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Integration With Third-Party Personalization Engines

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A company wants to prevent its customers from switching to a competitor's product by giving them special offers.

The company analyzes historical data and discovers a trend that is related to customer defection. Based on this analysis, the company creates a new model that uses relevant customer data to predict the probability of a customer switching to a competitor's product.

The model might be created using a third-party personalization engine. Several such models exist for the pharmaceutical industry.

The company wants to monitor customer actions in real time while running the model. Based on the model's predictions, the company wants to make a special offer to likely defectors to prevent them from switching.

Use the following steps to implement this:

Analyze historical data and create a new model that accesses the relevant data about customers and predicts the probability of their defection.
Create a business service that integrates Siebel with the third-party software and runs the predictive model.
Write personalization rules that trigger the business service in response to customer actions. These run-time personalization events are passed to the third-party engine through the business service interface.
Write personalization rules to deliver a special offer to a customer based on results from the model.

When the customer enters the Web site, he or she might fill out a survey or just browse the site. A special offer appears in a new browser window. Because the offer is targeted specifically to the customer, he or she takes the time to look at it instead of exiting the Web site.

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