Attribution & marketing mix modeling · Recast

Recast

Bayesian marketing mix modeling delivered as a managed SaaS, combining statistical MMM with geo experiments.

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Recast runs Bayesian media mix modeling for advertisers as a service: customers send spend and outcome data, Recast's statisticians configure and maintain the model, and the output is a continuously updated view of each channel's contribution and diminishing-return curve, used to guide budget allocation. It pairs the model with geo-experiment design to validate and calibrate MMM estimates against real holdout tests, addressing MMM's classic weakness of relying on historical variation alone. Unlike the DIY open-source route (Meta's Robyn or Google's Meridian), Recast is a paid, vendor-run alternative aimed at teams that want MMM output without hiring a data scientist to build and maintain the model themselves.

At a glance

Vendor Recast
Pricing model Quote only
Free tier No
Deployment Cloud
Open source No
Best for Advertisers wanting rigorous MMM output without building and maintaining the statistical model in-house.

Pricing

Custom quotes based on ad spend and number of channels modeled; pricing is not published.

Pricing has not been verified yet — see the vendor's site.

Features

  • Managed Bayesian media mix modeling
  • Geo-experiment design to validate model estimates
  • Diminishing-returns and saturation curves by channel
  • Budget allocation and scenario simulation
  • Regular model refreshes as new data arrives
  • Statistician-assisted setup and interpretation

Integrations

Profile last reviewed September 21, 2026

Alternatives

Robyn Open-source semi-automated marketing mix modeling package from Meta, built in R for data scientists to run themselves. Meridian Open-source Bayesian marketing mix modeling framework from Google, written in Python, for measuring channel ROI and optimizing budget. Haus Incrementality-testing platform that runs randomized geo and holdout experiments to measure marketing's causal lift.

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Terms to know

Marketing mix modeling A statistical technique estimating how much each marketing channel and external factor contributes to results. Causal inference The set of methods used to determine whether one variable actually causes a change in another, not merely correlates with it. Incrementality testing An experiment measuring the true causal lift of a marketing activity by comparing it against a held-out control. Data scientist A practitioner who combines statistics, programming and domain knowledge to build models and extract insight from data.

Related guides

How to choose a marketing attribution or MMM tool Attribution, media mix modeling, and incrementality testing answer the same question three different ways. Pick the method before you pick a vendor. How to choose an attribution model Rule-based, data-driven, MMM and incrementality testing answer different questions — pick by what decision the answer has to support. How to run marketing mix modeling What data an MMM needs, the build-vs-buy choice between open-source libraries and managed vendors, and how to validate the model with real experiments.