Forecasting the next likely purchase events of insurance customers

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.

Evaluating the Impact of Customer Relationship Management (CRM) Strategies on Customer Retention (A Study of Fast Food Chains in Pakistan)

“…The concept of CRM strategy refers to the practice of focusing on turning each potential customer into a customer and keeping customers through personalized communication with them. The idea of CRM has made organizations enhance communication with clients ever since implementation and reach their objectives (Mau, Pletikosa, Wagner and Mau, 2018). CRM's fundamental aim is to strengthen customer retention and satisfaction by adequately analyzing customer knowledge, thereby trying to improve the firm's global corporate competitiveness (Garrido-moreno and Padilla-melé ndez, 2011).…”

Customer Relationship Management Research from 2000 to 2020: An Academic Literature Review and Classification

“…Top management commitments, effective strategies, innovative organizational culture, skilled personnel and other resources help in repeating the maximum benefits of the CRM system. Mau et al (2018) By observing old data of customers can help in predicting the future actions of customers. The suggested model can use in identifying high profitable customers.…”

Current Directions and Future Research Priorities of Customer Data Analysis

“…L. powerful and significant compared to traditional statistical ones when it comes to prediction accuracy [13][14]. The customer analysis technique enables companies to communicate with the true customer, at the right time, by the expected offer [15]. Improving online purchase conversion rates by increasing the likelihood of a visiting customer to result in a purchase transaction [13].…”