PyMC 中的贝叶斯营销工具箱。包含媒体组合(MMM)、客户生命周期价值(CLV)、购买至死亡(BTYD)模型等。
media-mix-modeling 开源项目推荐(31 个)
media-mix-modeling 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 31 个仓库Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
该项目在 GitHub 上没有填写描述
(ml) - python implementation of bayesian media mix modelling with shape and carryover effect
Ask your WorkMagic marketing data anything — attribution, lift tests and budget plans in Claude, ChatGPT, Codex, Cursor, Copilot and Gemini.
Media Mix Model with simulated data and stan
Analysing the challenges and opportunities in Media Mix Modelling using sales and media spending time series data.
A self-contained Jupyter notebook covering Marketing Mix Modeling from theory to implementation, designed for data scientists.
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 Marketing Mix Modeling & Omnichannel Optimization — PepsiCo
A full-stack Marketing Mix Model (MMM) for a simulated DTC retail e-commerce brand (ShopNova), built from scratch in Python with an interactive React dashboard.
Interactive version of Daniel Saunder's blog post
Bayesian media mix model that shows its work: Stan model + dependency-free Swift engine with parameter recovery, holdout grading, and convergence gates
🛡️ Hybrid data ingestion pipeline: Elixir concurrency + Rust safety via Rustler NIFs. Built for MarTech AI pipelines with PII sanitization and prompt injection detection.
Healthcare lead-generation Marketing Mix Modeling reference implementation with PyMC-Marketing, adstock, saturation, ROI/mROAS, Streamlit dashboard, and budget allocation.
A production-grade dbt (Data Build Tool) project for advertising analytics with models, tests, snapshots, analyses, macros, and YAML documentation for DSPs (DV360, Amazon, TTD, Yahoo), ad servers (DCM), and Salesforce campaign data.
Lightweight Marketing Mix Model: adstock + saturation + regression. No Meridian, no PyMC — just numpy and scikit-learn. A simpler alternative to Google Meridian for quick MMM analysis.
Production-grade Bayesian Media Mix Model with adstock transformation, saturation curves, and budget optimization
Stan-style Bayesian marketing mix modeling in JAX.
Feature engineering for marketing mix modelling and multi-touch attribution in R. Geometric and Weibull adstock, four saturation curves, carryover selected by cross-validation against your KPI, and customer journeys built from raw event logs.
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/media-mix-modeling。
- 该话题在 GitHub 上共有 31 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 1893 Star,覆盖 8 种主要语言,数据更新于 2026-11-14。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。