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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).
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#
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.
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.
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), 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. |
Time-varying Intercept |
Capture time-varying baseline contributions in your model (using modern and efficient Gaussian processes approximation methods). See guide notebook. |
Time-varying Media Contribution |
Capture time-varying media efficiency in your model (using modern and efficient Gaussian processes approximation methods). See the guide notebook. |
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 for more details. |
GPU Support |
PyMC’s multiple backends allow for GPU acceleration. |
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 |
Experiment Calibration |
Fine-tune your model based on empirical experiments for a more unified view of marketing. See the lift test integration explanation for more details. Here 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:
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 |
Customer Choice Analysis#
Analyze the impact of new product launches and understand customer choice behavior with our Multivariate Interrupted Time Series (MVITS) models. Our API supports analysis in both saturated and unsaturated markets to help you:
Feature |
Benefit |
---|---|
Market Share Analysis |
Understand how new products affect existing product market shares |
Causal Impact Assessment |
Measure the true causal effect of product launches on sales |
Saturated Market Analysis |
Model scenarios where total market size remains constant |
Unsaturated Market Analysis |
Handle cases where new products grow the total market size |
Visualization Tools |
Plot market shares, causal impacts, and counterfactuals |
Bayesian Inference |
Get uncertainty estimates around all predictions |
See our example notebooks for saturated markets and unsaturated markets to learn more about customer choice modeling with PyMC-Marketing.
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.
More PyMC Labs Blog Posts and Resources#
Marketing Mix Models#
Customer Lifetime Value#
Case Studies#
Bayesian Media Mix Models: Modelling changes in marketing effectiveness over time
Improving the Speed and Accuracy of Bayesian Media Mix Models
Bayesian inference at scale: Running A/B tests with millions of observations
For more blogposts and resources, check out the PyMC Labs Blog.