Développement Fullstack

End-to-End Marketing Mix Model Architecture: From Data Pipeline to Optimization API

A comprehensive guide to deploying production-ready Marketing Mix Models—covering data ingestion, model training, cloud deployment, and real-time optimization APIs for budget allocation

Par

Cyril Noirot

15 octobre 2025

9 min de lecture

Deploying a Marketing Mix Model (MMM) in production requires orchestrating multiple complex systems: data pipelines that aggregate marketing spend and sales data, statistical models that quantify channel effectiveness, cloud infrastructure for model serving, and optimization APIs that enable real-time budget allocation decisions.

This guide presents a battle-tested architecture that scales from startup to enterprise, handling millions of data points across hundreds of campaigns while providing sub-second optimization responses.

graph TB subgraph DataSources["Data Sources"] GA[Google Ads] FB[Facebook Ads] TV[TV Agency] RT[Retail Sales] WE[Weather API] EC[Economic Data] end

subgraph Pipeline["Data Pipeline"] ET[ETL/ELT Layer] DW[Data WarehouseSnowflake/BigQuery] DQ[Data QualityDashboard] end

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.

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