CALL CENTERS (CENTRES) - Faculty of Industrial Engineering and ...

Abstract. We model a call center as a queueing model with Poisson arrivals having an unknown
varying arrival rate. We show how to compute prediction intervals for the arrival rate, and use
the Erlang formula for the waiting time to compute the consequences for the occupancy level of
the call center. We compare it to the current practice of using a point estimate of the arrival
rate (assumed constant) as forecast.
(Appears also in Section I.)
17. Mandelbaum, A., A. Sakov and S. Zeltyn. Empirical analysis of a call center. Technical report,
Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology,
Haifa, Israel, 2001.
18. Antipov, A. and N. Meade. Forecasting call frequency at a financial services call centre, The
Journal of the Operational Research Society, 53 (9), 2002, 953–960.
Abstract. A forecasting model is developed for the number of daily applications for loans at a
financial services telephone call center. The purpose of the forecasts and the associated prediction
intervals is to provide effective staffing policies within the call center. The model building
process is constrained by the availability of only 2 years and 7 months of data. The distinctive
feature of the data is that demand is driven in the main by advertising. The analysis given
focuses on applications stimulated by press advertising. Unlike previous analyses of broadly
similar data, where ARIMA models were used, a model with a dynamic level, multiplicative
calendar effects and a multiplicative advertising response is developed and shown to be effective.
Keywords: Studies, Forecasting techniques, Call centers, Financial services, Mathematical models,
Workforce planning, Advertising
19. Bayerl, S., T. Bollinger and C. Schommer. Applying models with scoring, Third International
Conference on Data Mining, WIT Press, Southampton, UK, 2002, 757–766.
Abstract. “Scoring”, in general, is defined as the usage of mining models—based on historical
data—for classification or segmentation of new items. For example, if the historical data consist
of classified customers, then we can use the model for the prediction of the behaviour of a new
customer. Scoring offers novel ways to exploit the power of data mining models in everyday
business activities, and proliferate mining applications to users who are not educated in mining.
In this paper, we present a) the generic scoring process, b) its technical implementation, and c)
an example of how scoring can be integrated in a real application. The generic process consists of
three steps: The mining models are learned first, then they are transferred into the application
database, and finally, the models are applied to the data loaded in that database. Arguments for
the necessity of such a mining improvement are collected. IBM DB2 Intelligent Miner Scoring
(IM Scoring) is the first technical implementation of scoring. It is based on the emerging open
standard for mining models (Predictive Model Markup Language—PMML), and the mining extensions
for SQL. Implementation issues are discussed, as well as problems that come along with
its integration into operational applications. The article closes with the description of a sample
application, the integration of scoring into a call center environment. A discussion of the scoring
method concludes this article.
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