Forecasting the next likely purchase events of insurance customers

Stefan Mau · Irena Pletikosa Cvijikj · Joël Wagner

23 citations0/0 files storedInternational Journal of Bank Marketing

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Customer churn and segmentation Customer Service Quality and Loyalty Insurance and Financial Risk Management

Abstract

Purpose The purpose of this paper is to demonstrate the value of enriched customer data for analytical customer relationship management (CRM) in the insurance sector. In this study, online quotes from an insurer’s website are evaluated in terms of serving as a trigger event to predict churn, retention, and cross-selling. Design/methodology/approach For this purpose, the records of online quotes from a Swiss insurer are linked to records of existing customers from 2012 to 2015. Based on the data from automobile and home insurance policyholders, random forest prediction models for classification are fitted. Findings Enhancing traditional customer data with such additional information substantially boosts the accuracy for predicting future purchases. The models identify customers who have a high probability of adjusting their insurance coverage. Research limitations/implications The findings of the study imply that enriching traditional customer data with online quotes yields a valuable approach to predicting purchase behavior. Moreover, the quote data provide supplementary features that contribute to improving prediction performance. Practical implications This study highlights the importance of selecting the relevant data sources to target the right customers at the right time and to thus benefit from analytical CRM practices. Originality/value This paper is one of the first to investigate the potential value of data-rich environments for insurers and their customers. It provides insights on how to identify relevant customers for ensuing marketing activities efficiently and thus avoiding irrelevant offers. Hence, the study creates value for insurers as well as customers.

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References

2006Challenges and Opportunities in Multichannel Customer Management 2007Multichannel customer management: Understanding the research-shopper phenomenon 2006Bagging and Boosting Classification Trees to Predict Churn 2004The role of multichannel integration in customer relationship management 2005Customer Relationship Management: A Databased Approach 2005Managing Marketing Communications with Multichannel Customers 2009Probability Models for Customer-Base Analysis 2008Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

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