Sellforte Experiments helps you measure the causal impact of specific marketing activities. While Marketing Mix Modeling answers the broad question — what is the overall ROI of our media mix? — Experiments answers a narrower and more actionable one: did this specific campaign, promotion, or test actually drive incremental results?
Experiment results are also designed to feed back into MMM calibration, creating a stronger and more evidence-based measurement foundation over time.
Three experiment types
Sellforte Experiments currently supports three types of experiment. Each is designed for a different situation.
Conversion Lift
For importing and visualising the results of a conversion lift study run inside Meta. Meta conducts the experiment; Sellforte structures, displays, and summarises the output.
Use this when you have already run a Meta conversion lift study and want to review and store the result in Sellforte.
Geo Lift
For measuring the incremental impact of a campaign or spend change using geographic groups already in Sellforte. No data upload is required. Sellforte builds a synthetic control from untreated regions and estimates the causal effect of the treatment.
Use this when a campaign ran in some regions but not others, and the relevant data is already in Sellforte.
A/B Test
For measuring incremental impact using data you upload directly. Uses the same methodology as Geo Lift, but accepts any grouping — customer segments, product categories, store tiers, or geographies — as long as the treatment is clearly isolated.
Use this when the relevant data is not in Sellforte, or when the experiment does not fit a standard geographic split.
Choosing the right experiment type
A simple way to decide:
- You ran a Meta conversion lift study and have the export → Conversion Lift
- Your data is in Sellforte and the split is geographic → Geo Lift
- Your data is outside Sellforte or the split is non-geographic → A/B Test
A consistent workflow across all types
Regardless of experiment type, Sellforte presents results in a consistent structure: headline metrics, visual charts, an AI-generated summary, and a comments section for capturing conclusions. All experiments are stored in the Experiment Library, making it easy to review, compare, and build on previous work.
How Experiments supports MMM
Experiments is a complement to MMM, not a replacement for it. MMM gives a broad view of marketing performance across channels. Experiments gives a focused read on whether a specific intervention caused an incremental outcome.
High-quality experiment results can be used to calibrate MMM with real-world evidence, improving the reliability of model outputs over time. Not every experiment is suitable for calibration — the quality of the design and the strength of the result both matter.
Further reading
Detailed guidance for each part of the Experiments toolset is available in the following articles:
- Sellforte Experiments: Conversion Lift — what it is, when to use it, and how to set it up
- Sellforte Experiments: Geo Lift — what it is, when to use it, and how to set it up
- Sellforte Experiments: A/B Test — what it is, when to use it, and how to set it up
- Sellforte Experiments: Methodology — how Geo Lift and A/B Test estimate incremental impact
- Sellforte Experiments: How to Read the Results Dashboard — how to interpret every metric, chart, and section