Research Article

DOI :10.26650/ibr.2022.51.951646   IUP :10.26650/ibr.2022.51.951646    Full Text (PDF)

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Nisan Güniz SerperElif ŞenBanu Çalış Uslu

Optimization models enable organizations to find the best solution and respond to the demand from an uncertain environment and stochastic process promptly and with less engineering effort. This study aims to optimize the number of seasonal agents and customer prioritization needed for a call center system using big data analytics and discrete event simulations to improve customer satisfaction. The study was carried out based on data from a leading heating and ventilation company’s call center. The K-means clustering technique was used to determine customer segmentation on 6-million-customer data. For prioritization, the making of a Recency-Frequency-Monetary (RFM) analysis was applied. The system was modeled using ARENA simulation software, and performance parameters were measured depending on the segments obtained. The results show that the simulation model performed with data analytics gives better results for a beneficial financial impact with numerical values in customer prioritization, reducing the average waiting time of the most prioritized customers by more than 90%, and for the least prioritized customers, it increased the average waiting time by approximately just 40%. However, with the company segments, the increase in the average waiting time of the least prioritized customers was approximately 300%.

PDF View


  • Abdullateef, A. O., & Salleh, S. M. (2013). Does customer relationship management influence call center quality performance? An empirical industry analysis. Total Quality Management & Business Excellence, 24(9-10), 1035-1045. google scholar
  • Afrapoli, A. M., Tabesh, M., & Askari-Nasab, H. (2019). A multiple objective transportation problem app-roach to dynamic truck dispatching in surface mines. European Journal of Operational Research, 276(1), 331-342. google scholar
  • Aktekin, T. (2014). Call center service process analysis: Bayesian parametric and semi-parametric mixture modeling. European Journal of Operational Research, 234(3), 709-719. google scholar
  • Alotaibi, Y., & Liu, F. (2013). Average waiting time of customers in a new queue system with different clas-ses. Business Process Management Journal. google scholar
  • Amorim-Lopes, M., Guimarâes, L., Alves, J., & Almada-Lobo, B. (2021). Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete-event simu-lation. International Transactions in Operational Research, 28(2), 687-715. google scholar
  • Andrade, C. (2019). The P-value and statistical significance: misunderstandings, explanations, challenges, and alternatives. Indian Journal of Psychological Medicine, 41(3), 210-215. google scholar
  • Anton, J. (2000). The past, present, and future of customer access centers. International Journal of Service Industry Management. google scholar
  • Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data-enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101. google scholar
  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152. google scholar
  • Arora, A. (2007). Dynamic project management using simulations. Project Managment Institute. google scholar
  • Avramidis, A. N., & L’Ecuyer, P. (2005, December). Modeling and simulation of call centers. In Proceedings of the Winter Simulation Conference, 2005. (pp. 9-pp). IEEE. google scholar
  • Banks, J., Carson II, J. S., & Barry, L. (2005). Discrete-event system simulation fourth edition google scholar
  • Behiri, W., Belmokhtar-Berraf, S., & Chu, C. (2018). Urban freight transport using passenger rail network: Scientific issues and quantitative analysis. Transportation Research Part E: Logistics and Transportation Review, 115, 227-245. google scholar
  • Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on the Elbow method and k-means in WSN. International Journal of Computer Applications, 105(9). google scholar
  • Bongomin, O., Mwasiagi, J. I., Nganyi, E. O., & Nibikora, I. (2020). A complex garment assembly line ba-lancing using simulation-based optimization. Engineering Reports, 2(11), e12258. google scholar
  • Calis, B. (2016). Agent-Based Simulation Model for Profit Maximization. Journal of Management and In-formation Science, 4(1), 26-33. google scholar
  • Carnein, M., & Trautmann, H. (2019, April). Customer segmentation based on transactional data using stre-am clustering. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 280-292). Springer, Cham. google scholar
  • Cui, M. (2020). Introduction to the K-Means Clustering Algorithm Based on the Elbow Method. Accounting, Auditing and Finance, 1(1), 5-8. google scholar
  • David, F. R. (2013). Strategic Management: Concepts and Cases. Pearson. google scholar
  • Dillman, D. A., Phelps, G., Tortora, R., Swift, K., Kohrell, J., Berck, J., & Messer, B. L. (2009). Response rate and measurement differences in mixed-mode surveys using mail, telephone, interactive voice respon-se (IVR) and the Internet. Social science research, 38(1), 1-18. google scholar
  • Doomun, R., & Jungum, N. V. (2008). Business process modelling, simulation and reengineering: call cent-res. Business Process Management Journal. google scholar
  • Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904. google scholar
  • Farajian, M. A., & Mohammadi, S. (2010). Mining the banking customer behavior using clustering and association rules methods. google scholar
  • Farruh, K. (2019). Consumer Life Cycle and Profiling: A Data Mining Perspective. In Consumer Behavior and Marketing. IntechOpen. google scholar
