Predictive models are an effective tool for customer retention as they enable customer behavior prediction through probabilistic methods. Marketing strategies can be derived from these predictions, and budgets can be more selectively allocated. In this webinar, Philippe Take, Co-Founder and CRO of Gpredictive and Manuel Hinz, Managing Director and Founder of CrossEngage, show how predictive models can be used with a customer data platform (CDP) for innovative CRM approaches. Both speakers have extensive experience working with different types of companies in different stages of predictive modeling/CDP adoption and use it to provide valuable insights and tips for every use case.

Using Predictive Models to Increase Customer Value and Prevent Churn

Predictive models have many uses, including calculating anticipated customer lifetime value (CLV) and churn score. CLV scoring evaluates customers according to how valuable they are to the company and how high their churn risk is, allowing marketers to engage them with content according to their potential and increase individual customer value through targeted campaigns. The churn score describes how vulnerable a customer is to churn, and can be calculated from a customer’s shopping baskets and buying frequency. Churn scoring makes proactive work on commitment, product adaptation, and loyalty possible.

How CDPs Enable Predictive Modeling and Make Innovative CRM Possible

A CDP’s 360-degree profiles allow marketers to understand customers individually and respond to their interests and needs in real-time. Learn more about CDPs through our beginner’s guide! Predictive models enable CDP-created segments that are based on the CLV or churn score, among others. Marketers can prioritize the right customers, respond to and predict changes in the CLV, and more. That means data from the past, present, and predicted future can all be used for marketing measures. You can find out exactly how this works and more by watching our recording of the webinar.

  • What are the disadvantages of current, popular approaches to customer segmentation (e.g. the RFM model)?
  • What advantages do predictive models offer?
  • How can they be used in tandem with a CDP for customer segmentation and campaign planning?
  • What are the typical mistakes made by companies, and how can they be avoided?
  • What are some concrete examples of predictive modeling campaign implementation?

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