Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

Karpiu is a package designed for marketing mix modeling by calling Orbit from the backend. Karpiu is still in its beta version. Please use it at your own risk.

PySiMMMulator is an open source Python marketing simulation package, which allows users to generate simulated data to use in testing Marketing Mix Models (MMMs).

The project analyses the impact of different marketing tactics on the sales of items. The problem is a multivariate-modeling problem as there are 3 different tactics of marketing. Since, the impact of marketing medium cannot be negative we will be using Bayesian model for regression.

Marketing Mix Model expert agent plugin for Claude Code - pymc-marketing v0.18.2+

A web dashboard for Bayesian MMM. It fits on sample marketing data then explores channel effects, budget trade-offs and optimisation in an interactive UI.

🛡️ Catch flipped labels, temporal leaks & causal biases before they kill your fraud/marketing models

MCP server for SIMBA — connect AI assistants (Claude, Cursor) to Bayesian Marketing Mix Models. Upload data, build models, optimize budgets, and run scenarios through natural language.

Open-source unified MMM interface

📊 Analyze customer behavior using K-Means clustering, segmenting by income and spending score to uncover insights and enhance business strategies.

Interactive version of Daniel Saunder's blog post

Marketing Mix Modeling (MMM) in Python: ridge regression with adstock & saturation, attribution metrics, and a simple budget optimizer.

marketing-mix-model implementation

A Streamlit web app that predicts weekly product sales based on advertising budgets for TV, Radio, and Newspaper using a 2‑degree Polynomial Regression model. The model captures the non‑linear, diminishing‑returns effect of media spend, helping marketers optimize budgets for maximum ROI.

Agentic causal inference system for Marketing Mix Model explanation — powered by LFM2.5

A marketing analytics tool combining Causal Inference, Marketing Mix Modeling (MMM), and RFM Segmentation to measure the true sales impact of a retail promotional campaign.

Synthetic-persona Bayesian simulator that turns plain-English business intake into a ready-to-paste Meta Ads Manager campaign — Hermes LLM swarm + behavioral conjoint + portfolio math.

Federated Bayesian MMM where competing participants train local models without sharing raw data — LLM-elicited priors guide each round, differential privacy bounds what is shared, and synthetic control validates causal channel effects.

Bayesian marketing mix modeling, multi-touch attribution, and causal inference toolkit for unified marketing measurement and incrementality analysis.

India-market Marketing Mix Modeling on 104 weeks of synthetic weekly spend data across 6 channels — recovering channel ROAS with adstock + saturation and recommending a quarterly budget reallocation plan.