Recast is a Bayesian marketing mix modeling platform that helps brands measure incremental media impact, forecast performance, and optimize budget allocation across digital and offline channels.
Recast is built for marketers who have outgrown last-click attribution but still need a practical operating model for media decisions. Its core product is a proprietary Bayesian MMM platform with weekly model validation, uncertainty intervals, channel-level response curves, and scenario planning. Recast also offers GeoLift, a geo-based incrementality testing product that can calibrate model assumptions with real experiments.
Unlike dashboard-only attribution tools, Recast is designed for teams making large budget decisions across paid search, paid social, TV, CTV, retail media, affiliates, promotions, email, and offline factors. The platform is especially relevant for DTC brands, consumer subscription companies, marketplaces, and omnichannel advertisers that need to understand total media contribution after privacy changes have weakened user-level tracking.
What is Recast?
Recast is a marketing measurement company focused on Bayesian marketing mix modeling. MMM is a statistical approach that estimates how marketing spend, pricing, seasonality, promotions, macro conditions, and other business drivers contribute to sales or conversions over time. Recast’s Bayesian approach adds uncertainty quantification, which means the model does not just output one ROI number. It shows a likely range of outcomes and how confident the model is in each estimate.
In practical terms, Recast helps marketing teams answer questions such as: which channels are truly incremental, where are we over-invested, how much should we spend next quarter, what happens if we shift budget from Meta to YouTube, and how much confidence should we have before changing the plan. It is not a media buying tool. It is a measurement and decision-support layer for budget planning.
Key Features
Proprietary Bayesian MMM
Recast’s central differentiator is its Bayesian MMM framework. Traditional MMM often produces point estimates that look precise but hide a large amount of uncertainty. Recast models channel impact as probability distributions, making uncertainty visible to marketing, finance, and executive stakeholders. This matters because budget decisions are rarely binary. A channel may look profitable on average, but the confidence interval may show that the risk is much higher than a simple ROI number suggests.
The Bayesian approach also gives Recast a more disciplined way to incorporate prior knowledge. For example, if a brand already has experiment results showing that branded search is partly demand capture rather than demand creation, the model can be calibrated to avoid over-crediting that channel. If the brand has reason to believe TV has a long lag, the model can represent that carryover instead of treating TV like a same-week performance channel.
Core Bayesian MMM capabilities include:
- Uncertainty intervals around channel ROI, marginal ROI, and forecast outputs
- Response curves that show saturation and diminishing returns by channel
- Adstock and lag modeling for media effects that carry over time
- Seasonality and trend controls for separating media impact from baseline demand
- Promotional and pricing effects where non-media factors influence revenue
- Ongoing model validation instead of one-off annual measurement
For a planner, the important output is not “Meta ROI is 2.4.” It is “Meta’s expected marginal ROI is strong up to this spend range, uncertainty is moderate, and the model suggests diminishing returns after this budget level.” That is the kind of information that can change an allocation.
Multi-Stage Modeling
Recast’s multi-stage MMM framework is designed for brands where the customer journey cannot be explained by a single conversion event. Many advertisers have upper-funnel media that influences site visits, branded search, email signups, or store traffic before revenue appears. Single-stage models can miss that chain and under-credit brand-building channels because they only look at final conversions.
Multi-stage modeling lets a brand model intermediate outcomes and downstream conversions separately. For example, YouTube and CTV may drive site traffic and branded search interest, while paid search and retargeting close demand later. A one-stage model might over-credit search because search is closest to conversion. A multi-stage model can show how upper-funnel channels feed lower-funnel activity.
Practical applications include:
- Modeling awareness media impact on branded search, direct traffic, or email signups
- Separating acquisition media from conversion-capture media
- Understanding how upper-funnel channels influence lower-funnel efficiency
- Evaluating long-consideration categories such as financial services, B2B, education, and healthcare
- Reducing the tendency to cut brand channels because their impact is less immediate
This is especially useful for marketers defending brand or video budgets. Instead of arguing that upper-funnel media “probably helps,” Recast can model the pathway by which those channels influence measurable business outcomes.
GeoLift Incrementality Testing
GeoLift is Recast’s geo-based incrementality testing product. It compares test and control geographies to measure the lift caused by a media change, channel launch, budget increase, or campaign pause. The results can then calibrate the MMM, improving confidence in the model’s estimates.
