# pymc-labs/pymc-marketing

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

Repository: https://github.com/pymc-labs/pymc-marketing
Canonical: https://ross.abutalabs.com/products/pymc-marketing
Homepage: https://www.pymc-marketing.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: clv, data-science, marketing, mmm, python, btyd, customer-lifetime-value, media-mix-modeling, buy-till-you-die, marketing-mix-modeling
Last push: 2026-09-02T22:28:21+00:00

## Health v2 (maintenance only)
Score: 100/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 99, longevity 100
- inputs: {"age_days": 1640, "days_push": 0, "days_rel": 6, "gap_med": 18.5, "n_releases_24m": 29}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1252, forks 403 (observed 2026-09-03T02:15:17.137222+00:00)

## What it is
PyMC-Marketing is a Python library of Bayesian marketing analytics models built on PyMC, including Marketing Mix Modeling (MMM), Customer Lifetime Value (CLV), buy-till-you-die models, customer choice, and incrementality estimation. It is actively maintained by PyMC Labs and distributed under the Apache-2.0 license.

## Use cases
- build a marketing mix model to measure channel ROI
- estimate customer lifetime value from transaction data
- fit buy-till-you-die models like BG/NBD for repeat purchases
- run Bayesian media mix modeling for budget allocation
- measure marketing incrementality from experiments
- model customer choice with discrete choice or MaxDiff
- forecast adoption with Bass diffusion models

## When to choose
- you want Bayesian MMM or CLV models in Python with PyMC
- you need uncertainty quantification for marketing spend decisions
- you want an open-source, actively maintained alternative to commercial MMM tools

## When to avoid
- you need a no-code or hosted marketing analytics dashboard
- you are not comfortable with Bayesian modeling or Python
- you need real-time attribution rather than aggregate modeling

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, analytics
- domain: data-science, analytics
- platform: python
- tags: bayesian, pymc, marketing-mix-modeling, customer-lifetime-value, media-mix-model, buy-till-you-die, marketing-analytics, marketing

## Member repositories
- pymc-labs/pymc-marketing (main) score 100

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:17.137222+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:08:16.281362+00:00, confidence not recorded.
  - readme: https://github.com/pymc-labs/pymc-marketing (fetched 2026-09-03T02:15:17.137222+00:00, sha 81aeb7bc0168)
  - registry_pypi: https://pypi.org/pypi/pymc-marketing/json (fetched 2026-08-29T12:20:32.027313+00:00, sha 37d36497ffee)
- Data as of 2026-08-30T08:39:29.467469+00:00.