Marketing Mix Modeling (MMM) has become essential for understanding the true impact of your marketing investments. Here's a structured breakdown of what's actually involved in deploying an MMM solution, based on real-world implementation patterns.
Before any data touches your hands, you need organizational buy-in. This means aligning with marketing leadership, finance, and analytics teams on objectives, expected outcomes, and success metrics. Define what questions the model needs to answer: ROI by channel? Optimal budget allocation? Incrementality measurement?
This is where theory meets reality. You're gathering advertising spend, impressions, clicks, and conversions across every channel—paid search, social, display, TV, radio, out-of-home. The challenge isn't just volume; it's dealing with inconsistent naming conventions, different attribution windows, and platform-specific metrics.
Meta, Google, TikTok, programmatic DSPs—each has its own API quirks and data schemas. You're building robust extraction pipelines that handle rate limits, authentication refreshes, and historical data backfills. This isn't a one-time pull; it needs to be repeatable and maintainable.
À propos de l'auteur
Cyril Noirot
Lead Data Scientist
Data scientist freelance. Je conçois et déploie des systèmes de décision — prévision, pricing, marketing measurement, optimisation.