Churn Model Training and Predictions

Churn Model Training

The Churn Model Training batch trains a customer churn prediction model and stores the trained model for use in future prediction workflows.

Input Parameter

Model Name must be a unique, alphanumeric model name. Spaces and special characters are not supported. Maximum length: 20 characters.

Source Data

The training dataset is sourced from VW_ML_CHURN_MODEL_DATASET table.

Prerequisites

Before running the batch:
  • Complete data loading.
  • Successfully complete Run Chart execution, including customer segmentation.
  • Configure the dataset view to return data for both current and previous segmentation dates in FSI_BI_SEGMENTATION.
  • Ensure the dataset has both churn and non-churn cases.
  • The process uses Synthetic Minority Over-sampling Technique (SMOTE) to improve class balance, but highly skewed data can affect model quality.

Churn Case Definition

A customer is considered a churn (positive) case when any of the following applies:
  • The customer’s balance is zero.
  • All customer accounts have an account-closure date.
  • The customer exists in the previous dataset but is absent from the latest dataset.

All other customers are classified as non-churn (negative) cases.

Output

After successful processing:
  • The trained model is stored in FSI_CS_MODEL_STORE table.
  • One record is created for each unique model name.
  • Reusing an existing model name updates the stored model and its performance metrics.
  • The model is available for downstream prediction processes.

Product and Channel Mappings

Product mappings are configured in Environment Properties:

  • Property Class: ML_MODELS
  • Property Code: PRODUCT_ML_MAPPING

Channel mappings are configured in Environment Properties:

  • Property Class: ML_MODELS
  • Property Code: CHANNEL_ML_MAPPING

Note:

These mappings are customer-specific. Refer to the Property Code Details table in the Machine Learning Model Properties section.

Churn Predictions

The Churn Predictions batch uses a previously trained churn model to generate customer churn predictions. Results are stored in the database and is available in Churn Model Predictions page.

Prerequisites

Before running the batch:
  • Train a churn model through PA Churn Model Training.
  • Confirm the specified model name exists in the model repository.
  • Ensure all required source data is loaded and available.

Processing

During execution, the batch:
  • Retrieves the trained model for the specified model name.
  • Applies the model to the latest customer data.
  • Generates churn predictions for eligible customers.

Output

After successful processing:
  • Results include churn scores generated by the trained model.
  • Predictions are stored in FSI_CS_CHURN_PREDICTIONS table.
  • Predictions are available for reporting and downstream analysis.

Viewing Predictions

Use Churn Model Predictions interface to view the latest churn prediction results generated by the PA Churn Predictions batch.

Note:

  • PA Churn Predictions requires a successfully trained model.
  • Predictions cannot be generated if the specified model name does not exist.
  • Use the exact same model name for training and prediction.