Cancellation forecast

What is the challenge?

Customer retention and loyalty are important competitive factors. However, in many markets, customers are becoming increasingly willing to switch. This contrasts with the high costs of acquiring new customers and winning them back. 

What data can help?

  • Customer data (sociographic data, usage data, historical sales and consumption behavior data, search histories, master data, interaction data)
  • Contract data (contract duration)
© Shutterstock
Use case category: cancellation forecast

How can companies use their data?

Terminations are often preceded by certain patterns. For example, frequent contact with customer service may be an indicator of cancellation. This and other patterns are identified using data analytics in historical customer data so that appropriate messages are generated. These data-driven insights are used to better target and prevent switching or cancellation.

Where is this use of data already being applied?

Telecommunications service provider O2 uses data analytics to predict cancellations. By analyzing the data, patterns for terminations are created. This can be used to make predictions about the likelihood of termination for each contract partner for two to eight weeks in advance. With this information, O2 approaches its customers and creates offers for customer retention.

The media group Netflix uses data analytics to identify cancellation intentions in advance (e.g. with information on series/movie cancellations, times between use, decreasing usage time per visit over time, ratings). This information is used to create special offers for these customers or customer groups.

How does this use of data contribute to value creation?

With data-based forecasting of intentions to cancel, companies receive an early warning signal and can select and design their customer retention activities in a more targeted manner. In this way, more customer relationships and revenues can be stabilized or increased.

Aim of data use

Sources: O2 (2013), Netflix (2017)