Marketing Science - Leveraging & Benchmarking Open-Source MMM Approaches to Strengthen Marketing Mix Projects

Description: Explore how open-source MMM platforms and complementary competitive-intelligence tooling can benchmark results, stress-test assumptions, and support MASS Analytics’ algorithms and storytelling. Compare outputs across different MMM philosophies, understand divergences, and identify where MASS tools are stronger and where open-source adds value (diagnostics, uncertainty, experimentation, etc.).

Key attributes / Main competencies:

• Experience with common data science toolkits (Python, R, etc.)
• Solid understanding of econometrics concepts
• Strong Excel and Python skills
• Analytical, rigorous mindset with focus on methodological comparability

Learning outcomes:

• Map open-source MMM tools by methodology, strengths, and limitations (e.g., frequentist vs Bayesian, automation level, hierarchical models, uncertainty, calibration)
• Design a consistent benchmarking framework (same dataset/question, aligned transforms and metrics)
• Compare model behavior/outputs (contributions, ROI curves, diminishing returns, response curves, stability under collinearity, sensitivity to priors/hyperparameters)
• Build an assessment for MASS Analytics: strengths vs where open-source adds value