The impact of geographical factors on churn prediction: an application to an insurance company in Madrid's urban area

Abstract

Geography has previously been noted as a decisive factor in business literature. This paper provides evidence of the significant role geography plays in customer lapse behaviour in an urban environment. This novel approach is based on the idea that the customers who cancel all policies and leave the company are not randomly distributed; rather, a mimetic performance of close individuals is noted. The physical proximity of the customer to the geographical focus (strategical centre, as insurance offices) and the interaction with nearby customer are spatial factors that increase (or decrease) the probability of churning. An empirical analysis using more than 7,000 spatially georeferenced offline customers of a Spanish insurance company in the urban area of Madrid (Spain) demonstrated that the customer's proximity to offices of such insurance company under study decreases the probability of churning, whereas high lapse risk was detected in customers in the surroundings of the company's competitor branches. In addition, we identified spatial autocorrelation in churn probability, thus demonstrating that the probability of churn of a customer increases if nearby customers churn.

Spatial models for online retail churn: Evidence from an online grocery delivery service in Madrid

“…In order to ascertain the robustness of the results obtained in model 3, we performed a cross‐validation exercise (De la Llave et al, 2019) to validate the estimations. By taking 85% of the training sample randomly one thousand times, the variability of the estimators in the spatial‐probit model was obtained.…”

“…The second, with 306 observations (15% of the data) was the test sample. Moreover, an exercise of cross‐validation in a spatial framework (see De la Llave et al, 2019) was undertaken to assess how well the results of the statistical analysis could be generalized.…”

“…Therefore, taking advantage of the spatial correlation among customers can improve the performance of the model. However, in relation to customer churn behaviour, no research has yet taken into account geography and ‘space’ as explicative factors, and only De la Llave et al (2019) include geographic factors at all.…”

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.…”

The MARS Algorithm in the Spatial Framework: Non-Linearities and Spatial Effects in Hedonic Models

“…Despite its power, the MARS algorithm is still seldom used in spatial economics. To the best of our knowledge, there are only two regional economics studies using MARS: Martinetti and Geniaux (2017) and De la Llave et al (2019). However, both cases only apply the MARS algorithm to make an automatic pre-selection of non-spatial variables and not to select spatial term in a model with spatial effects.…”