Not just the result of one test —
the health of your entire program
AB-Labz aggregates every experiment in your archive into a three-level dashboard: operational efficiency, product results, and business impact. One place to answer "is our testing actually working?"
Try freeThree levels of visibility
Each level answers a different question — from process hygiene to board-level reporting.
Operational
Is the process healthy?
Capacity, SRM Health Rate, Sample Sufficiency — hygiene checks on how experiments are set up and run, before you even look at results.
Product
Are hypotheses working?
Win Rate and Signal Rate tell you whether the team is generating real learning — and Consistency tracks whether that improves over time.
Business
What is the program worth?
GMV PnL and CR PnL aggregate the revenue impact of shipped experiments into a running total — ready for your quarterly business review.
Is the testing process running well?
Tracks the hygiene of how experiments are set up and run — before looking at whether they win or lose.
Capacity
Share of filled days
SRM Health Rate
Clean splits ratio
Sample Sufficiency
Adequately powered
Are our experiments actually working?
Win Rate and Signal Rate tell you whether your team's hypotheses generate real signal — not just whether tests are running.
Win Rate
Positive key metric result
Share of experiments with a significant positive result on the key metric. Benchmark: 20–30%.
Signal Rate
Any detectable effect
Any meaningful effect on any metric. A higher bar than Win Rate — measures hypothesis quality, not just outcome.
Does your team learn from its experiments?
Consistency is Signal Rate tracked as a trend over time. When you watch how Signal Rate moves month to month, you're asking a harder question: is the team getting better at identifying quality hypotheses before they launch?
A rising line means analysts are learning from past experiments. A flat line means the team is still running on intuition alone.
Signal rate — monthly
↑ Trending up
What is the AB department actually worth?
Effect is measured as normalised ARPU delta per user per month. Multiply by your MAU — and that's your number for the business review.
GMV PnL
Cumulative GMV per user · 30d
Total uplift
+13.80
CR PnL
Cumulative CR delta · pp
Total uplift
+4.7 pp
Positive Experiments
Positive GMV impact
Negative Experiments
Negative GMV impact
Noise
Inconclusive GMV signal
See your program's health in one dashboard
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