PyMC-Marketing: Bayesian Marketing Mix Models and Customer Analytics in Python

Résumé fourni par la source

PyMC-Marketing is a comprehensive Python library implementing Bayesian marketing analytics, built on PyMC (Abril-Pla et al., 2023).Commercial marketing analytics tools typically provide limited transparency into their models, while open-source alternatives like Meta's Robyn and Google's Meridian focus primarily on media mix modeling (Google Inc., 2023; Meta Platforms Inc., 2022).PyMC-Marketing provides a unified framework spanning multiple marketing domains, including: Marketing Mix Models (MMMs), Customer Lifetime Value (CLV) analysis, Bass Diffusion Models, and Customer Choice Models.All outputs include full posterior distributions rather than point estimates, enabling explicit risk assessment in business decisions.Key capabilities include time-varying media coefficients via Hilbert space Gaussian process approximations (Solin & Särkkä, 2020), experimental calibration integrating lift tests into model likelihood, hierarchical Buy-Till-You-Die (BTYD) models (Fader & Hardie, 2009) for CLV, and budget optimization with business constraints.The library is available on PyPI and conda-forge, with comprehensive documentation and example notebooks at https://www.pymc-marketing.io.

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