Add new customer_choice module

docs/source/getting_started/quickstart/customer_choice/index.md

@@ -1,5 +1,41 @@
 # Customer Choice Quickstart
 
-TODO - add python code block below with minimal code to get started with customer choice modeling
+```python
+from pymc_marketing.customer_choice.mv_its import (
+    MVITS,
+    generate_saturated_data,
+    plot_product,
+)
 
-Explore a hands-on [simulated example](https://pymc-marketing.readthedocs.io/en/stable/notebooks/customer_choice/mv_its_saturated.html) for more insights into customer choice with PyMC-Marketing.
+# Generate simulated data
+scenario = {
+    "total_sales_mu": 1000,
+    "total_sales_sigma": 5,
+    "treatment_time": 40,
+    "n_observations": 100,
+    "market_shares_before": [[0.7, 0.3, 0]],
+    "market_shares_after": [[0.65, 0.25, 0.1]],
+    "market_share_labels": ["competitor", "own", "new"],
+    "rng": rng,
+}
+
+data = generate_saturated_data(**scenario)
+
+# Build a multivariate interrupted time series model
+model = MVITS(
+    existing_sales=["competitor", "own"],
+    saturated_market=True,
+)
+
+model.inform_default_prior(
+    data=data.loc[: scenario1["treatment_time"], ["competitor", "own"]]
+)
+
+# Parameter estimation
+model.sample(data[["competitor", "own"]], data["new"])
+
+# Visualize the results
+model.plot_fit();
+```
+
+See the {ref}`howto` section for more information about using the customer choice module.

docs/source/guide/customer_choice/mv_its_intro.md

@@ -127,7 +127,7 @@ We could relax this assumption and consider an unsaturated market. More specific
 
 This requires only a minor change to the multivariate normal form of the model.
 
-Specifically, rather than having $P$ products which are the source of new product sales, we could simply add a psuedo-product which represents new sales. The first $P$ products are as normal, all the existing products. The final pseudo product would represent the market growth. We could simply add an additional $\beta$ parameter and specifcy the prior as:
+Specifically, rather than having $P$ products which are the source of new product sales, we could simply add a psuedo-product which represents new sales. The first $P$ products are as normal, all the existing products. The final pseudo product would represent the market growth. We could simply add an additional $\beta$ parameter and specify the prior as:
 
 $$
 \beta_1, \beta_2, \ldots, \beta_P, \beta_{P+1} \sim \mathrm{Dirichlet}(\alpha_1, \ldots, \alpha_P, \alpha_{P+1})

docs/source/notebooks/customer_choice/mv_its_unsaturated.ipynb

@@ -828,7 +828,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If we examine the parameters (in the unsaturated market case we examine `beta_all`, not `beta`) we can see that the model attributing a near zero reduction in the existing competitor and own company products. There is no reduction of course, the sales numbers actually _increased_ despite the market share decreasing."
+    "If we examine the parameters (in the unsaturated market case we examine `beta_all`, not `beta`) we can see that the model attributes a near zero reduction in the existing competitor and own company products. There is no reduction of course, the sales numbers actually _increased_ despite the market share decreasing."
    ]
   },
   {