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            Recipes and Datasets to Predict Customers Likely to Add More Assets

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            3. Financial Services Cloud Admin Guide

            Recipes and Datasets to Predict Customers Likely to Add More Assets

            The app created by using the Customer Likelihood of Adding Assets template creates three recipes. The recipes create example, prediction, and predicted score datasets.

            Required Editions

            Available in: Professional, Enterprise, and Unlimited editions with the Revenue Intelligence for Financial Services license
            Recipes and Datasets
            Recipe Description Output
            Example Dataset to Predict Added Assets Evaluates account snapshot data, financial account snapshot data, and data from configured objects to create an example dataset that Einstein learns from. Example dataset
            Feature Dataset to Predict Added Assets Evaluates account snapshot data, financial account snapshot data, and data from configured objects to create a prediction dataset based on which Wealth Management customers who are likely to add assets are identified. Prediction dataset
            Prediction Dataset to Predict Added Assets Evaluates the prediction dataset to get the prediction score of customers likelihood to buy assets and the top three factors that possibly contribute to asset addition. 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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