diff --git a/pymc_marketing/model_builder.py b/pymc_marketing/model_builder.py
index 725eeb6db..855db00ee 100644
--- a/pymc_marketing/model_builder.py
+++ b/pymc_marketing/model_builder.py
@@ -480,12 +480,39 @@ def attrs_to_init_kwargs(cls, attrs) -> dict[str, Any]:
             "sampler_config": json.loads(attrs["sampler_config"]),
         }
 
+    def build_from_idata(self, idata: az.InferenceData) -> None:
+        """Build model from the InferenceData object.
+
+        This is part of the :func:`load` method. See :func:`load` for more larger context.
+
+        Usually a wrapper around the :func:`build_model` method unless the model
+        has some additional steps to be built.
+
+        Parameters
+        ----------
+        idata : az.InferenceData
+            The InferenceData object to build the model from.
+
+        """
+        dataset = idata.fit_data.to_dataframe()  # type: ignore
+        X = dataset.drop(columns=[self.output_var])
+        y = dataset[self.output_var]
+
+        self.build_model(X, y)
+
     @classmethod
     def load(cls, fname: str):
         """Create a ModelBuilder instance from a file.
 
         Loads inference data for the model.
 
+        This class method has a few steps:
+
+        - Load the InferenceData from the file.
+        - Construct a new instance of the model using the InferenceData attrs
+        - Build the model from the InferenceData
+        - Check if the model id matches the id in the InferenceData loaded.
+
         Parameters
         ----------
         fname : string
@@ -521,11 +548,7 @@ def load(cls, fname: str):
             model = cls(**init_kwargs)
 
         model.idata = idata
-        dataset = idata.fit_data.to_dataframe()
-        X = dataset.drop(columns=[model.output_var])
-        y = dataset[model.output_var]
-        model.build_model(X, y)
-        # All previously used data is in idata.
+        model.build_from_idata(idata)
 
         if model.id != idata.attrs["id"]:
             error_msg = (