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

Keywords: Call centres, Classification, Customer relationship management, Data mining, Hypermedia
markup languages, Marketing data processing, SQL, Very large databases
20. Foss, Bryan, Iain Henderson, Peter Johnson, Don Murray and Merlin Stone. Managing the
quality and completeness of customer data, The Journal of Database Marketing, 10 (2), 2002,
139–158.
Abstract. Although companies have been collecting customer-related data for years, this was
normally for administration rather than customer management. While larger companies have
more recently collected customer data for database marketing—to recruit new customers, sell
more to existing customers, support customer service operations, and retain customers—returns
are usually limited because most data are still held and used departmentally. The growth of
contact centers, e-commerce, and more complex value chains has raised additional issues of enterprise
data management and exploitation, while demonstrating beyond doubt that available
data are insufficient to support new customer management processes. The article considers these
issues and proposed tried and tested approaches for addressing these customer data management
issues in a practical and achievable manner.
Keywords: Database marketing, Marketing management, Data integrity, Customer relationship
management
(Appears also in Section VIII.)
21. Hur, D. A comparative evaluation of forecast monitoring systems in service organizations, 33rd
Annual Meeting of the Decision Sciences Institute, Decision Sciences Institute, San Diego, CA,
USA, 2002, 5 pp.
Abstract. Demand forecasts are major inputs to workforce scheduling and material planning
in many service organizations, and the effectiveness of such planning activities hinges upon the
accuracy of the forecasts. Since forecasts are rarely precise in reality, managers need to monitor
forecast errors when they implement the labor and material plans. The paper aims to identify
and evaluate an automatic detector of forecast bias to help managers. The paper identified and
evaluated five error detection techniques using both actual data from a call center, and simulated
data. All five techniques detected a considerable demand shift in a timely manner, and appeared
very robust across diverse demand environments. In particular, the threshold curve and wineglass
chart turned out to be the quickest and most powerful of the five methods. In addition,
the patterns of within day demand arrival and their stability throughout the day significantly
influenced the performance of the detection techniques.
Keywords: Call centres, Forecasting theory, Human resource management, Manufacturing resources
planning, Scheduling, Service industries, Statistics
22. Avramidis, Athanassios, Alexandre Deslauiers and Pierre L’Ecuyer. Modeling daily arrivals to
a telephone call center, Management Science, 50 (7), 2004, 896–908.
Abstract. We develop stochastic models of time-dependent arrivals, with focus on the application
to call centers. Our models reproduce essential features of call center arrivals observed in
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