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----

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# <span style="color:limegreen">PyMC-Marketing</span>: Bayesian Marketing Mix Modeling (MMM) & Customer Lifetime Value (CLV)

## Marketing Analytics Tools from [PyMC Labs](https://www.pymc-labs.com)

Unlock the power of **Marketing Mix Modeling (MMM)** and **Customer Lifetime Value (CLV)** analytics with PyMC-Marketing. This open-source marketing analytics tool empowers businesses to make smarter, data-driven decisions for maximizing ROI in marketing campaigns.

----

This repository is supported by [PyMC Labs](https://www.pymc-labs.com).

<center>
    <img src="https://github.com/pymc-labs/pymc-marketing/blob/main/docs/source/_static/labs-logo-light.png" width="50%" />
</center>

For businesses looking to integrate PyMC-Marketing into their operational framework, [PyMC Labs](https://www.pymc-labs.com) 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). For more information see [here](#-schedule-a-free-consultation-for-mmm--clv-strategy).

Explore these topics further by watching our video on [Bayesian Marketing Mix Models: State of the Art](https://www.youtube.com/watch?v=xVx91prC81g).

### Community Resources

- [PyMC-Marketing Discussions](https://github.com/pymc-labs/pymc-marketing/discussions)
- [PyMC discourse](https://discourse.pymc.io/)
- [Bayesian discord server](https://discord.gg/swztKRaVKe)
- [MMM Hub Slack](https://www.mmmhub.org/slack)

## Quick Installation Guide for Marketing Mix Modeling (MMM) & CLV

To dive into MMM and CLV analytics, set up a specialized Python environment, `marketing_env`, via conda-forge:

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

For a comprehensive installation guide, refer to the [official PyMC installation documentation](https://www.pymc.io/projects/docs/en/latest/installation.html).

### Docker

We provide a `Dockerfile` to build a Docker image for PyMC-Marketing so that is accessible from a Jupyter Notebook. See [here](/scripts/docker/README.md) for more details.

## In-depth Bayesian Marketing Mix Modeling (MMM) in PyMC

Leverage our Bayesian MMM API to tailor your marketing strategies effectively. Leveraging on top of the research article [Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017)](https://research.google/pubs/pub46001/),  and extending it by integrating the expertise from core PyMC developers, our API provides:

| Feature                                    | Description                                                                                                                                                                                                                                                                                                                                                                            |
| ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Custom Priors and Likelihoods              | Tailor your model to your specific business needs by including domain knowledge via prior distributions.                                                                                                                                                                                                                                                                               |
| Adstock Transformation                     | Optimize the carry-over effects in your marketing channels.                                                                                                                                                                                                                                                                                                                            |
| Saturation Effects                         | Understand the diminishing returns in media investments.                                                                                                                                                                                                                                                                                                                               |
| Customize adstock and saturation functions | You can select from a variety of adstock and saturation functions. You can even implement your own custom functions. See [documentation guide](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_components.html).                                                                                                                                                             |
| Time-varying Intercept                     | Capture time-varying baseline contributions in your model (using modern and efficient Gaussian processes approximation methods). See [guide notebook](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_time_varying_media_example.html).                                                                                                                                      |
| Time-varying Media Contribution            | Capture time-varying media efficiency in your model (using modern and efficient Gaussian processes approximation methods). See the [guide notebook](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_tvp_example.html).                                                                                                                                                       |
| Visualization and Model Diagnostics        | Get a comprehensive view of your model's performance and insights.                                                                                                                                                                                                                                                                                                                     |
| Choose among many inference algorithms     | We provide the option to choose between various NUTS samplers (e.g. BlackJax, NumPyro and Nutpie). See the [example notebook](https://www.pymc-marketing.io/en/stable/notebooks/general/other_nuts_samplers.html) for more details.                                                                                                                                                    |
| Out-of-sample Predictions                  | Forecast future marketing performance with credible intervals. Use this for simulations and scenario planning.                                                                                                                                                                                                                                                                         |
| Budget Optimization                        | Allocate your marketing spend efficiently across various channels for maximum ROI. See the [budget optimization example notebook](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_budget_allocation_example.html)                                                                                                                                                            |
| Experiment Calibration                     | Fine-tune your model based on empirical experiments for a more unified view of marketing. See the [lift test integration explanation](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_lift_test.html) for more details. [Here](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_roas.html) you can find a *Case Study: Unobserved Confounders, ROAS and Lift Tests*. |

### MMM Quickstart

```python
import pandas as pd

from pymc_marketing.mmm import (
    GeometricAdstock,
    LogisticSaturation,
    MMM,
)

data_url = "https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/mmm_example.csv"
data = pd.read_csv(data_url, parse_dates=["date_week"])

mmm = MMM(
    adstock=GeometricAdstock(l_max=8),
    saturation=LogisticSaturation(),
    date_column="date_week",
    channel_columns=["x1", "x2"],
    control_columns=[
        "event_1",
        "event_2",
        "t",
    ],
    yearly_seasonality=2,
)
```

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

```python
X = data.drop("y",axis=1)
y = data["y"]
mmm.fit(X,y)
mmm.plot_components_contributions();
```

![](https://github.com/pymc-labs/pymc-marketing/blob/main/docs/source/_static/mmm_plot_components_contributions.png)

Once the model is fitted, we can further optimize our budget allocation as we are including diminishing returns and carry-over effects in our model.

