Forecasting the next likely purchase events of insurance customers A case study on the value of data-rich multichannel environments

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.