Marketing Science

Mixed Integer Programming for MMM Budget Optimization

Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.

Analytics TeamSeptember 9, 20255 min read

Analytics Team

Analytics consultant specializing in marketing science, forecasting, and strategic data solutions. Passionate about bridging the gap between advanced analytics and business impact.

Marketing Science

Part 3 | Hands-On Implementation: Building a Causal MMM from Scratch

A practical tutorial implementing causal marketing mix modeling with DoWhy, including code examples and a complete toy dataset

Aug 281 min

Marketing Science

Practical optimizer scenarios in MMM: A step-by-step guide

Walk through real-world MMM optimization scenarios using Mixed Integer Programming. See how to discretize response curves, formulate constraints, and solve practical reallocation problems with actual numbers.

Aug 2116 min

Marketing Science

Part 2 | Beyond Attribution: How Causal Inference complements Modern MMM

Exploring structural causal models using DAGs, counterfactual reasoning, and frameworks like DoWhy and PyMC-Marketing

Aug 208 min

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Part 3 | Hands-On Implementation: Building a Causal MMM from Scratch

Practical optimizer scenarios in MMM: A step-by-step guide

Part 2 | Beyond Attribution: How Causal Inference complements Modern MMM