangerous without disciplined hypothesis framing.