Recast

Name: Recast
Brand: Recast

AI Native

Recast is building the world's most rigorous incrementality platform. Powered by Recast MMM and Recast GeoLift.

Est 2021|Valuation $23M|Raised $4.5M

LinkedIn 8K|Website|Web traffic 3K|FTEs 66|HQ New York, New York, US

MarketsMarketing Mix Modeling (MMM)

Published Sep 26, 2026

01Common complaints

Requirement for comprehensive enterprise security documentation up front
- If procurement requires ISO 27001 certificates, detailed security whitepapers, explicit data residency guarantees, or a published trust center, the vendor's public security documentation appears limited beyond a declarative SOC 2 compliance statement.
Channels that cannot be geographically controlled
- Vendor documentation notes limitations for channels such as SEO and certain forms of earned media; if these are the primary growth drivers, the geo experiment approach may provide limited value.
Inability to operationalize experiments
- Organizations without the capability to execute holdouts (organizationally or contractually) may find an MMM-only vendor or platform-native lift tests more appropriate.
Insufficient volume for statistical power
- Geo lift studies can produce wide confidence intervals; smaller organizations may obtain results that are not sufficiently actionable.
Need for abundant third-party reviews and references
- Major review listings showed limited user reviews at the time of this research, increasing the due-diligence burden for buyers.

1) Requirement for comprehensive enterprise security documentation up front - If procurement requires ISO 27001 certificates, detailed security whitepapers, explicit data residency guarantees, or a published trust center, the vendor's public security documentation appears limited beyond a declarative SOC 2 compliance statement. 2) Channels that cannot be geographically controlled - Vendor documentation notes limitations for channels such as SEO and certain forms of earned media; if these are the primary growth drivers, the geo experiment approach may provide limited value. 3) Inability to operationalize experiments - Organizations without the capability to execute holdouts (organizationally or contractually) may find an MMM-only vendor or platform-native lift tests more appropriate. 4) Insufficient volume for statistical power - Geo lift studies can produce wide confidence intervals; smaller organizations may obtain results that are not sufficiently actionable. 5) Need for abundant third-party reviews and references - Major review listings showed limited user reviews at the time of this research, increasing the due-diligence burden for buyers.

Deal disqualifiers (poor fit)

Very low conversion volume or highly noisy outcome signals
Channels where geographic isolation is infeasible or spillover dominates
Organizations seeking a plug-and-play attribution dashboard without a commitment to causal measurement discipline

Vendor-acknowledged limitations

Spillover and geographic contamination can reduce test accuracy.
Lift tests are time-bound and may not generalize across different time periods without re-testing.
Heterogeneous geographic markets (demographic differences) reduce the precision of estimates.
Outlier events (promotions, major news, macro events) can skew results.
Concurrent test analysis is supported but automated optimal design for multi-cell tests is not currently available.
A documented login/loading issue exists with a provided workaround (use a new browser or incognito mode).

- Spillover and geographic contamination can reduce test accuracy. - Lift tests are time-bound and may not generalize across different time periods without re-testing. - Heterogeneous geographic markets (demographic differences) reduce the precision of estimates. - Outlier events (promotions, major news, macro events) can skew results. - Concurrent test analysis is supported but automated optimal design for multi-cell tests is not currently available. - A documented login/loading issue exists with a provided workaround (use a new browser or incognito mode).

Scale and operational limitations

In large enterprises, scaling geo tests across many markets and channels requires programmatic governance (test calendar, creative constraints, and cross-functional coordination).
For small organizations, statistical power and wide confidence intervals may be the primary gating constraint.

Roadmap items

Vendor documentation indicates plans to expand multi-cell capabilities but does not provide a specific timeline.

02Why they buy

Observed differentiators (vendor-provided evidence)

Self-service GeoLift with published pricing and an extended free trial provides a lower-cost entry path to geo experimentation relative to some competitors that are predominantly sales-led.
Emphasis on model validation: the vendor frames MMM as requiring predictive validation against holdout data to be considered reliable, which is a stronger procedural stance than vendors focused solely on explanatory fit.
Methodology flexibility: the vendor presents options to use commonly used open-source methods versus a proprietary algorithm (REBA) to accommodate different buyer preferences.

- Self-service GeoLift with published pricing and an extended free trial provides a lower-cost entry path to geo experimentation relative to some competitors that are predominantly sales-led. - Emphasis on model validation: the vendor frames MMM as requiring predictive validation against holdout data to be considered reliable, which is a stronger procedural stance than vendors focused solely on explanatory fit. - Methodology flexibility: the vendor presents options to use commonly used open-source methods versus a proprietary algorithm (REBA) to accommodate different buyer preferences.

Competitor advantages

Competitors may have larger review footprints, greater brand recognition in practitioner communities, more mature reference programs, or different managed-service engagement models.

03Field reports

Availability of third-party reviews

A major review platform listing exists but shows an unpopulated review footprint as of the time of research. Buyers cannot rely on a populated review presence from that source at this time.

Community sentiment (incrementality/MMM)

Community discussions include both interest and skepticism; many threads discuss incrementality concepts rather than detailed product-level implementation feedback. Representative short excerpts from community posts indicate curiosity about the product's geo lift tool and mixed practitioner opinions about Bayesian MMM approaches. Vendor-posted content in social/community channels exists and should be treated as vendor-distributed information rather than independent endorsement.

Review platform findings

Credible pages on major review platforms (e.g., G2, TrustRadius, Capterra) specific to this vendor were not identified in accessible results during this research run.
Searches surfaced unrelated brands with the same name, creating a brand-name collision risk when researching third-party reviews.