The reason this matters is simple: MMMs are models, and models need ground truth. Without experiments, an MMM may confuse correlation with causation, especially in channels where spend rises at the same time as demand. Geo testing gives marketers an empirical check. If the model predicts that a channel should drive a certain lift, a geo experiment can test whether that lift actually appears in exposed markets.
GeoLift is useful for:
- Testing whether a channel is incremental before scaling spend
- Calibrating MMM priors with real experimental evidence
- Measuring offline or platform-walled channels where user-level attribution is weak
- Evaluating budget increases or channel pauses in selected markets
- Building executive confidence before reallocating large budgets
It is not a replacement for MMM. It is a companion. Experiments are precise for the thing tested but limited in scale and frequency. MMM covers the full portfolio continuously but depends on assumptions. Recast’s pitch is that the combination is stronger than either method alone.
Scenario Planning and Budget Optimization
Recast turns the MMM into planning workflows through scenario planning. Users can evaluate future budgets, channel mix changes, and performance forecasts before money is committed. Because the model includes uncertainty, scenarios can show not only expected outcomes but also risk ranges.
A typical workflow might compare a flat budget scenario, a 15% increase in CTV, a paid social cut, and a retail media expansion. Instead of simply projecting the historical average ROI, Recast estimates how each channel may respond at the new spend level. This is where diminishing returns becomes practical. A channel with excellent average ROI may be a poor candidate for more budget if it is already saturated.
Scenario planning supports:
- Quarterly and annual budget planning
- Channel-level spend reallocation
- Incremental budget requests to finance
- Spend cuts with least-damage modeling
- Forecasting under multiple demand assumptions
- Communication of confidence levels to executives
For media planners, this helps move recommendations from “last quarter’s ROAS was higher” to “the model expects this allocation to produce the best marginal return at the portfolio level.”
Data Integration and Model Operations
Recast requires structured data, but it is not limited to digital ad platform metrics. MMM is strongest when it includes all meaningful business drivers: spend, impressions, clicks, revenue, orders, pricing, promotions, distribution changes, seasonality, holidays, macro factors, and offline media. Recast’s implementation typically involves ingesting weekly or daily data across the channels and business outcomes that matter to the brand.
The operational burden is lower than building MMM from scratch, but brands still need clean enough data to support a reliable model. The best customers usually have consistent spend history, stable outcome definitions, and internal alignment on what decisions the MMM should support.
Typical data inputs include:
- Media spend and delivery by channel, campaign, and geography where available
- Revenue, orders, leads, trials, app installs, or other outcome metrics
- Pricing, discounts, promotional calendars, and product launches
- Seasonality, holidays, weather, or macroeconomic controls where relevant
- Experiment results from GeoLift or other incrementality tests
- Offline media and brand investment data when available
Recast is most valuable when it becomes a recurring operating cadence: ingest data, validate forecasts, review channel response, update scenarios, run experiments where uncertainty is high, and use the next model refresh to improve decisions.
Business Impact
Recast is aimed at organizations where small allocation mistakes are expensive. If a brand spends millions of dollars per year across several paid channels, even a modest improvement in marginal allocation can justify a serious MMM investment. The biggest impact is usually not a single insight but a better decision process.
Practical benefits include:
- Reduced dependence on last-click attribution: MMM accounts for offline channels, walled gardens, and privacy-limited environments.
- More confident budget shifts: uncertainty intervals make risk explicit before changing spend.
- Improved finance conversations: forecasts and scenario ranges translate marketing plans into business outcomes.
- Better experiment prioritization: teams can run GeoLift tests where the model shows high uncertainty or high budget stakes.
- Portfolio-level optimization: the model evaluates total spend allocation rather than channel dashboards in isolation.
Recast is not the right fit for every advertiser. Very small budgets, short operating histories, or chaotic tracking definitions can limit MMM usefulness. But for brands with enough spend and enough variation over time, it can become the measurement backbone for media planning.
Why Bayesian MMM?
Bayesian MMM is useful because marketing data is noisy. Spend, seasonality, promotions, competitor activity, and macro demand all move at the same time. A model that pretends to know the exact ROI of every channel can create false confidence. A Bayesian model makes the uncertainty visible and updates beliefs as new data arrives.
That matters for decisions such as cutting a channel, scaling spend, or defending a brand campaign. If two channels have similar expected ROI but one has much wider uncertainty, a finance team may reasonably treat them differently. If a channel has low average ROI but strong evidence of incremental upper-funnel impact, a media team may keep it in the plan for strategic reasons. Bayesian outputs make those tradeoffs more explicit.
