Turn past A/B tests into structured team memory.
Import CSV, JSON, spreadsheet rows, or a supported platform export. GrowthLayer keeps the record, evidence checks, status, and learning together for future review.
Built for teams running 30+ tests a year across brands, agencies, product lines, or growth squads.
Illustrative experiment record
Checkout trust badge test
Variant B added trust badges below payment CTAs.
Control: 12,847 visitors, 412 orders.
Variant: 12,903 visitors, 487 orders.
Reported lift: +17.7%. Winner.
- Hypothesis
- Trust signals reduce checkout anxiety
- Outcome
- Variant B won
- Calculated lift
- +17.7%
- Statistical QA
- p = 0.013, no SRM warning
Saved with tags: checkout, trust, payment, anxiety, ecommerce.
The problem
Testing tools show results. They do not preserve what your team learned.
Before
Results trapped in testing tools, docs, spreadsheets, and memory.
During import
Hypotheses, variants, metrics, and learnings become structured.
After
The next roadmap decision starts with a consistent prior record.
You already paid for the experiments. GrowthLayer makes sure you keep learning from them.
Old results are scattered
A single program can have tests in Optimizely, VWO, spreadsheets, decks, Jira, Notion, and Slack.
Learning does not compound
Teams remember individual winners, but rarely preserve the pattern behind why something worked.
New tests repeat old mistakes
Without structured history, teams can miss prior evidence and repeat ideas the organization already evaluated.
From raw result to reusable evidence.
- 01
Choose a shipped input path
Add a test manually, paste spreadsheet rows, upload CSV or JSON, or select a supported platform export.
- 02
Preview and map the fields
Confirm the parsed experiments or map generic columns to the GrowthLayer record before importing.
- 03
Validate the result
The product checks the numbers and flags risky conclusions, missing sample data, SRM issues, and mismatch between stated and calculated results.
- 04
Save it to the workflow
The test becomes a private structured record that the team can review, move through statuses, and include in reports.
See how an imported result becomes a reviewable library record.
This mockup uses sample data—not customer results—to show the shipped review pattern: source data, mapped fields, statistical QA, an explicit related-test check, and the final save step.
03
imports waiting
04
QA checks run
02
similar tests found
GrowthLayer workspace
Experiment import review
Selected import
Pricing page annual savings test
Imported source
Variant added annual savings callout near billing toggle.
Control
18,902 visits
614 trials
Variant
18,744 visits
701 trials
Analyst note: buyers responded to clearer annual savings. Follow up with enterprise proof near the same decision point.
Mapped record
Hypothesis
Annual savings clarity reduces pricing hesitation
Primary metric
Trial starts
Audience
New visitors on pricing page
Outcome
Winner, statistically significant
Statistical QA
Lift
+15.1%
Matches imported result
p-value
0.009
Statistically significant
SRM
Passed
Traffic split looks healthy
Bayesian win probability
99.5%
Variant likely better
Start with one result your team already has. No credit card required.
A test library is only valuable if analysts can trust it and use it again.
Import structured history
Paste spreadsheet rows, upload CSV or JSON, or use a supported platform export. Preview and map the data before importing it.
Validate before the team trusts it
Recalculate lift, p-values, Bayesian probability, and SRM risk before an old result becomes part of your decision history.
Review a consistent record
Keep the hypothesis, metric, page, outcome, dates, tags, and learnings in one repeatable format.
Know what you tested where
See testing coverage across your site: which pages and sections have evidence, what won there, and where you are still guessing.
Manage the test pipeline
Filter by status, switch between list and board views, and prioritize ideas with ICE, PIE, or RICE scoring.
Review quarterly outcomes
Summarize recorded test volume, outcomes, velocity, supplied impact fields, and documented learnings by quarter.
One library answers different questions for every growth stakeholder.
Analysts need clean records. Directors need program patterns. Executives need proof that experimentation is compounding.
- 1
CRO analyst
Import a result, review the mapped fields, verify the available math, and preserve the decision context.
- 2
Growth lead
See where the program is learning, which themes keep winning, and which areas are still running on opinion.
- 3
Agency strategist
Pilot one client history using private records, status workflow, and quarterly reporting before a broader rollout.
- 4
CMO or founder
Ask what the team has learned about checkout, pricing, onboarding, or acquisition and get evidence instead of anecdotes.
Start with the last test your team ran.
Import one old experiment, verify the result, and see how GrowthLayer turns it into reusable knowledge for the next roadmap conversation.