List of the Top 2 Free Incrementality Testing Tools in 2026

Google Meridian is a publicly available framework for Marketing Mix Modeling (MMM) developed by Google to aid advertisers and analysts in accurately evaluating the impact of their marketing efforts across both digital and traditional channels without relying on cookies or tracking individual users. At the heart of Meridian lies a Bayesian causal-inference model that analyzes aggregated data such as expenditures, sales figures, key performance metrics, reach and frequency, geographic information, seasonal trends, and external variables to assess the incremental effects of various marketing channels like search engines, social media, video content, and offline advertising on overall performance, while also calculating return on ad spend (ROAS), response curves, and optimal budget allocations. Being an open-source resource, it provides users with full access to its methodologies and code, allowing for the customization of model parameters, data inputs, and foundational assumptions. This transparency not only builds user trust but also fosters collaboration among users to enhance and refine the model over time. Moreover, the community-driven aspect of the open-source framework facilitates ongoing contributions that can result in consistent enhancements and innovative solutions within the tool, thereby benefiting the broader marketing community. As users engage with the platform, they can share insights and best practices, further enriching the collective knowledge surrounding marketing analytics.

Robyn is an advanced, open-source tool for Marketing Mix Modeling (MMM) developed by the Marketing Science team at Meta with a focus on experimental applications. Its primary goal is to support advertisers and analysts in creating comprehensive, data-driven models that evaluate the influence of different marketing channels on key business outcomes, such as sales and conversions, while maintaining user privacy through the use of aggregated data. Rather than relying on the tracking of individual users, Robyn leverages historical time-series data by combining marketing spend or reach metrics—including advertisements, promotions, and organic outreach—with performance metrics to assess incremental effects, saturation levels, and carry-over dynamics. The tool employs a blend of traditional statistical methods and innovative machine learning techniques; it utilizes ridge regression to address multicollinearity in complex models, executes time-series decomposition to separate trends from seasonal variations, and applies a multi-objective evolutionary algorithm for optimization purposes. This cutting-edge methodology empowers businesses to achieve a deeper understanding of their marketing performance, enabling them to make data-driven decisions founded on solid analysis. As organizations increasingly prioritize data-driven strategies, tools like Robyn will play a crucial role in enhancing marketing effectiveness and driving growth.