Pricing
Recast does not publish standard pricing. It is an enterprise SaaS and service-supported MMM platform with custom quotes based on model complexity, number of markets, data scope, refresh cadence, experiment needs, and support requirements. Recast’s GeoLift product has been positioned with lower entry pricing than full MMM, but full Recast deployments should be evaluated as a significant annual measurement investment.
For context, enterprise MMM platforms and managed MMM engagements often range from tens of thousands to several hundred thousand dollars per year, depending on scope. A single-market DTC model is very different from a global omnichannel brand model with multiple products, offline channels, retail media, and custom experiments.
Factors that influence Recast pricing include:
- Number of brands, regions, products, or business units modeled
- Data granularity and refresh cadence
- Number of channels and outcomes included
- Need for GeoLift experiments or experiment design support
- Scenario planning and stakeholder reporting requirements
- Level of onboarding and analyst support
Teams evaluating Recast should budget not only for software but also for internal data preparation, stakeholder training, and a recurring decision cadence.
User Reviews
Recast does not have the same public review footprint as broad SaaS tools on G2 or Capterra. That is common for enterprise MMM vendors because buying decisions are usually made through referrals, case studies, measurement teams, and executive evaluation rather than self-serve review marketplaces.
Public discussion around Recast tends to emphasize its statistical rigor, Bayesian methodology, and willingness to explain the weaknesses of simplistic attribution. The company is also active in MMM education, including comparisons with open-source frameworks such as Meta Robyn and Google’s LightweightMMM.
Common strengths associated with Recast:
- Bayesian uncertainty quantification rather than point-estimate-only reporting
- Strong fit for teams that want a rigorous alternative to last-click attribution
- Practical scenario planning for budget allocation
- GeoLift calibration to connect models with real experiments
- Clear thought leadership around MMM methodology
Likely buyer concerns:
- Pricing is not publicly transparent
- Implementation requires clean data and cross-functional alignment
- MMM outputs require interpretation and stakeholder education
- Small or early-stage advertisers may not have enough data or spend variation
Recast vs Alternatives
Recast vs Measured
Measured is an incrementality testing platform that focuses on controlled experiments, holdouts, and media effectiveness measurement. Recast is primarily a Bayesian MMM platform that uses experiments, including GeoLift, to calibrate a broader portfolio model. The distinction is experiment-first versus model-first.
Choose Measured when the main need is direct incrementality testing for specific channels, tactics, or platform claims. Choose Recast when the bigger problem is ongoing budget allocation across the whole media mix. In many mature measurement programs, the two approaches are complementary: experiments validate key assumptions, while MMM provides continuous portfolio-level planning.
Recast vs Keen
Keen is a marketing investment intelligence platform designed to make budget optimization and scenario planning accessible to marketing teams. It is often positioned as easier for business users and less dependent on deep statistical interpretation. Recast is more explicitly Bayesian and statistically rigorous, with more emphasis on model validation, uncertainty, and methodological transparency.
Choose Keen when the organization wants a more accessible planning interface and ongoing budget guidance for non-technical stakeholders. Choose Recast when the team has complex measurement questions, values statistical depth, and is prepared to build a disciplined MMM operating rhythm.
Recast vs Nielsen MMM
Nielsen offers marketing mix modeling and measurement services for large advertisers, often as a managed service built on its broader media measurement footprint. Recast is a more specialized, modern MMM company centered on Bayesian modeling, weekly validation, and interactive scenario planning. Nielsen may be more familiar to large CPG and TV-heavy advertisers; Recast may appeal more to growth-oriented brands looking for a nimble platform approach.
Choose Nielsen when legacy media measurement alignment, large-enterprise procurement, or existing Nielsen relationships matter. Choose Recast when the priority is Bayesian uncertainty, faster iteration, and a platform that marketing teams can use more actively for planning decisions.
Recast vs Northbeam
Northbeam is a multi-touch attribution and media measurement platform popular with DTC and e-commerce brands. It provides more granular digital campaign visibility and faster tactical feedback than MMM. Recast is better for strategic budget allocation across channels, including offline media and privacy-constrained environments where user-level attribution breaks down.
Choose Northbeam when the primary need is day-to-day performance attribution across digital campaigns. Choose Recast when the question is incrementality and portfolio allocation: how much to spend by channel, which channels are saturated, and what total business impact marketing is driving.
