Best Incrementality Testing Tools

Compare the Top Incrementality Testing Tools as of December 2025

What are 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 Incrementality Testing tools currently available using the table below. This list is updated regularly.

  • 1
    Skai

    Skai

    Skai

    Introducing the engine for full-circle intelligence, deeper customer connections and insights-driven success, from concept to commerce. Make smarter, predictive decisions at every turn with all the data you need and faster time-to-insight. Our rich data solutions provide AI-contextualized actionable intelligence about consumer sentiment, market trends, competition and more. Customer interactions happen everywhere. Maximize every touchpoint with the the broadest reach and gain the power to engage across all critical retailers, DTC channels, search engines, social media and more. Get more return from your ad spend with flexible, robust and cross-channel performance media activation. Skai’s™ solutions are industry-leading, with seamless ecosystem integrations, for data-driven campaigns and results that exceed KPIs.
  • 2
    Lifesight

    Lifesight

    Lifesight

    Lifesight is a unified marketing measurement platform that helps marketers move away from inaccurate measurement techniques and enables them to measure their full-funnel marketing using modern measurement methods like marketing mix modeling, incrementality testing & causal attribution - Helping them make better decisions that result in sustainable & profitable growth.
  • 3
    Google Meridian
    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
  • 4
    WorkMagic

    WorkMagic

    WorkMagic

    WorkMagic helps omnichannel brands measure the true impact of their marketing by combining incrementality testing, multi-touch attribution, and MMM into one unified platform. Its automated geo-based incrementality tests reveal what spend actually drives incremental sales across DTC, Amazon, retail, and wholesale. Instead of relying on guesswork or over-attribution from ad platforms, WorkMagic calibrates attribution models using real experimental results for maximum accuracy. Brands get a clearer view of performance, smarter budget allocation, and higher iROAS. Onboarding takes minutes, thanks to seamless integrations with Shopify, marketing channels, and marketplaces. With WorkMagic, teams finally understand what truly works—and what doesn’t—and can scale efficiently with confidence.
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