improve readme (#1438)

pymc-labs · Jan 26, 2025 · d0a86cb · d0a86cb
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diff --git a/README.md b/README.md
@@ -68,6 +68,7 @@ Leverage our Bayesian MMM API to tailor your marketing strategies effectively. L
 | 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.                                                                                                                                                                                                                                                                                                                      |
+| Causal Identification                      | Input a business driven directed acyclic graph to identify the meaningful variables to include into the model to be able to draw causal conclusions. For a concrete example see the [guide notebook](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_causal_identification.html).                                                                                             |
 | 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.                                                                                                                                                     |
 | 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.                                                                                                                                                                                                                                                                          |
@@ -102,7 +103,7 @@ mmm = MMM(
 )
 ```
 
-Initiate fitting and get a visualization of some of the outputs with:
+Initiate fitting and get insightful plots and summaries. For example, we can plot the components contributions:
 
 ```python
 X = data.drop("y",axis=1)
@@ -113,13 +114,20 @@ mmm.plot_components_contributions();
 
 ![](docs/source/_static/mmm_plot_components_contributions.png)
 
+You can compute channels efficienty and compare them with the estimated return on ad spend (ROAS).
+
+<center>
+    <img src="docs/source/_static/roas_efficiency.png" width="70%" />
+</center>
+
 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.
+- 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.
+- Get started with a complete end-to-end analysis: from model specification to budget allocation. See the [guide notebook](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_case_study.html).
 
 ### Essential Reading for Marketing Mix Modeling (MMM)
 
@@ -207,13 +215,17 @@ mvits = MVITS(
 # Fit model
 mvits.fit(X, y)
 
-# Plot counterfactuals
-mvits.plot_counterfactual()
-
 # Plot causal impact on market share
 mvits.plot_causal_impact_market_share()
+
+# Plot counterfactuals
+mvits.plot_counterfactual()
 ```
 
+<center>
+    <img src="docs/source/_static/conterfactual.png" width="100%" />
+</center>
+
 See our example notebooks for [saturated markets](https://www.pymc-marketing.io/en/stable/notebooks/customer_choice/mv_its_saturated.html) and [unsaturated markets](https://www.pymc-marketing.io/en/stable/notebooks/customer_choice/mv_its_unsaturated.html) to learn more about customer choice modeling with PyMC-Marketing.
 
 ## Why PyMC-Marketing vs other solutions?

diff --git a/docs/source/_static/conterfactual.png b/docs/source/_static/conterfactual.png
diff --git a/docs/source/_static/roas_efficiency.png b/docs/source/_static/roas_efficiency.png