Best Free Incrementality Testing Tools
View:
Compare the Top Free Incrementality Testing Tools as of January 2026
Sort By:
What are Free Incrementality Testing Tools?
Incrementality testing tools help marketers measure the true causal impact of their advertising and growth campaigns by isolating what results would have happened without the marketing effort. These tools run controlled experiments—such as holdout groups, geo-testing, or matched-market tests—to determine whether an action actually drives incremental revenue, conversions, or lift. They cut through attribution noise by focusing on statistically valid comparisons rather than last-click or multi-touch modeling alone. Modern incrementality platforms use machine learning to automate experiment setup, validate sample sizes, and deliver reliable, real-time reporting. With these tools, teams can optimize budgets, improve ROI, and eliminate wasted ad spend with confidence. Compare and read user reviews of the best Free Incrementality Testing tools currently available using the table below. This list is updated regularly.
-
1
Google Meridian
Google
Google Meridian is an open source Marketing Mix Modeling (MMM) framework built by Google to help advertisers and analysts accurately measure the impact of their marketing efforts across online and offline channels without relying on cookies or user-level tracking. At its core, Meridian uses a Bayesian causal-inference model that can ingest aggregated data (spend, sales or KPI outcomes, reach/frequency, geo-level data, seasonality, and external controls) to estimate the incremental contribution each marketing channel (e.g., search, social, video, offline media) makes to overall performance, and compute return on ad spend (ROAS), response curves, and optimal budget allocation. Because it’s open source, users have full transparency into methodology and code, giving them control over model configuration, data inputs, and assumptions.Starting Price: Free -
2
Robyn
Meta
Robyn is an open source, experimental Marketing Mix Modeling (MMM) package developed by Meta’s Marketing Science team. It’s designed to help advertisers and analysts build rigorous, data-driven models that quantify how different marketing channels contribute to business outcomes (like sales, conversions, or other KPIs) in a privacy-safe, aggregated way. Rather than relying on user-level tracking, Robyn analyzes historical time-series data, combining marketing spend or reach data (ads, promotions, organic efforts, etc.) with outcome metrics, to estimate incremental impact, saturation effects, and carry-over (adstock) dynamics. Under the hood, Robyn blends classical statistical methods with modern machine learning and optimization; it uses ridge regression (to regularize against multicollinearity in many-channel models), time-series decomposition to isolate trend and seasonality, and a multi-objective evolutionary algorithm.Starting Price: Free
- Previous
- You're on page 1
- Next
