Registry / ai-ml / pymc-marketing

pymc-marketing

library0.19.4pypypi✓ verified 87d ago

A Python library for marketing statistical models built on top of PyMC. It provides Bayesian approaches to marketing mix modeling (MMM), customer lifetime value (CLV), and other marketing analytics. Current version 0.19.3, requires Python >=3.12. Released under active development with frequent updates.

ai-ml data

pip install pymc-marketing

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · PYMC-MARKETING

pymc-marketing

ai-mlpythonv0.19.4

Install

29.1s avg

Import

12317ms

Disk

687MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.15.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 29.1s · import 9.854s · 694MB

687MB installed

● package 687MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

MMM

✓ from pymc_marketing.mmm import MMM

✗ from pymc_marketing import MMM

MMM is in the mmm submodule, not the top-level package.

CLVModel

✓ from pymc_marketing.clv import CLVModel

Fits a Bayesian Marketing Mix Model using PyMC.

import pandas as pd from pymc_marketing.mmm import MMM # Load some data (replace with real data) data = pd.DataFrame({ 'date': pd.date_range('2022-01-01', periods=100), 'spend': np.random.uniform(100, 1000, 100), 'sales': np.random.normal(5000, 500, 100) }) # Create model with dummy priors mmm = MMM( date_column='date', channel_columns=['spend'], control_columns=[], adstock_max_lag=4, yearly_seasonality=2 ) # Sample (small draws for quick demo) mmm.fit(data, draws=100, tune=100, chains=1) print(mmm.summary())

Debug

Known issues

breakingVersion 0.19.x introduced breaking changes in the MMM API: the `adstock_max_lag` and `yearly_seasonality` parameters are now required and the default prior distributions changed.

fix

Update code to include explicit adstock_max_lag and yearly_seasonality parameters; review prior specifications.

affects: <0.19

gotchaThe MMM model requires a DataFrame with a date column; the column must be in datetime format. Passing strings will cause an error.

fix

Ensure date column is converted to datetime: data['date'] = pd.to_datetime(data['date'])

affects: all

gotchaWhen using the CLV module, the `CLVModel` expects a specific data shape with columns 'frequency', 'recency', 'T', and 'monetary_value'. Missing columns will raise a KeyError.

fix

Verify data has the required columns before fitting.

affects: all

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'pymc_marketing'

Package not installed or installed under a different name.

fix

Run: pip install pymc-marketing

AttributeError: module 'pymc_marketing' has no attribute 'MMM'

MMM is not imported directly from the top-level package; it's in a submodule.

fix

Use: from pymc_marketing.mmm import MMM

ValueError: Date column must be datetime64[ns] dtype

The date column is not in datetime format.

fix

Convert the column to datetime: data['date'] = pd.to_datetime(data['date'])

Upgrade

Version history

0.19.4latest on PyPI · released May 6, 2026

Audit

Dependencies

pymcrequiredCore probabilistic programming framework

arvizrequiredBayesian model diagnostics and visualization

xarrayrequiredData structures for MCMC samples

pandasrequiredData manipulation

Agent activity

12 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/pymc-labs/pymc-marketing ↗

packagehttps://pypi.org/project/pymc-marketing/ ↗

homepagehttps://www.pymc-marketing.io ↗