Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

R package for Customer Behavior Analysis

🚨 Resources 💼 to learn/practice 🎯 Marketing analytics 💹 🚨

A curated list of awesome marketing science resources including geo incrementality testing, media mix models, multi-touch attribution, causal inference, and more from shakostats.com . Star ⭐ the repo if it helps you, and feel free to contribute your own favorite resources

Cluster multilingual search terms captured from different time windows into semantically relevant topics.

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

A New GeneratUsing Robyn aims to reduce human bias in the modeling process, esp. by automating modelers decisions like adstocking, saturation, trend & seasonality as well as model validation. Moreover, the budget allocator & calibration enable actionability and causality of the results

This repository provides open-source best practices for for conducting geographic randomized controlled trials (Geo RCTs) for measuring incremental sales effect of advertising cammpaigns. It includes details on one design type in particular, a multi-armed stepped experimental design that has particular advantages in terms of statistical strength.

Simba — Bayesian Marketing Mix Modeling (MMM) platform. Media attribution, budget optimization, incrementality measurement, and scenario planning. Built on PyMC-Marketing. No-code, fully transparent, enterprise-ready.

10-paper marketing science framework with 11 live dashboards. Bayesian MMM, causal inference, probabilistic identity resolution, and real-time streaming attribution. All papers published with Zenodo DOIs.

End-to-end machine learning pipeline to predict seller churn for the Olist e-commerce

Customer churn prediction model (Logistic Regression) in Python, scoring and retention action CLI built in Go. / Modelo de prediccion de fuga de clientes con Python y CLI de retencion en Go.

AI Strategist, Marketing Economist & Applied Researcher. Founder, Vector1 Research. Enterprise analytics leadership, causal inference, cognitive AI research.

Customer segmentation using RFM scoring and K-Means clustering. Polyglot project: SQL for data extraction, Python for ML, Go for the segment reporter CLI. / Segmentacion de clientes con RFM y K-Means. Proyecto multilenguaje: SQL, Python y Go.

End-to-end A/B Testing framework with Bayesian and Frequentist analysis. Built for Marketing Science. / Dashboard interactivo de Pruebas A/B con analisis frecuentista y bayesiano.

Command-line tool to compute marketing KPIs (CTR, CVR, ROAS, CPA, CPC) from CSV campaign data, built in Go. / Herramienta CLI para calcular KPIs de marketing desde datos CSV, construida en Go.

8-dimensional brand perception framework. AI-implementable prompt toolkit + YAML templates. Demonstrated across 5 brands.