Pymc Marketing Mmm Clv
- 799 installs
- 6 repo stars
- Updated July 18, 2026
- aradotso/marketing-skills
pymc-marketing-mmm-clv is an agent skill that builds Bayesian marketing mix models, customer lifetime value forecasts, and BTYD retention models with pymc-marketing for channel ROI and budget allocation.
About
pymc-marketing-mmm-clv is an agent skill from marketing-skills that walks developers through Bayesian marketing mix modeling (MMM), customer lifetime value (CLV), and buy-till-you-die (BTYD) retention models using the pymc-marketing Python library. Instead of last-click attribution, Bayesian MMM estimates channel contribution with uncertainty intervals, while CLV and BTYD models forecast future customer value from transaction histories. The skill fits data scientists and growth engineers who need reproducible Python notebooks or pipelines for budget reallocation, cohort forecasting, and ROI reporting. Reach for pymc-marketing-mmm-clv when pymc-marketing is the chosen stack and the task is probabilistic channel attribution—not spreadsheet pivots.
- Media Mix Modeling with geometric adstock and logistic saturation
- CLV models including Beta-Geometric/NBD and Gamma-Gamma components
- Budget optimization and lift-test calibration for channel ROI
- Conda, pip, and Docker install paths for pymc-marketing
Pymc Marketing Mmm Clv by the numbers
- 799 all-time installs (skills.sh)
- +2 installs in the week ending Jul 27, 2026 (Skillselion tracking)
- Ranked #386 of 2,066 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
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| Installs | 799 |
|---|---|
| repo stars | ★ 6 |
| Last updated | July 18, 2026 |
| Repository | aradotso/marketing-skills ↗ |
How do you build Bayesian MMM with pymc-marketing?
Build Bayesian MMM, CLV, and BTYD models with pymc-marketing for channel ROI, budget allocation, and customer value forecasts.
Who is it for?
Data scientists and growth engineers who run probabilistic marketing attribution and CLV forecasting in Python with pymc-marketing.
Skip if: Developers who only need simple SQL dashboards or deterministic last-click attribution without Bayesian inference.
When should I use this skill?
The developer asks to model marketing channel ROI, estimate CLV, or fit BTYD retention models with pymc-marketing.
What you get
Fitted MMM, CLV, and BTYD models with posterior samples, channel ROI estimates, and budget allocation forecasts.
- Fitted MMM model
- CLV forecast outputs
- BTYD retention estimates
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What models does pymc-marketing-mmm-clv build?
pymc-marketing-mmm-clv builds Bayesian marketing mix models (MMM), customer lifetime value (CLV) forecasts, and buy-till-you-die (BTYD) retention models using the pymc-marketing Python library for channel ROI and budget allocation.
When should developers use pymc-marketing-mmm-clv?
Developers should use pymc-marketing-mmm-clv when they need probabilistic channel attribution, CLV forecasting, or retention modeling in Python—not simple last-click reports. The skill targets pymc-marketing workflows with spend and transaction datasets.