Add quickstart to readme (#653)

* add quickstartt to readme

* add pymc labs logo

* typos

* add community links

* fixes

README.md

@@ -20,6 +20,23 @@
 
 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="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
+
+- [Bayesian discord server](https://discord.gg/swztKRaVKe)
+- [PyMC discourse](https://discourse.pymc.io/)
+
 ## Quick Installation Guide for Marketing Mix Modeling (MMM) & CLV
 
 To dive into MMM and CLV analytics, set up a specialized environment, `marketing_env`, via conda-forge:
@@ -39,11 +56,53 @@ We provide a `Dockerfile` to build a Docker image for PyMC-Marketing so that is
 
 Leverage our Bayesian MMM API to tailor your marketing strategies effectively. Based on the research [Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017)](https://research.google/pubs/pub46001/),  and integrating the expertise from core PyMC developers, our API provides:
 
+- **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.
+- **Visualization and Model Diagnostics**: Get a comprehensive view of your model's performance and insights.
+- **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.
 - **Experiment Calibration**: Fine-tune your model based on empirical experiments for a more unified view of marketing.
 
+### MMM Quickstart
+
+```python
+import pandas as pd
+from pymc_marketing.mmm import DelayedSaturatedMMM
+
+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 = DelayedSaturatedMMM(
+    date_column="date_week",
+    channel_columns=["x1", "x2"],
+    control_columns=[
+        "event_1",
+        "event_2",
+        "t",
+    ],
+    adstock_max_lag=8,
+    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();
+```
+
+![](/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)
@@ -72,7 +131,28 @@ Explore our detailed CLV examples using data from the [`lifetimes`](https://gith
 | **Continuous** | Buying groceries    | Audible                         |
 | **Discrete**   | Cinema ticket       | Monthly or yearly 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.
+
+![](/docs/source/_static/expected_purchases.png)
+
+See the Examples section for more on this.
 
 ## Why PyMC-Marketing vs other solutions?
 
@@ -90,8 +170,6 @@ Not sure how to start or have questions? MMM-GPT is an AI that answers questions
 
 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). Explore these topics further by watching our video on [Bayesian Marketing Mix Models: State of the Art](https://www.youtube.com/watch?v=xVx91prC81g).
-
 We provide the following professional services:
 
 - **Custom Models**: We tailor niche marketing analytics models to fit your organization's unique needs.

docs/source/index.md

@@ -150,6 +150,7 @@ beta_geo_model = clv.BetaGeoModel(
 
 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. See the Examples section for more on this.
 
 ## 📞 Schedule a Free Consultation for MMM & CLV Strategy