Understanding Adstock and Carryover Effects
How adstock transformations capture the lingering effect of advertising on consumer behavior over time.
Marketing Mix Modeling
Marketing Mix Modeling (MMM) techniques for measuring marketing effectiveness in product and service-focused industries.
📚 Knowledge Base
View all →Introduction to Marketing Mix Modeling
A comprehensive overview of Marketing Mix Modeling — what it is, why it matters, and how it helps businesses optimize marketing spend.
🎓 Tutorials
View all →Building Your First Marketing Mix Model in Python
A practical, step-by-step guide to building an MMM using Python — from data preparation to interpreting results.
📄 Documentation
View all →Data Requirements for Marketing Mix Modeling
Data Requirements A comprehensive guide to the data needed for building robust marketing mix models. Minimum Data Requirements Time series length: At least 2 years…
📜 White Papers
View all →Optimizing Media ROI in CPG: A Marketing Mix Modeling Approach
This paper demonstrates how a leading CPG company used Bayesian Marketing Mix Modeling to reallocate $50M in media spend, achieving a 23% improvement in marketing…
▶️ Video Training
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Marketing Mix Modeling: From Data to Decisions
A 1-minute crash course on Marketing Mix Modeling — understand how MMM measures marketing effectiveness.
🔧 Tools
View all →Meta Robyn
Meta's open-source automated Marketing Mix Modeling package for R with Nevergrad optimizer.
Google Meridian
Google's open-source Bayesian Marketing Mix Model built in Python with built-in support for reach and frequency data.