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Why most A/B tests fail before they even start, and what high-performing Growth Ops teams do differently.
Most teams don’t struggle with ideas.
They struggle with turning experiments into confident decisions.
When we audit growth teams, the blockers almost always come back to analytics, not tools, not talent.
Here are the 7 mistakes we see most often, and how strong teams avoid them.
1. Tracking Is Treated as an Afterthought
If tracking is added after an experiment launches, the result is almost always unusable.
You lose:
- A clean baseline
- Consistent exposure data
- Confidence in outcomes
Pattern we see:
Teams rush to ship → metrics are patched later → results are debated → nothing ships.
What works instead
- Tracking is part of experiment design
- Success metrics are locked before launch
- QA happens before exposure, not after
Experiments don’t fail at analysis.
They fail at setup.
2. Metrics Have No Clear Owner
(Short & sharp)
When multiple teams “own” a metric, no one really does.
This leads to:
- Conflicting numbers
- Endless validation
- Leadership distrust
Fix
- One owner per metric
- One definition
- One source of truth for decisions
No owner = no experiment confidence.
3. Too Many KPIs, No Decision Metric
(Medium depth)
Dashboards don’t make decisions, people do.
Yet many experiments launch with:
- 10+ metrics
- No primary success signal
- No defined decision
The result?
“Inconclusive” tests and stalled momentum.
What strong teams do
- 1 primary metric
- 1–2 guardrails
- A clear decision the test supports
If a test doesn’t change a decision, it’s noise.
4. Broken Funnels Go Undetected
(Deep with ONE example, only where it matters)
Funnels quietly break more often than teams realize.
Events change.
Properties get renamed.
Logic drifts.
Real example (brief):
A team optimized signup conversion for weeks before discovering the “signup completed” event fired twice for some users. The real bottleneck was onboarding, not signup.
What works
- Routine funnel QA
- Change tracking for events
- Early anomaly detection (often automated)
Broken funnels don’t just mislead, they waste entire quarters.
5. Experiment Results Lack Context
“Variant B won” is not a learning.
Without context:
- You can’t reuse insights
- You can’t scale impact
- You can’t explain why it worked
Strong teams always ask
- Who did this work for?
- What behavior changed?
- Would we expect this to work again?
Wins without insight don’t compound.
6. AI Insights Are Used Blindly (or Ignored)
(Medium depth, no long example)
AI doesn’t fail because it’s inaccurate.
It fails because teams don’t trust it.
What breaks trust
- Black-box logic
- No visibility into metrics
- AI replacing judgment
What works
- AI for QA, summaries, and pattern detection
- Humans for decisions
- Clear guardrails
AI should accelerate learning, not replace thinking.
7. Learnings Aren’t Stored or Reused
If experiment learnings live in Slack or Notion comments, they disappear.
That leads to:
- Repeated tests
- Lost knowledge
- Flat growth curves
Fix
- Simple experiment log
- Hypothesis → Result → Insight
- Learnings feed future tests
Experimentation only works when learning compounds.
Final Thoughts
Most experimentation problems are not about creativity or tools.
They’re about structure, ownership, and trust in data.
Fix those, and:
- Decisions speed up
- Experiments get bolder
- Growth becomes repeatable
That’s the difference between running tests and running a growth system.
If you’re unsure which of these issues exist in your setup, that’s exactly what we uncover in our Growth Analytics Audits.
