Test comparison
You
How do our Meta tests compare?
AI
Let’s match the markets, periods, and KPIs.
Sellforte Test Library
Turn every test into knowledge your team can use
Keep geo tests, Meta Conversion Lift Studies, and A/B tests in one place. Find the evidence behind your next media decision, explore the results, and build on what your team has already learned.
Part of Sellforte Incrementality Testing. Available without MMM.
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Your testing history, ready for the next decision
Keep every test type in one shared library
Bring geo tests, Meta Conversion Lift Studies, and uploaded A/B tests into a consistent view. See what was tested, where and when it ran, and the current status.
Review spend, test iROAS, and confidence where available. Open an individual test to understand the evidence behind the headline result.
| Test | Spend | iROAS | Confidence |
|---|---|---|---|
| Meta Cart RetargetingConversion Lift · United States | $61.11k | 5.01 | 96% |
| Meta + Google go-darkGeo test · United States | $1.08M | 5.06 | 100% |
| Email winback pushA/B Test · United States | $7.63k | 9.50 | 100% |
| Meta full-funnelConversion Lift · United States | $350.41k | 3.14 | 98% |
| Meta ProspectingConversion Lift · United States | $360.61k | 2.65 | 95% |
Illustrative demo data. Open each test for its design, measurement window, and result details.
Find relevant tests with flexible filters
Filter by ad platform, country, experiment type, status, or date range, and search for a test by name.
Pull up last quarter’s Meta studies, find a geo test in a specific market, or revisit an earlier owned-media experiment. Start with the question you need to answer and narrow the library to the relevant evidence.
Find the evidence you need Example selection
- Experiment type
- Conversion Lift
- Country
- United States
- Ad platform
- Meta Ads
- Status
- Analyzed
- Active during
- 1 Jul – 30 Sep 2026
- Search by name
- Prospecting
Go beyond the headline number
Open the analysis behind each result
Review the test design, incremental sales, and uncertainty in one results dashboard. For geo and A/B tests, inspect actual performance against the counterfactual and the quality of the pre-treatment match.
For Meta Conversion Lift Studies, review the imported study results and the iROAS distribution. Spend and iROAS are shown when the relevant media data is available.
From the library to the result Illustrative example
Geo test · Paid social · United States
Incremental sales$150k
Incremental spend$50k
Test iROAS3.0
90% credible interval2.4–3.6
Read the estimate alongside the intervention, test period, and pre-treatment fit.
Preserve the context, conclusions, and next steps
Use the AI summary for a readable explanation of the design and findings. Add comments to record business context, decisions, and follow-up questions directly on the experiment.
When the same budget question comes up again, your team can revisit what was measured and why a decision was made.
Keep the learning with the test Illustrative notes
AI summary
The test estimates the incremental sales associated with the media increase. Review the interval and test design alongside the central estimate.
Team comment
A promotion ran during the test. Review whether it affected test and control markets differently before applying the finding.
Follow-up
Use this finding in the next budget review and assess whether a repeat test would answer the remaining question.
Build on evidence across test types
Compare results without losing the differences
A common library makes it easier to revisit evidence from different methods. Compare findings for the same channel or market and understand where they agree, differ, or leave a question open.
A higher iROAS in one study is not automatically a better investment. The KPI, audience, intervention, spend level, and measurement window can differ between tests.
Compare the evidence in context
Geo tests
Regional data in Sellforte; Bayesian synthetic control estimates the counterfactual.
Meta Conversion Lift Studies
Results from a holdout experiment conducted in Meta and imported into Sellforte.
Uploaded A/B tests
Your test and control data; the same analysis methodology used for geo tests.
Bring relevant test evidence into your measurement
Where MMM is part of your setup, suitable experiment results can inform model calibration. Review the contributing studies, their iROAS estimates, and uncertainty before applying that evidence.
Keep uncertain findings in the library too. They help your team see which questions remain unanswered and where a follow-up test could be useful.
Find your next test with Test Prioritizer · Compare plans and capabilities
Meta Prospecting · RevenueMeta Ads · United States · Short uplift
- Weighted Experiment iROAS
- 1.64
- Approx. 90% interval
- 1.39–1.88
| Contributing experiments | iROAS | 90% interval | Weight |
|---|---|---|---|
| Meta US upper-funnel | 1.34 | 1.21–1.47 | 72% |
| Meta US prospecting | 2.40 | 1.58–3.22 | 28% |
| Meta US prospecting · follow-upBelow 50% significance | 2.10 | −0.31–4.51 | 0% |
Bring your test library into your own AI workflows
Quarterly readout
You
Summarize our tests from last quarter.
AI •••
Let’s pull together the findings and open questions.
Uncertain results
You
Which tests need a closer look?
AI •••
Let’s review the estimates and their uncertainty.
Next test
You
Where do we need more evidence?
AI •••
Let’s look for gaps in recent testing.
Tests and MMM
You
Do our tests agree with our MMM?
AI •••
Let’s compare the same channel, market, and period.
Business context
You
What else changed during this test?
AI •••
Let’s review your campaign and promotion notes.
Leadership report
You
Create a readout from these tests.
AI •••
Let’s cover the design, findings, and next steps.
Past learning
You
What have we learned about email?
AI •••
Let’s review your completed owned-media tests.
Create reports from your test library
Use your own AI tools to turn experiment results into presentations, documents, and team readouts. Bring the test design, incremental sales, iROAS, and uncertainty together with evidence connected through Sellforte MCP.
Build test analysis agents
Give your AI agents access to the test library. Have them compare completed experiments, summarize findings, and flag uncertain results for review. Use the evidence to inform follow-up questions and future testing priorities.
Connect tests to your measurement
Bring experiment results together with MMM, attribution, and other data available to your AI. Compare the same channel, market, and period to understand where the evidence agrees or needs more investigation.
Add business context to the results
Combine test results with campaign briefs, promotions, budget changes, and previous decisions. Give your AI the context it needs to explain the findings and the questions that remain open.
Test Library FAQs
What is Sellforte Test Library?
Sellforte Test Library brings your incrementality tests into one place. Review what was tested, find relevant results, open detailed analyses, and preserve the context and conclusions for future decisions.
Which test types can I keep in Sellforte Test Library?
The library supports geo tests, imported Meta Conversion Lift Studies, and A/B tests based on uploaded data. Each type has a consistent results structure, with details appropriate to its methodology. Read the experiment overview.
How can I find a specific test in the library?
Search by test name or filter by ad platform, country, experiment type, status, and date range. Combine filters to focus on the evidence relevant to your question.
Can I compare results across different test types?
Yes. The library lets you review results across methods. Check the KPI, audience or region, intervention, test period, spend definition, and uncertainty before drawing a conclusion from differences in iROAS.
Can my team add context to a test result?
Yes. Use the comments section on an experiment to capture business context, conclusions, decisions, and follow-up actions. The AI summary provides a readable account of the design and findings.
Do I need MMM to use Sellforte Test Library?
No. The Test Library is included in standalone Incrementality Testing. MMM and model calibration capabilities depend on your plan and setup. See current plans.
Can I use my test library with AI assistants?
Use Sellforte AI to explore your tests, or connect experiment data to supported AI assistants through Sellforte MCP. Your AI environment can help compare findings and turn them into reports and other outputs. Explore Sellforte MCP.
Build your testing knowledge with Sellforte
Explore how one shared test library can support your team’s next media decision.