Inferred reasons for vendor selection

Based on vendor positioning and practitioner discussions, potential reasons organizations may adopt the vendor include:

Desire to validate and triangulate platform-attributed ROAS using controlled experiments.
Need for geo-based testing and a modeling option without developing in-house capabilities.
Preference for vendors that provide detailed methodological explanations and limitations.

Inferred reasons for churn or non-purchase

Given the limited independent review footprint, reasons organizations might not adopt or might leave include:

Insufficient geographic granularity, spend, or conversion volume to produce actionable confidence intervals.
Operational constraints that prevent effective holdouts (spillover, border effects, VPNs, syndicated media).
Preference for vendors with more extensive enterprise security documentation, broader integration sets, or a more established review presence.

Given the limited independent review footprint, reasons organizations might not adopt or might leave include: - Insufficient geographic granularity, spend, or conversion volume to produce actionable confidence intervals. - Operational constraints that prevent effective holdouts (spillover, border effects, VPNs, syndicated media). - Preference for vendors with more extensive enterprise security documentation, broader integration sets, or a more established review presence.

Vendor case studies and customer logos

The vendor references case studies and displays recognizable customer logos. Full case study contents were not accessible in the materials collected for this report.

Critical evaluation guidance for buyers: vendor case studies commonly highlight positive outcomes and may omit implementation challenges, time-to-value delays, or inconclusive experiments. If ROI or lift numbers are presented, procurement teams should request raw assumptions, baseline definitions, confidence intervals, and details on how results were triangulated across experiments and modeling.

The vendor references case studies and displays recognizable customer logos. Full case study contents were not accessible in the materials collected for this report. Critical evaluation guidance for buyers: vendor case studies commonly highlight positive outcomes and may omit implementation challenges, time-to-value delays, or inconclusive experiments. If ROI or lift numbers are presented, procurement teams should request raw assumptions, baseline definitions, confidence intervals, and details on how results were triangulated across experiments and modeling.

Independent media coverage

Industry trade coverage of the product launch was available and included practitioner commentary. Such coverage is closer to independent reporting than vendor marketing but typically relies on interview statements rather than neutral benchmark studies.

04Best fit

Company size

Employees: predominantly mid-market to large organizations for full MMM value; GeoLift may be suitable for smaller organizations with sufficient signal.
Marketing spend: enough to run geo tests with reliable statistical results, which generally implies meaningful conversion volume and the ability to implement holdouts.

Recast targets mid-market to enterprise organizations, though the self-service GeoLift offering can be appropriate for smaller teams that meet the data and operational requirements for geo testing. Practical ICP guidance: - Employees: predominantly mid-market to large organizations for full MMM value; GeoLift may be suitable for smaller organizations with sufficient signal. - Marketing spend: enough to run geo tests with reliable statistical results, which generally implies meaningful conversion volume and the ability to implement holdouts.

Industries / verticals

Vendor materials cite use cases in CPG, fintech, and pharmaceuticals, and in businesses with mixed online/offline channels.

Roles and personas

Marketing leadership (e.g., VP Growth, CMO)
Performance marketing managers
Marketing science, analytics, and data science teams
Finance and FP&A stakeholders involved in budget planning and accountability

Pain points addressed

Verification and triangulation of platform-attributed ROAS using controlled experiments
Need to forecast outcomes and plan budgets under uncertainty
Measurement of offline or upper-funnel channels and interaction effects

Qualification criteria (good fit)

Ability to run geo-targeted campaigns with sufficiently clean boundaries
Historical outcome data by geo/time with consistent measurement
Organizational readiness to act on experimental results (e.g., budget reallocation, test cadence governance)

05The pitch

What the product does (core value proposition)

Recast is an incrementality-focused marketing measurement platform that combines:

Media Mix Modeling (MMM): a proprietary Bayesian MMM intended to estimate incremental impact across channels and business drivers.
Geo-based incrementality experiments (GeoLift by Recast): a self-service tool to design and analyze geo lift tests using synthetic control methods.

The vendor positions the product as providing forecasting and budget optimization informed by incrementality experiments and MMM, with an emphasis on frequent model validation and forecast accuracy rather than retrospective-only reporting. The vendor messaging critiques platform-reported attribution (for example, modeled conversions) and emphasizes causal measurement and validation.

Recast is an incrementality-focused marketing measurement platform that combines: - Media Mix Modeling (MMM): a proprietary Bayesian MMM intended to estimate incremental impact across channels and business drivers. - Geo-based incrementality experiments (GeoLift by Recast): a self-service tool to design and analyze geo lift tests using synthetic control methods. The vendor positions the product as providing forecasting and budget optimization informed by incrementality experiments and MMM, with an emphasis on frequent model validation and forecast accuracy rather than retrospective-only reporting. The vendor messaging critiques platform-reported attribution (for example, modeled conversions) and emphasizes causal measurement and validation.

Target market and positioning

Recast markets itself primarily to:

Mid-market to enterprise brands (the vendor cites categories such as large enterprise, CPG, fintech and pharmaceutical brands).
Teams that require cross-channel measurement (including offline/upper-funnel channels) and seek alignment between marketing and finance planning.

Positioning highlights include a "forecasting-first" approach to MMM for planning, pacing, and goal tracking, and an integrated incrementality workflow described as planning → experimentation → validation → optimization.

Recast markets itself primarily to: - Mid-market to enterprise brands (the vendor cites categories such as large enterprise, CPG, fintech and pharmaceutical brands). - Teams that require cross-channel measurement (including offline/upper-funnel channels) and seek alignment between marketing and finance planning. Positioning highlights include a "forecasting-first" approach to MMM for planning, pacing, and goal tracking, and an integrated incrementality workflow described as planning → experimentation → validation → optimization.

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