PyMC Marketing: Open Source Marketing Analytics Solution#

Unlock the power of marketing analytics with PyMC-Marketing – the python based open source solution for smarter decision-making. Marketing mix modeling and customer lifetime value modules allow businesses to make data-driven decisions about their marketing campaigns. Optimize your marketing strategy and unlock the full potential of your customer data.

Checkout the video below to see how Bolt leverages PyMC Marketing to assess the impact of their marketing efforts.


Install and activate an environment (e.g. marketing_env) with the pymc-marketing package from conda-forge. It may look something like the following:

conda create -c conda-forge -n marketing_env pymc-marketing
conda activate marketing_env

Installation for developers#

If you are a developer of pymc-marketing, or want to start contributing, refer to the contributing guide to get started.

See the official PyMC installation guide if more detail is needed.


Create a new Jupyter notebook with either JupyterLab or VS Code.

JupyterLab Notebook#

After installing the pymc-marketing package (see above), run the following with marketing_env activated:

conda install -c conda-forge jupyterlab
jupyter lab

VS Code Notebook#

After installing the pymc-marketing package (see above), run the following with marketing_env activated:

conda install -c conda-forge ipykernel

Start VS Code and ensure that the “Jupyter” extension is installed. Press Ctrl + Shift + P and type “Python: Select Interpreter”. Ensure that marketing_env is selected. Press Ctrl + Shift + P and type “Create: New Jupyter Notebook”.

MMM Quickstart#

import pandas as pd
from pymc_marketing.mmm import DelayedSaturatedMMM

data_url = ""
data = pd.read_csv(data_url, parse_dates=['date_week'])

mmm = DelayedSaturatedMMM(
    channel_columns=["x1", "x2"],

Initiate fitting and get a visualization of some of the outputs with:

X = data.drop('y',axis=1)
y = data['y'],y)

See the Example notebooks section for examples of further types of plot you can get, as well as introspect the results of the fitting.

CLV Quickstart#

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from pymc_marketing import clv

data_url = ""
data = pd.read_csv(data_url)
data['customer_id'] = data.index

beta_geo_model = clv.BetaGeoModel(
    data = data

Once fitted, we can use the model to predict the number of future purchases for known customers, the probability that they are still alive, and get various visualizations plotted. See the Examples section for more on this.

📞 Schedule a Free Consultation for MMM & CLV Strategy#

Maximize your marketing ROI with a free 30-minute strategy session with our PyMC-Marketing experts. Learn how Bayesian Marketing Mix Modeling and Customer Lifetime Value analytics can boost your organization by making smarter, data-driven decisions.

For businesses looking to integrate PyMC-Marketing into their operational framework, PyMC Labs offers expert consulting and training. Our team is proficient in state-of-the-art Bayesian modeling techniques, with a focus on Marketing Mix Models (MMMs) and Customer Lifetime Value (CLV). Explore these topics further by watching our video on Bayesian Marketing Mix Models: State of the Art.

We provide the following professional services:

  • Custom Models: We tailor niche marketing anayltics models to fit your organization’s unique needs.

  • Build Within PyMC-Marketing: Our team are experts leveraging the capabilities of PyMC-Marketing to create robust marketing models for precise insights.

  • SLA & Coaching: Get guaranteed support levels and personalized coaching to ensure your team is well-equipped and confident in using our tools and approaches.

  • SaaS Solutions: Harness the power of our state-of-the-art software solutions to streamline your data-driven marketing initiatives.


This repository is supported by PyMC Labs.

For companies that want to use PyMC-Marketing in production, PyMC Labs is available for consulting and training. We can help you build and deploy your models in production. We have experience with cutting edge Bayesian modelling techniques which we have applied to a range of business domains including marketing analytics.

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