Recast vs Optimine
Optimine provides marketing mix modeling and budget optimization with an emphasis on actionable planning for brand teams. Recast overlaps strongly in MMM and scenario planning, but differentiates through its Bayesian methodology, uncertainty communication, and GeoLift calibration. Both are relevant alternatives for brands trying to replace last-click reporting with a more durable measurement system.
Choose Optimine if the organization wants an established MMM vendor with accessible planning workflows. Choose Recast if the team specifically wants Bayesian modeling, transparent uncertainty intervals, and experiment-calibrated MMM.
Recast vs Marketing Mix Modeling Consultants
Traditional MMM consultants can be a good fit when a company needs a one-time custom study, board-level analysis, or highly bespoke modeling across many data sources. The downside is that consultant-led MMM can become a static annual report that does not change day-to-day planning behavior. Recast is designed to make MMM more continuous, with recurring model updates, scenario planning, and ongoing validation.
Choose consultants when the business problem is unusual, the organization needs a custom study, or internal stakeholders expect a traditional advisory engagement. Choose Recast when the goal is to operationalize MMM as a recurring planning system that marketers can use throughout the year.
Recent Updates (2025–2026)
- Late 2025: Recast released a multi-stage MMM framework for modeling complex customer journeys where upper-funnel media influences lower-funnel conversions over time.
- September 2025: Recast launched GeoLift, a geo-based incrementality testing product for validating and calibrating MMM outputs with real experiments.
- 2025: Recast continued publishing educational content comparing Bayesian MMM with open-source alternatives such as Robyn and LightweightMMM.
- 2025: Recast emphasized weekly forecast validation and uncertainty reduction as core product differentiators.
- 2026: Recast appeared in industry lists of notable MMM platforms for brands evaluating privacy-resilient measurement.
- 2025–2026: The broader MMM category continued gaining attention as marketers dealt with signal loss, platform attribution limits, and CFO pressure for incrementality evidence.
Frequently Asked Questions
What is Recast?
Recast is a Bayesian marketing mix modeling platform that helps brands estimate incremental media impact, forecast performance, and optimize budgets across digital and offline channels. It combines MMM, scenario planning, uncertainty quantification, and GeoLift incrementality testing.
How is Recast different from Nielsen MMM?
Nielsen MMM is often delivered as a large managed-service measurement engagement tied to Nielsen’s broader media measurement ecosystem. Recast is a specialized Bayesian MMM platform with interactive scenario planning, uncertainty intervals, and experiment calibration. Nielsen may be preferred by legacy TV-heavy enterprises; Recast may be preferred by growth teams that want a more iterative platform workflow.
How much does Recast cost?
Recast does not publish standard pricing. Full MMM deployments are custom enterprise engagements and should be expected to cost materially more than lightweight analytics tools. Pricing depends on model scope, markets, channels, data complexity, refresh cadence, and whether GeoLift testing is included.
Is Recast a SaaS or a service?
Recast is best understood as a SaaS platform with expert support. The software provides the modeling, validation, and scenario planning environment, but successful MMM implementation usually requires onboarding, data alignment, and analyst guidance. It is not a simple plug-and-play dashboard.
What data do I need for Recast?
Recast typically needs historical outcome data, media spend and delivery by channel, campaign timing, promotional calendars, pricing or discount information, seasonality signals, and any experiment results available. The stronger and more consistent the data history, the more useful the model will be.
How long does Recast take to deploy?
Deployment timing depends on data readiness and model complexity. A focused single-market model can move faster than a global multi-brand deployment. Teams should expect implementation to include data ingestion, validation, model setup, stakeholder review, and training before the MMM becomes part of regular planning.
Does Recast work for B2B?
Yes, Recast can work for B2B if the company has enough historical data, marketing variation, and meaningful outcome volume. B2B models may need to account for long sales cycles, pipeline stages, sales activity, and delayed revenue recognition. It is usually a better fit for scaled B2B companies than early-stage teams with sparse data.
Who founded Recast?
Recast was co-founded by Michael Kaminsky and Tomás Hoz de Vila. The company is known for its technical focus on Bayesian marketing mix modeling and for publishing educational material on modern MMM methodology.
Is Recast better than Northbeam?
Recast and Northbeam solve different measurement problems. Northbeam is stronger for tactical digital attribution and e-commerce reporting. Recast is stronger for strategic incrementality and portfolio-level budget allocation across channels, including offline and privacy-constrained media. Many teams evaluate them based on whether they need day-to-day campaign attribution or quarterly budget optimization.
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