Measuring customer quality in retail banking Export

@article{2005_HandCrowder, abstract = {The retail banking sector makes heavy use of statistical models to predict various aspects of customer behaviour. These models are built using data from earlier customers, but have several weaknesses. An alternative approach, widely used in social measurement, bur apparently not yet applied in the retail banking sector, is to use latent-variable techniques to measure the underlying key aspect of customer behaviour. This paper describes such a model that separates the observed variables for a customer into primary characteristics on the one hand, and indicators of previous behaviour on the other, and links the two via a latent variable that we identify as 'customer quality'. We describe how to estimate the conditional distribution of customer quality, given the observed values of primary characteristics and past behaviour.}, author = {Hand, D. J. and Crowder, M. J.}, citeulike-article-id = {3485852}, citeulike-linkout-0 = {http://dx.doi.org/http://dx.doi.org/10.1191/1471082X05st092oa}, citeulike-linkout-1 = {http://dx.doi.org/10.1191/1471082X05st092oa}, comment = {- p.2 cites recent reviews}, doi = {http://dx.doi.org/10.1191/1471082X05st092oa}, journal = {Statistical Modelling}, keywords = {banking, cards, classification, consumer, credit, default, delinquency, discrete, effects, file-import-08-11-06, financial, latent, loan, models, prediction, random, retail, scorecards, variables}, number = {2}, pages = {145--158}, posted-at = {2008-11-06 21:36:17}, priority = {0}, title = {Measuring customer quality in retail banking}, url = {http://dx.doi.org/http://dx.doi.org/10.1191/1471082X05st092oa}, volume = {5}, year = {2005} }

TY - JOUR ID - 2005_HandCrowder L3 - citeulike-article-id:3485852 N2 - The retail banking sector makes heavy use of statistical models to predict various aspects of customer behaviour. These models are built using data from earlier customers, but have several weaknesses. An alternative approach, widely used in social measurement, bur apparently not yet applied in the retail banking sector, is to use latent-variable techniques to measure the underlying key aspect of customer behaviour. This paper describes such a model that separates the observed variables for a customer into primary characteristics on the one hand, and indicators of previous behaviour on the other, and links the two via a latent variable that we identify as 'customer quality'. We describe how to estimate the conditional distribution of customer quality, given the observed values of primary characteristics and past behaviour. IS - 2 JF - Statistical Modelling EP - 158 TI - Measuring customer quality in retail banking VL - 5 SP - 145 KW - banking KW - cards KW - classification KW - consumer KW - credit KW - default KW - delinquency KW - discrete KW - effects KW - file-import-08-11-06 KW - financial KW - latent KW - loan KW - models KW - prediction KW - random KW - retail KW - scorecards KW - variables N1 - - p.2 cites recent reviews AU - Hand, DJ AU - Crowder, MJ PY - 2005/// UR - http://dx.doi.org/http://dx.doi.org/10.1191/1471082X05st092oa ER -