Marketing Analytics

Bayesian Statistics

Machine Learning

Marketing Measurement

Digital Marketing

Media Mix Modeling

Causal Inference

Marketing Mix Modeling

Explore recommended reads, summarized by AI

Technical Methods and Implementation

Approaches to building, calibrating, and implementing marketing mix models using statistical and programming tools.

Bayesian and Python Implementations

Causal Inference and Methodological Issues

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Brian James Curry

Marketing Mix Modeling You Can Check: Adstock, Saturation and ROAS Against a Known Truth

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Popular this week

Brian James Curry

Marketing Mix Modeling You Can Check: Adstock, Saturation and ROAS Against a Known Truth

Brian James Curry

Papilon 0.3.0:

In

Data And Beyond

by

Dima Iakubovskyi

Marketing Mix Models 101: When Your TV Ad Did Not Return 21x, but Christmas Did

In

The Academic

by

Diogo Ribeiro

The Budget Was Never Randomized: What a Marketing Mix Model Can and Cannot Tell You

In

Data And Beyond

by

Smit Malik

When MMM and Incrementality Disagree: What Is Each Actually Telling Us?

Latest stories

Brian James Curry

Marketing Mix Modeling You Can Check: Adstock, Saturation and ROAS Against a Known Truth

Brian James Curry

Papilon 0.3.0:

Brian James Curry

Your Best-Looking Ad Channel May Be Doing the Least: A CMO’s Guide to Marketing Mix Modeling

In

Startup Stash

by

Kshitij Marwah

Your Branded Search Ads May Be Claiming Sales You’d Get Anyway

In

Data And Beyond

by

Dima Iakubovskyi

Marketing Mix Models 101: When Your TV Ad Did Not Return 21x, but Christmas Did

See more stories

Who to follow