Time Varying Media Contribution Notebook

README.md

@@ -61,6 +61,7 @@ Leverage our Bayesian MMM API to tailor your marketing strategies effectively. L
 - **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.
 - **Time-varying Intercept:** Capture time-varying baseline contributions in your model (using modern and efficient Gaussian processes approximation methods).
+- **Time-varying Media Contribution:** Capture time-varying media efficiency in your model (using modern and efficient Gaussian processes approximation methods).
 - **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.

docs/source/notebooks/index.md

@@ -10,6 +10,7 @@ mmm/mmm_example
 mmm/mmm_budget_allocation_example
 mmm/mmm_lift_test
 mmm/mmm_tvp_example
+mmm/mmm_time_varying_media_example
 mmm/mmm_components
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