What policyholder and contract features determine the evolution of non-life insurance customer relationships?

Abstract

Purpose Over the last decade, technological and social trends have significantly influenced the relationship between customers and insurers. New buying patterns, price comparison platforms and the usage of different interaction channels driving single-product purchases and impacting lapses have influenced insurers’ customer portfolios and development. The purpose of this paper is to study the features driving the customer relationship along three areas, namely, customer acquisition, development and retention. Design/methodology/approach After defining 14 related hypotheses, the authors use econometric analyses to quantitatively support these hypotheses in the three areas of interest. The authors build on a large-scale longitudinal data set from a Swiss insurance company covering the period from 2005 to 2014 and including 2,757,000 customer-years. The data comprise information on private customers, their contract history, including coverage and losses and the channels used for buying insurance. This analysis focuses on the two most common non-life insurance products, namely, household/liability and car insurance. Findings The authors provide descriptive statistics and results from econometric analyses to determine the significant features and patterns affecting customer development and retention. Among the main results, the authors underline the significant influence on cross-selling given by the customer’s age and the interaction channel. Customers from rural regions are more loyal and likely to conduct cross-buying when compared to their peers from urban regions. Car insurance holders are more likely to lapse than household/liability insurance clients. Finally, while newly acquired customers tend to buy only a single product, the authors show the importance of cross-selling for retaining customers. In fact, customer retention is positively influenced by the number of products hold. Research limitations/implications This work is relevant for academics and practitioners alike, adding a quantitative basis to the understanding of managing customer relationships and for the development of further prospective models. Further work could investigate or add products, extend the study to other companies and focus on customer development with time. Originality/value This study explores a large-scale longitudinal data set. The analyses of customer acquisition, development and retention can support insurers to construct their own models for customer relationship management.

Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

“…In terms of financial volume, we consolidate a total claim amount of CHF 149 mio for the training data and CHF 35 mio for the test data. Table 1 summarizes the available explanatory variables for the process of ratemaking (Frees 2015), i.e., building homogeneous classes of policyholders (see, e.g., Laas et al 2016;Staudt and Wagner 2018). Most of the factors are determined at the moment of underwriting of the contract (a priori variables, Denuit and Lang 2004;Denuit et al 2007;Verbelen and Antonio 2016).…”

Non-life insurance cancellation: a systematic quantitative literature review

“…The publications selected were analysed according to their methodological approach. Eight papers are theoretical (Larsson and Broström 2019;Brophy 2015;Gallo 2014;Van Gelder et al 2018;Guillenet al 2008;Naujoks et al 2017;Rawson et al 2013;Julie Robson 2015) and 18 are empirical; of the latter, 16 are quantitative (Bolancé et al 2016;Brockett et al 2008;de la Llave et al 2019;Felício and Freire 2016;Frank and Lamiraud 2009;Gamble et al 2009;Guillen et al 2003Guillen et al , 2009Guillén et al 2012;Günther et al 2014;Haugen and Moger 2016;Jeong et al 2018;Lin 2010;López-Díaz et al 2017;Paredes 2018;Staudt and Wagner 2018), one is qualitative (Robson and Sekhon 2011) and one uses a mixed methodology (Dominique-Ferreira 2018). This sample indicates that the study of the cancellation of insurance policies is predominantly conducted through empirical quantitative analysis.…”

“…Regarding the methods of data collection for the empirical research, longitudinal analysis predominates, applied in 11 papers (Bolancé et al 2016;Brockett et al 2008;de la Llave et al 2019;Guillen et al 2003Guillen et al , 2009Guillén et al 2012;Günther et al 2014;Haugen and Moger 2016;Jeong et al 2018;Paredes 2018;Staudt and Wagner 2018), over cross-sectional analysis, used in six papers (Frank and Lamiraud 2009;Gamble et al 2009;Lin 2010;Robson and Sekhon 2011;López-Díaz et al 2017), including the paper that used a mixed qualitative and quantitative methodology (Dominique-Ferreira 2018). In the paper by Felício and Freire (2016), both longitudinal and cross-sectional approaches were applied.…”

Forecasting the next likely purchase events of insurance customers

“…Lemmens and Croux, 2006), or whether he would be interested in cross-buying an additional product (e.g. Staudt and Wagner, 2016). In the various scenarios, the objective remains similar: identify sales opportunities for an individual customer and subsequently target him with an adequate offer (e.g.…”