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---
## 📞 Schedule a Free Strategy Consultation
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.
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).
We provide the following professional services:
- **Custom Models**: We develop models that fit your organization's unique needs.
- **Coaching**: Regular, personalized coaching to ensure your team is well-equipped to confidently use PyMC-Marketing and related approaches.
- **SaaS Solutions**: Harness the power of our state-of-the-art software solutions to streamline your data-driven marketing initiatives.
### PyMC Labs Client Testimonials
## Quick links
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::::{grid-item-card} Example notebooks
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{material-outlined}`menu_book;5em`
^^^^^^^^^^^^^^^
The example notebooks provide examples of using
the library in both real case scenarios
and synthetic data. They explain how to use
the library and showcase its features.
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To the example notebooks
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::::{grid-item-card} API Reference
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{material-outlined}`data_object;5em`
^^^^^^^^^^^^^^^
The reference guide contains a detailed description of the functions,
modules, and objects included in the library. The reference describes how the
methods work and which parameters can be used. It assumes that you have an
understanding of the key concepts.
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To the reference guide
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## 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 | Benefit |
| ------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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*. |
## 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)
Each of these models is tailored to different types of data and business scenarios:
| | **Non-contractual** | **Contractual** |
| -------------- | ------------------------ | ----------------------- |
| **Continuous** | online purchases | ad conversion time |
| **Discrete** | concerts & sports events | recurring subscriptions |
---
Resources
### Bolt's success story with PyMC-Marketing
**Checkout the video below to see how Bolt leverages PyMC Marketing to assess the impact of their marketing efforts.**
### Time-varying parameters in MMMs in PyMC-Marketing
### Customer Lifetime Value Modeling in Marine Industry
For more videos, webinars and resources, check out the [PyMC Labs YouTube channel](https://www.youtube.com/@PyMC-Labs).
---
### More [PyMC Labs](https://www.pymc-labs.com/) Blog Posts and Resources
#### Marketing Mix Models
- [Unobserved Confounders, ROAS and Lift Tests in Media Mix Models](https://www.pymc-labs.com/blog-posts/mmm_roas_lift/)
#### Customer Lifetime Value
- [Hierarchical Customer Lifetime Value Models](https://www.pymc-labs.com/blog-posts/hierarchical_clv/)
- [Customer Lifetime Value in the non-contractual continuous case: The Bayesian Pareto NBD Model](https://www.pymc-labs.com/blog-posts/pareto-nbd/)
- [Cohort Revenue & Retention Analysis](https://www.pymc-labs.com/blog-posts/cohort-revenue-retention/)
### Case Studies
- [Building an in-house marketing analytics solution](https://www.pymc-labs.com/blog-posts/2023-07-18-niall-In-house-marketing/)
- [Bayesian Media Mix Models: Modelling changes in marketing effectiveness over time](https://www.pymc-labs.com/blog-posts/modelling-changes-marketing-effectiveness-over-time/)
- [Improving the Speed and Accuracy of Bayesian Media Mix Models](https://www.pymc-labs.com/blog-posts/reducing-customer-acquisition-costs-how-we-helped-optimizing-hellofreshs-marketing-budget/)
- [Bayesian Media Mix Modeling for Marketing Optimization](https://www.pymc-labs.com/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization/)
- [Bayesian inference at scale: Running A/B tests with millions of observations](https://www.pymc-labs.com/blog-posts/bayesian-inference-at-scale-running-ab-tests-with-millions-of-observations/)
For more blogposts and resources, check out the [PyMC Labs Blog](https://www.pymc-labs.com/blog-posts/).
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guide/index
api/index
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