Instant customer base analysis in the financial services sector

Abstract

This study empirically validates the performance of stochastic customer base models in noncontract settings where the time at which a customer becomes dormant is not observable. We collaborate with a nationwide financial services company in Korea and analyze the complete transaction data of 373,031 retail customers from 2015 to 2018. We implement the following four buy-'til-you-die (BTYD) models: a) the original Pareto/NBD model, b) the Pareto/GGG model, c) the BG/CNBD-k model, and d) the MBG/CNBD-k model. The four BTYD models perform well in classifying active customers, with an area under the receiver operating characteristic curve of 0.82 ~ 0.86 for each of the one-month, two-month, ..., and twelve-month forecasting horizons. The results demonstrate that the BTYD framework can be used for customer base analysis as an instant heuristic approach that can complement existing customer relationship management tools.