Sellforte Experiments helps you measure the causal impact of specific marketing activities. While Marketing Mix Modeling (MMM) helps answer the broad question, “What is the overall ROI of our media mix across channels?”, Experiments helps answer a narrower and more actionable one: “Did this specific campaign, promotion, launch, or test actually work?”
Experiments is built for incrementality measurement. It helps you understand whether a specific activity drove additional sales or conversions beyond what would have happened otherwise. It also helps strengthen your measurement foundation over time, because experiment results can be used to calibrate your MMM with real-world evidence.
Sellforte Experiments currently supports three experiment types:
- Conversion Lift
- GeoLift
- A/B Test
No matter which test type you use, Sellforte brings your results into one place with a consistent structure, visual reporting, and AI-generated summaries to help teams understand and act on results faster.
What Sellforte Experiments is for
Use Sellforte Experiments when you want to evaluate the incremental impact of a single marketing action, rather than the ongoing contribution of your entire media mix.
Typical use cases include:
- Measuring whether a specific campaign drove incremental sales
- Evaluating the impact of a launch or promotion
- Understanding whether a platform test produced real lift
- Comparing exposed and unexposed groups
- Turning experiment outputs into a clear, shareable report
- Feeding experiment learnings back into MMM calibration
Sellforte Experiments is designed to make advanced experiment analysis easier to access. Instead of working through raw exports or separate analyses, you can upload or configure your test and review the results in a consistent Sellforte workflow.
1. Conversion Lift
What it does
Conversion Lift processes conversion lift test data exported from an ad platform and reports the incremental impact measured by that platform’s experiment.
At the moment, Sellforte supports Meta conversion lift uploads.
What you need to provide
To run a Conversion Lift analysis, you provide:
- The ad platform selection: Meta
- A KPI file containing the raw conversion lift export from Meta
- Optional account, campaign, ad set, or ad IDs to scope the analysis
When to use it
Use Conversion Lift when you have already run a conversion lift study in Meta and want to analyze the results inside Sellforte alongside your other experiments.
2. GeoLift
What it does
GeoLift compares groups that were exposed to a campaign or channel against similar groups that were not exposed. It then estimates the incremental sales and iROAS driven during the test period.
What you need to provide
To run a GeoLift analysis, you provide:
- A grouping dimension, such as region, state, or market
- Which groups belong to the test set
- Which groups belong to the control set
- The test window, including start and end dates
- Optional filters to narrow the dataset
GeoLift uses data that already exists in Sellforte, so no file upload is required.
When to use it
Use GeoLift when a campaign ran in some groups but not others. This is often the fastest path to a credible lift estimate when the data structure supports a treated-versus-untreated comparison.
3. A/B Test
What it does
A/B Test uses the same lift analysis approach as GeoLift, but with user-uploaded data instead of data already stored in Sellforte.
It estimates incremental sales and iROAS for a test group versus a control group over a selected time window.
What you need to provide
To run an A/B Test, you provide:
- A required KPI file, such as sales or conversions by group and date
- An optional media file, such as spend or impressions by group and date
When to use it
Use A/B Test when:
- The relevant data lives outside Sellforte
- You want to analyze a custom dataset
- Your test does not fit the standard GeoLift setup
- You still want a structured incrementality analysis using a test-versus-control design
What the results include
Sellforte Experiments presents results in a consistent way across experiment types.
Typical outputs include:
- Incremental sales
- iROAS
- Confidence interval
- p-value
- Total sales over the test window
- Observation counts for test and control groups
The main visualization compares actual sales to a counterfactual estimate of what would likely have happened without the campaign. It also includes a cumulative treatment effect curve to help show how impact developed over time.
Results are designed to be easier to interpret and easier to share than raw platform exports or manual analysis outputs. Sellforte also provides AI summaries that translate complex experiment results into clearer takeaways for marketers, analysts, and leadership teams.
How Experiments supports MMM
Experiments is not a replacement for MMM. It is a complement to it.
MMM gives you a broad view of marketing performance across channels and over time. Experiments gives you a more focused read on whether a specific intervention caused an incremental outcome.
Together, these two approaches create a stronger measurement system:
- MMM helps guide overall investment decisions
- Experiments helps validate specific actions
- Experiment results can be fed back into MMM calibration
- Over time, this can improve the reliability and trustworthiness of model outputs
This makes Experiments especially valuable for teams that want not only faster answers on individual tests, but also a stronger long-term measurement foundation.
Example scenarios
Here are a few examples of when each method is a good fit:
Use Conversion Lift if…
- You ran a Meta conversion lift study
- You have the export file ready
- You want Sellforte to structure, visualize, and centralize the result
Use GeoLift if…
- A campaign ran in some regions or markets but not others
- You have a clear treated group and control group
- The KPI and media data already exists in Sellforte
Use A/B Test if…
- You ran a test outside Sellforte
- You have KPI data by group and date
- You may also have supporting media data by group and date
- You want the same style of lift analysis using uploaded data
Why teams use Sellforte Experiments
Sellforte Experiments helps teams move from raw outputs to decision-ready insights faster. Instead of keeping results in separate spreadsheets or platform reports, teams can review experiments in a unified experience with consistent metrics, visual reporting, and AI summaries.
Key benefits include:
- A central place to review different experiment types
- Faster interpretation of results
- Clearer communication to stakeholders
- Better connection between experiment findings and MMM calibration
- A more practical way to use incrementality testing in everyday marketing decisions
In summary
Sellforte Experiments helps you answer a simple but important question: did this specific activity create incremental impact?
It supports three experiment types:
- Conversion Lift for Meta conversion lift exports
- GeoLift for treated-versus-control analysis using data already in Sellforte
- A/B Test for treated-versus-control analysis using uploaded data
Across all three, Sellforte helps you turn experiment data into clear results, visual reporting, and actionable insight — while also creating evidence that can improve MMM calibration over time.