000089257 001__ 89257
000089257 005__ 20200716101429.0
000089257 0247_ $$2doi$$a10.1080/03461238.2018.1531781
000089257 0248_ $$2sideral$$a109559
000089257 037__ $$aART-2019-109559
000089257 041__ $$aeng
000089257 100__ $$ade la Llave, Miguel Ángel
000089257 245__ $$aThe impact of geographical factors on churn prediction: an application to an insurance company in Madrid's urban area
000089257 260__ $$c2019
000089257 5060_ $$aAccess copy available to the general public$$fUnrestricted
000089257 5203_ $$aGeography 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 7000 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.
000089257 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/S37-17R$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/ECO-2015-65758-P
000089257 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000089257 590__ $$a1.705$$b2019
000089257 592__ $$a1.206$$b2019
000089257 591__ $$aSTATISTICS & PROBABILITY$$b36 / 124 = 0.29$$c2019$$dQ2$$eT1
000089257 593__ $$aEconomics and Econometrics$$c2019$$dQ1
000089257 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b49 / 106 = 0.462$$c2019$$dQ2$$eT2
000089257 593__ $$aStatistics, Probability and Uncertainty$$c2019$$dQ1
000089257 591__ $$aSOCIAL SCIENCES, MATHEMATICAL METHODS$$b21 / 51 = 0.412$$c2019$$dQ2$$eT2
000089257 593__ $$aStatistics and Probability$$c2019$$dQ1
000089257 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000089257 700__ $$aLópez, Fernando A.
000089257 700__ $$0(orcid)0000-0003-4719-1345$$aAngulo, Ana$$uUniversidad de Zaragoza
000089257 7102_ $$14000$$2415$$aUniversidad de Zaragoza$$bDpto. Análisis Económico$$cÁrea Fund. Análisis Económico
000089257 773__ $$g2019, 3 (2019), 188-203$$pSCANDINAVIAN ACTUARIAL JOURNAL$$tSCANDINAVIAN ACTUARIAL JOURNAL$$x0346-1238
000089257 8564_ $$s1016489$$uhttps://zaguan.unizar.es/record/89257/files/texto_completo.pdf$$yPostprint
000089257 8564_ $$s246212$$uhttps://zaguan.unizar.es/record/89257/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000089257 909CO $$ooai:zaguan.unizar.es:89257$$particulos$$pdriver
000089257 951__ $$a2020-07-16-08:48:12
000089257 980__ $$aARTICLE