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            Recipes and Datasets to Identify Retail Banking Customers Likely to Churn

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            Recipes and Datasets to Identify Retail Banking Customers Likely to Churn

            The app created by using the Customer Churn Risk for Retail Banking template creates three recipes. The recipes create example, historical, prediction, and predicted score datasets.

            Required Editions

            Available in: Professional, Enterprise, and Unlimited editions with the Revenue Intelligence for Financial Services license
            Feedback Management features are available with the Feedback Management - Starter license or the Feedback Management - Growth license.
            Sentiment Insights features are available with the Feedback Management - Starter license or the Feedback Management - Growth license, and the Sentiment Insights license.
            Recipes and Datasets
            Recipe Description Output
            Retail Banking Churn Example Dataset The recipe evaluates account snapshot data, financial account snapshot data, and data from configured objects to create an example dataset that Einstein learns from. If you chose to include Feedback Management and Sentiment Insights features when creating the app, the recipe also evaluates the example set containing these features. Example dataset
            Retail Banking Churn Feature Dataset The recipe evaluates account snapshot data, financial account snapshot data, and data from configured objects to create a historical dataset with details of accounts that were previously likely to churn. The recipe also creates a prediction dataset based on which Retail Banking customers who are likely to churn are identified. If you chose to include Feedback Management and Sentiment Insights features when creating the app, the recipe also evaluates the prediction set containing these features.
            • Historical dataset
            • Prediction dataset
            Retail Banking Churn Prediction Dataset Evaluates the prediction dataset to get the churn score of customers and the top three factors that possibly contribute to churn. Predicted Score and Top Contributing Factor dataset

            You can modify the recipes in these scenarios:

            • Your schema deviates from the Financial Services Cloud schema.
            • A custom field of an existing entity changes.
            • The use of feature data changes from an existing entity to a custom entity.
            • The data doesn’t load properly.
            • The app stops working because of incorrect data values.

            See Also

             
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