7 Signs Your Experimentation Program is Failing

Most A/B tests get ignored. The data team runs them, shares the results, and... nothing happens. Nobody outside the analytics team even knows the experiment existed.

Sound familiar? You're not alone. After talking to hundreds of product teams, we've identified the most common signs that an experimentation program is failing.

1. Low experiment velocity

If your team runs fewer than four experiments per month, you're leaving growth on the table. High-performing teams run 10 or more experiments monthly.

The fix: Set a team goal for experiments shipped, not just features shipped.

2. Results go ignored

You run experiments, but the results don't change decisions. Teams ship whatever they were going to ship anyway.

The fix: Make team leads predict outcomes before experiments conclude. When they have skin in the game, they pay attention to results.

3. Only the data team cares

Product managers and engineers treat experiments as someone else's job. They build features and throw them over the wall.

The fix: Get everyone involved in hypothesis creation. Make experiment results visible in the channels where teams already work (like Slack).

4. No feedback loop

Teams don't learn from past experiments. The same mistakes get repeated. There's no institutional memory.

The fix: Create a searchable archive of past experiments with key learnings. Review past experiments in sprint retros.

5. Testing for testing's sake

Experiments happen, but there's no clear hypothesis or success criteria. "Let's just see what happens."

The fix: Require a written hypothesis before any experiment launches. Include the expected impact size.

6. Fear of failure

Teams only test safe ideas because failed experiments feel like personal failures.

The fix: Celebrate learning, not just wins. A well-run experiment that disproves a hypothesis is still valuable.

7. No cross-team visibility

Teams run duplicate experiments because they don't know what others are testing.

The fix: Create a shared experiment calendar. Post new experiments to a public channel.

What's next?

If you recognized your team in three or more of these signs, it's time to rethink your approach. The good news: these problems are fixable.

The key is making experiments engaging and visible. When people care about outcomes, they pay attention to results.

That's exactly why we built ExperimentBets. It turns passive observers into active participants by letting teams predict experiment outcomes.