Lakshmipathiraju Pericharla

Marketing Mix Modeling (MMM) Platform

Creating comprehensive MMM solution using Bayesian methods and hierarchical modeling.

PythonBayesian StatisticsHierarchical ModelingMarketing Analytics

Project Overview

This project involves developing a sophisticated Marketing Mix Modeling platform that uses Bayesian statistics and hierarchical modeling to optimize marketing spend allocation across channels. The platform measures both short-term and long-term brand effects, providing actionable insights for marketing budget optimization.

Key Features

  • Bayesian hierarchical modeling for robust estimates
  • Multi-channel attribution and optimization
  • Long-term brand effect measurement
  • Scenario planning and budget optimization
  • Automated report generation
  • Interactive visualization dashboards

Challenges

  • Handling multicollinearity between marketing channels
  • Accounting for carryover and saturation effects
  • Incorporating external factors and seasonality
  • Ensuring model stability across different markets
  • Scaling to large numbers of products and markets

Solutions

  • Implemented ridge regression with Bayesian priors
  • Used distributed lag models for carryover effects
  • Created comprehensive external factor integration
  • Developed market-specific hierarchical structures
  • Built scalable computation using MCMC sampling

View Source Code

Interested in the technical implementation? Check out the complete source code on GitHub.

View on GitHub