Recast's checklist template of important features to consider when evaluating a media mix modeling vendor or building your own MMM in-house.| Recast
Schedule a Demo Plan, experiment, validate, and optimize marketing performance in a rigorous and transparent platform. Learn how Recast’s MMM, GeoLift and dashboard tools can help your team engage in a cycle of testing and iteration towards stronger marketing performance. Not ready for a demo? You can always reach us at: info@getrecast.com … Read More »| Recast
Facebook invented a new accuracy metric, which has the effect of throwing out models that tell you your budget was wasted!| Recast
Marketing mix modeling is hard. Trust us. When we were starting Recast, we thought it’d be easy: we’d use some off-the-shelf Bayesian time-series models and six months later we’d have a top-shelf model. But unfortunately, it didn’t work like that. It took us multiple years of PhD-level research time to … Why MMM is so hard Read More »| Recast
Bayesian MarketingMix Modeling The privacy-friendly modeling technique from the 1960s being modernized and automated by Google, Facebook, and Recast. Marketing Mix Modeling (MMM) has been around since the 1960s, used by CPG brands to help them allocate their marketing budgets across different marketing channels. Bayesian statistics have been around far … Bayesian Marketing Mix Modeling Read More »| Recast
Marketing attribution is a complex topic, and most marketers don't know all the methods available or their strengths and weaknesses.| Recast
Have you ever heard someone use the word "Bayesian", and wondered what that meant, and why it was better? You're in the right place.| Recast
Recast is built to be fully verifiable, update fully automatically (on a weekly cadence) and to be an actionable decision-making platform.| Recast
You're probably modeling seasonality the wrong way. Controlling for seasonality means underspending at peak times. Learn what to do instead.| Recast
Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.| facebookexperimental.github.io