You Shipped That A/B Test Three Weeks Ago. Nobody Remembers Why.
Your growth team just pinged the product channel: "Should we ship the new onboarding flow? The test has been running for two weeks."
Someone asks: "What was the hypothesis again?"
Nobody's sure. Was it to improve activation? Reduce drop-off? Increase time-to-value? The experiment documentation lives... somewhere. Maybe that Notion doc from the planning meeting? Or was it in the Jira ticket?
Someone else asks: "What were the results?"
Your data analyst shares a screenshot from your experimentation platform. Variant B had 6.2% higher conversion. P-value of 0.04. Statistically significant. Ship it?
But wait—what about retention? What about the secondary metrics? Did we check segment breakdowns? Someone vaguely remembers the hypothesis mentioned something about mobile users specifically...
By the time you've reconstructed the original experiment plan, debated statistical significance (is 0.04 enough? should we wait another week?), and hunted down all the relevant context, the meeting's over and nobody made a decision.
This is what A/B testing becomes when your experiment planning, results interpretation, and conclusions live in three different places. You're running experiments. You're just not learning from them.
The Scattered Experiment Problem
Here's what actually happens at most data-led companies:
Experiment planning happens in docs. You write a hypothesis in Notion. Define success metrics. Maybe calculate sample size in that calculator that pops up when you Google "A/B test calculator." Someone copies it into a Slack thread for visibility.
Results live in platforms. Your feature flagging tool (LaunchDarkly, Optimizely, whatever) shows statistical significance. Your product analytics (Amplitude, Mixpanel) shows cohort comparisons. Your data warehouse has the raw event data if anyone wants to dig deeper.
Discussion happens everywhere. Slack threads. Meeting notes. Comments on dashboards. Someone's spreadsheet. That one engineer who's skeptical leaving feedback in a code review.
So when it's time to decide whether to ship, you're stitching together context from five different places. What was the hypothesis? (Check Notion.) What were the results? (Check the experimentation platform.) What did we learn? (Scroll through three weeks of Slack history.)
And next quarter, when someone asks "didn't we already test something like this?" nobody can find the documentation. Or they find it, but the results are just a screenshot with no context. Or the context exists, but it's disconnected from the actual data.
Your company is running experiments. Great. But you're not building institutional knowledge. You're creating documentation debt.
What If Your Experiment Was a Complete Story?
Count's A/B test report template puts experiment planning, result interpretation, and conclusions in one collaborative canvas.
Planning and documentation. Hypothesis clearly stated. Variants defined. Success metrics documented. Sample size calculations shown. Everyone knows what you're testing and why, right there in the canvas where they'll see the results.
Results and visualization. Statistical significance. Confidence intervals. Time-series trends showing how metrics evolved over the experiment duration. Segment breakdowns if you need them. Charts and tables with live data from your data warehouse, or CSVs if that's your workflow.
Conclusions and learnings. Ship or kill decision. Why you chose that. What you learned. What to test next. Dissenting opinions documented (because someone always thinks you should wait another week).
Which is a fancy way of saying: your A/B test becomes a complete narrative instead of scattered artifacts.
Your PM proposes shipping. Your designer comments on the canvas: "Variant B performed better overall, but mobile drop-off is still concerning—should we iterate?" Your data analyst adds context: "Significance hit on day 10, has been stable since—safe to ship." Your engineering lead drops a sticky: "Agreed to ship, but flagging concerns about implementation complexity for future tests."
Three weeks later, someone asks "why did we ship that?" The answer is right there. Hypothesis. Results. Discussion. Decision. All in one place.
Done.
From Statistical Theater to Institutional Learning
Here's what changes when your experiments are unified canvases:
Decisions get faster. No more hunting for context. The hypothesis, results, and discussion are in the same place. Review the canvas, make the call, move on.
Learning compounds. Next quarter's experiments benefit from this quarter's documentation. You can see what you tested, what worked, what failed, and why—without relying on institutional memory.
Experimentation normalizes. When experiments are easy to document and share, teams run more of them. When results are visible across functions, everyone gets better at data-driven decisions.
The reality is: A/B testing works when you treat experiments as learning opportunities, not just ship/no-ship gates. But learning requires documentation. Context. Narrative. The ability to look back six months later and understand what you tried and why.
Build your A/B test report once as a template. Use it for every experiment. Document your hypothesis, visualize your results, capture your conclusions. And finally turn experimentation from statistical theater into actual institutional knowledge.
Right?