<center>
    <img src="/docs/source/_static/mmm_plot_plot_channel_contributions_grid.png" width="80%" />
</center>

Explore a hands-on [simulated example](https://pymc-marketing.readthedocs.io/en/stable/notebooks/mmm/mmm_example.html) for more insights into MMM with PyMC-Marketing.

### Essential Reading for Marketing Mix Modeling (MMM)

- [Bayesian Media Mix Modeling for Marketing Optimization](https://www.pymc-labs.com/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization/)
- [Improving the Speed and Accuracy of Bayesian Marketing Mix Models](https://www.pymc-labs.com/blog-posts/reducing-customer-acquisition-costs-how-we-helped-optimizing-hellofreshs-marketing-budget/)
- [Johns, Michael and Wang,  Zhenyu. "A Bayesian Approach to Media Mix Modeling"](https://www.youtube.com/watch?v=UznM_-_760Y)
- [Orduz, Juan. "Media Effect Estimation with PyMC: Adstock, Saturation & Diminishing Returns"](https://juanitorduz.github.io/pymc_mmm/)
- [A Comprehensive Guide to Bayesian Marketing Mix Modeling](https://1749.io/learn/f/a-comprehensive-guide-to-bayesian-marketing-mix-modeling)

## Unlock Customer Lifetime Value (CLV) with PyMC

Understand and optimize your customer's value with our **CLV models**. Our API supports various types of CLV models, catering to both contractual and non-contractual settings, as well as continuous and discrete transaction modes.

Explore our detailed CLV examples using data from the [`lifetimes`](https://github.com/CamDavidsonPilon/lifetimes) package:

- [CLV Quickstart](https://pymc-marketing.readthedocs.io/en/stable/notebooks/clv/clv_quickstart.html)
- [BG/NBD model](https://pymc-marketing.readthedocs.io/en/stable/notebooks/clv/bg_nbd.html)
- [Pareto/NBD model](https://pymc-marketing.readthedocs.io/en/stable/notebooks/clv/pareto_nbd.html)
- [Gamma-Gamma model](https://pymc-marketing.readthedocs.io/en/stable/notebooks/clv/gamma_gamma.html)

### Examples

|                | **Non-contractual**      | **Contractual**         |
| -------------- | ------------------------ | ----------------------- |
| **Continuous** | online purchases         | ad conversion time      |
| **Discrete**   | concerts & sports events | recurring subscriptions |

### CLV Quickstart

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

data_url = "https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/clv_quickstart.csv"
data = pd.read_csv(data_url)
data["customer_id"] = data.index

beta_geo_model = clv.BetaGeoModel(data=data)

beta_geo_model.fit()
```

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.

![](https://github.com/pymc-labs/pymc-marketing/blob/main/docs/source/_static/expected_purchases.png)

See the Examples section for more on this.

## Why PyMC-Marketing vs other solutions?

PyMC-Marketing is and will always be free for commercial use, licensed under [Apache 2.0](LICENSE). Developed by core developers behind the popular PyMC package and marketing experts, it provides state-of-the-art measurements and analytics for marketing teams.

Due to its open-source nature and active contributor base, new features are constantly added. Are you missing a feature or want to contribute? Fork our repository and submit a pull request. If you have any questions, feel free to [open an issue](https://github.com/your-repo/issues).

### Thanks to our contributors!

[![https://github.com/pymc-devs/pymc/graphs/contributors](https://contrib.rocks/image?repo=pymc-labs/pymc-marketing)](https://github.com/pymc-labs/pymc-marketing/graphs/contributors)


## Marketing AI Assistant: MMM-GPT with PyMC-Marketing

Not sure how to start or have questions? MMM-GPT is an AI that answers questions and provides expert advice on marketing analytics using PyMC-Marketing.

**[Try MMM-GPT here.](https://mmm-gpt.com/)**

## 📞 Schedule a Free Consultation for MMM & CLV Strategy

Maximize your marketing ROI with a [free 30-minute strategy session](https://calendly.com/niall-oulton) 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.

We provide the following professional services:

- **Custom Models**: We tailor niche marketing analytics models to fit your organization's unique needs.
- **Build Within PyMC-Marketing**: Our team members 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.
