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AI-powered tool to build and monetize your X (Twitter) audience.

Fast, accurate forecasting powered by incrementality experiments and MMM.

Recast is a marketing mix modeling (MMM) platform designed for complex businesses, particularly Fortune 500, CPG, fintech, and pharmaceutical brands. It uses proprietary Bayesian MMM and universal lift test calibration to deliver accurate forecasts, validated weekly. The platform emphasizes incrementality measurement, allowing users to validate forecasts and make data-driven decisions. Recast's models handle the complexities of modern marketing, adapting to spend shifts, campaign launches, and evolving strategies. It provides a complete toolkit for predicting outcomes before spending, testing strategies risk-free, monitoring performance in real-time, and reducing uncertainty. Key features include time-varying ROIs, promotional event modeling, and upper/lower funnel interaction analysis. Recast also offers multi-stage MMMs for understanding the down-funnel impact of marketing efforts.
Recast is a marketing mix modeling (MMM) platform designed for complex businesses, particularly Fortune 500, CPG, fintech, and pharmaceutical brands.
Explore all tools that specialize in incrementality measurement. This domain focus ensures Recast delivers optimized results for this specific requirement.
Recast models marketing performance changes dynamically over time, incorporating seasonality on a per-channel basis using a novel model specification.
Models the impact of promotional spikes, estimating pull-forward and pull-backward effects in addition to the immediate impact.
Accounts for the relationship between upper-funnel channels (e.g., television) and lower-funnel channels (e.g., branded search).
Combines Recast’s MMM with a Kaplan-Meier style survival model to understand the down-funnel impact of marketing efforts in multi-stage conversion funnels.
Recast uses universal lift test calibration to continuously improve the accuracy of its forecasts.
1. Data Integration: Connect marketing and sales data sources to Recast.
2. Model Configuration: Configure the Bayesian MMM model with relevant parameters.
3. Calibration: Calibrate the model using universal lift tests.
4. Validation: Validate the model's accuracy using historical data.
5. Dashboard Setup: Set up live dashboards to monitor performance and forecasts.
6. Scenario Analysis: Run scenario analyses to test different spend flights.
7. Training: Recast provides training on using the platform and interpreting the results.
All Set
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"Recast is highly regarded for its accurate forecasting, incrementality measurement, and ability to handle complex marketing scenarios."
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