The Self-Learning Response Model (SLRM) node generates and enables the updating of a model that allows you to predict which offers are most appropriate for customers and the probability of the offers being accepted. These sorts of models are most beneficial in customer relationship management, such as marketing applications or call centers.

This example is based on a fictional banking company. The marketing department wants to achieve more profitable results in future campaigns by matching the appropriate offer of financial services to each customer. Specifically, the example uses a Self-Learning Response model to identify the characteristics of customers who are most likely to respond favorably based on previous offers and responses and to promote the best current offer based on the results.

This example uses the flow named Making Offers to Customers - Self-Learning, available in the example project . The data files are pm_customer_train1.csv, pm_customer_train2.csv, and pm_customer_train3.csv.

1. Open the Example Project.
2. Scroll down to the Modeler flows section, click View all, and select the Making Offers to Customers - Self-Learning flow.

Existing data

The banking company has historical data tracking the offers made to customers in past campaigns, along with the responses to those offers. This data also includes demographic and financial information that can be used to predict response rates for different customers.