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Non‐parametric modelling of time‐varying customer service times at a bank call centre
Author(s) -
Shen Haipeng,
D. Brown Lawrence
Publication year - 2006
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.618
Subject(s) - pointwise , heteroscedasticity , computer science , log normal distribution , service (business) , parametric statistics , econometrics , variance (accounting) , statistics , mathematics , economics , machine learning , mathematical analysis , economy , accounting
Call centres are becoming increasingly important in our modern commerce. We are interested in modelling the time‐varying pattern of average customer service times at a bank call centre. Understanding such a pattern is essential for efficient operation of a call centre. The call service times are shown to be lognormally distributed. Motivated by this observation and the important application, we propose a new method for inference about non‐parametric regression curves when the errors are lognormally distributed. Estimates and pointwise confidence bands are developed. The method builds upon the special relationship between the lognormal distribution and the normal distribution, and improves upon a naive estimation procedure that ignores this distributional structure. Our approach includes local non‐parametric estimation for both the mean function and the heteroscedastic variance function of the logged data, and uses local polynomial regression as a fitting tool. A simulation study is performed to illustrate the method. We then apply the method to model the time‐varying patterns of mean service times for different types of customer calls. Several operationally interesting findings are obtained and discussed. Copyright © 2006 John Wiley & Sons, Ltd.