Handle challenges in MMM/MMX

Q: Handle challenges in MMM/MMX

This question evaluates a data scientist's competency in applied Machine Learning and causal inference for marketing-mix modeling, focusing on diagnosing model fragility from multicollinearity, endogeneity, non‑stationarity, measurement error, and decisions around adstock, saturation and promotion effects.

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Question

MMM Fragility Diagnosis and Remediation Plan (Weekly, 156 Weeks)

Context

You inherit a weekly Marketing Mix Model (MMM/MMX) with 156 weeks of data. The outcome is weekly business performance (e.g., sales or conversions). Candidate drivers include:

TV GRPs
Paid Search Spend
Display Spend
Email Sends
Price
Promotions
Competitor Index

Additional facts:

TV and Paid Search are highly correlated (r = 0.90).
There is a pandemic-related structural break beginning in week 70.

Task

Identify why this model is fragile given the setup.
Propose concrete remedies, being specific about:
- Endogeneity and omitted variables
- Multicollinearity remedies (priors, ridge/LASSO, hierarchical Bayesian)
- Adstock/lag and saturation choices
- Non-stationarity and change points
- Promotion cannibalization
- Privacy-induced measurement error (e.g., ATT)
- Calibration using randomized geo-tests
Describe a validation plan (out-of-time fit, lift alignment, posterior predictive checks).
Explain how you would produce robust ROI and budget recommendations with uncertainty.

Handle challenges in MMM/MMX

MMM Fragility Diagnosis and Remediation Plan (Weekly, 156 Weeks)

Context

Task

Solution

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Handle challenges in MMM/MMX

Overview

MMM Fragility Diagnosis and Remediation Plan (Weekly, 156 Weeks)

Context

Task

Solution

Comments (0)