Recast
Ridding the world of wasted marketing spend through privacy-friendly attribution, modern Bayesian statistics, and automated data pipelines. Most clients using Recast can improve their blended ROI by 10% within 6 months, achieving faster and more efficient growth. Recast does not use any user-level or cookie data, so it is easy to set up and will not be affected by changing privacy regulations that affect other measurement methodologies. Recast helps optimize your marketing performance by accurately measuring the true impact of your marketing in real time. Built to help modern marketers adjust spend based on real-time performance. Confidence intervals for every ROI, saturation curve, and time shift estimate. Predicts where the next dollar will be most effective because they are different and impactful concepts. Recast’s fully Bayesian model lets us incorporate your business context right into the code.
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Measured
Measured provides marketing attribution & cross-channel view across all media channels, PLUS media incrementality testing. Turn on 100+ audience level experiments across Google, Facebook and on 70+ integrated media platforms. Identify Media Waste, Test for Scale. Capture up to 30% marketing efficiency. Powered by incrementality measurement.
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Solutions provided:
- Marketing Attribution, Cross-Channel View of Marketing Spend
- 70+ integrations on major media platform like Google, Facebook, Verizon Media, Criteo, AdRoll, SnapChat, YouTube, and more!
- Run always-on, A/B, incrementality tests seamlessly
- Integration is easy, be up and running in less than 24 hours
- Understand maximum, efficient spend levels without an expensive stress test
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Robyn
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.
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Haus
Haus is a marketing science platform that enables brands to measure the precise business impact of their advertising efforts, both online and offline, through automated incrementality experiments. It offers products like GeoLift for geo-based incrementality testing, Causal Attribution for day-to-day incrementality reporting, and the upcoming Causal MMM for incrementality-powered media mix modeling. These tools allow users to design and launch experiments in minutes, obtain results in as little as two weeks, and optimize marketing investments with daily incrementality reporting. Haus emphasizes privacy-durable solutions that do not rely on pixels, cookies, or personally identifiable information, ensuring compliance with evolving privacy regulations.
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