  • Feinberg, R., De Ruyter, K., & Bennington, L. (2005). Cases in call center management: great ideas (th) at work. Purdue University Press. google scholar
  • Feinberg, R. A., Kim, I. S., Hokama, L., De Ruyter, K., & Keen, C. (2000). Operational determinants of caller satisfaction in the call center. International Journal of Service Industry Management. google scholar
  • Figueiredo, V., Duarte, F. J., Rodrigues, F., Vale, Z., & Gouveia, J. (2003, September). Electric energy custo-mer characterization by clustering. In Proc. ISAP. google scholar
  • Gayathri, M., Jha, S., Parmar, M., & Malathy, C. (2020, February). Customer Profiling Using Demographic Analysis by Video Face Detection and Recognition. In 2020 International Conference on Inventive Com-putation Technologies (ICICT) (pp. 570-575). IEEE. google scholar
  • Gittins, P., McElwee, G., & Tipi, N. (2020). Discrete event simulation in livestock management. Journal of Rural Studies, 78, 387-398. google scholar
  • Greco, F., & Polli, A. (2020). Emotional Text Mining: Customer profiling in brand management. Internatio-nal Journal of Information Management, 51, 101934. google scholar
  • Gustriansyah, R., Suhandi, N., & Antony, F. (2020). Clustering optimization in RFM analysis based on k-means. Indonesia. J. Electr. Eng. Comput. Sci, 18(1), 470-477. google scholar
  • Hahnke, J. (2000). The CRM Lifecycle-Without CRM Analytics, Your Customers Won’t Even Know You’re There. Defying the Limits, 159-164. google scholar
  • Hassan, M. M. T. M., & Tabasum, M. (2018). Customer profiling and segmentation in retail banks using data mining techniques. International journal of advanced research in computer science, 9(4). google scholar
  • Hung, P. D., Lien, N. T. T., & Ngoc, N. D. (2019, March). Customer segmentation using hierarchical agglo-merative clustering. In Proceedings of the 2019 2nd International Conference on Information Science and Systems (pp. 33-37). google scholar
  • Ibrahim, R., Ye, H., L’Ecuyer, P., & Shen, H. (2016). Modeling and forecasting call center arrivals: A litera-ture survey and a case study. International Journal of Forecasting, 32(3), 865-874. google scholar
  • Jintana, J., & Mori, T. (2019). Customer clustering for a new method of marketing strategy support within the courier business. Academia Book Chapter, 31(2), 1-19. google scholar
  • Kadir, M. A., & Achyar, A. (2019). Customer Segmentation on Online Retail using RFM Analysis: Big Data Case of Bukku. id. google scholar
  • Klement, P., & Snasel, V (2011). Using SOM in the performance monitoring of the emergency call-taking system. Simulation Modelling Practice and Theory, 19(1), 98-109. google scholar
  • Knapc^kovâ, L., Behûnova, A., & Behûn, M. (2020). Using a discrete event simulation as an effective met-hod applied in the production of recycled material. Advances in Production Engineering & Management, 15(4), 431-440. google scholar
  • Koole, G., & Pot, A. (2006). An overview of routing and staffing algorithms in multi-skill customer contact centers. google scholar
  • Lam, K., & Lau, R. S. M. (2004). A simulation approach to restructuring call centers. Business Process Ma-nagement Journal. google scholar
  • Legros, B., Jouini, O., & Koole, G. (2017). A uniformization approach for the dynamic control of queueing systems with abandonments. Operations Research, 66(1), 200-209 google scholar
  • Liu, F., & Deng, Y. (2020). Determine the number of unknown targets in Open World based on Elbow met-hod. IEEE Transactions on Fuzzy Systems. google scholar
  • Ma, J., Kim, N., & Rothrock, L. (2011). Performance assessment in an interactive call center workforce simulation. Simulation Modelling Practice and Theory, 19(1), 227-238 google scholar
  • Maheshwari, K., Khapekar, R., Bahl, A., & Bhatia, K. (2019). Credit Profile of E-Commerce Customer. google scholar
  • Maraghi, M., Adibi, M. A., & Mehdizadeh, E. (2020). Using RFM Model and Market Basket Analysis for Segmenting Customers and Assigning Marketing Strategies to Resulted Segments. Journal of Applied Intelligent Systems and Information Sciences, 1(1), 35-43. google scholar
  • Mehrotra, V., & Fama, J. (2003, December). Call center simulation modeling: methods, challenges, and opportunities. In Proceedings of the 35th conference on Winter simulation: driving innovation (pp. 135143). Winter Simulation Conference. google scholar
  • Monks, T., Currie, C. S., Onggo, B. S., Robinson, S., Kunc, M., & Taylor, S. J. (2019). Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines. Journal of Simulation, 13(1), 55-67. google scholar
  • Mousavi, S., Boroujeni, F. Z., & Aryanmehr, S. (2020). Improving customer clustering by optimal selection of cluster centroids in k-means and k-medoids algorithms. Journal of Theoretical and Applied Information Technology, 98(18). google scholar
  • Nainggolan, R., Perangin-angin, R., Simarmata, E., & Tarigan, A. F. (2019, November). Improved the Per-formance of the K-Means Cluster Using the Sum of Squared Error (SSE) optimized by using the Elbow Method. In Journal of Physics: Conference Series (Vol. 1361, No. 1, p. 012015). IOP Publishing. google scholar
  • Namvar, M., Gholamian, M. R., & KhakAbi, S. (2010, January). A two phase clustering method for intel-ligent customer segmentation. In 2010 International Conference on Intelligent Systems, Modelling and Simulation (pp. 215-219). IEEE. google scholar
  • Niyagas, W., Srivihok, A., & Kitisin, S. (2006). Clustering e-banking customers using data mining and mar-keting segmentation. ECTI Transactions on Computer and Information Technology (ECTI-CIT), 2(1), 63-69. google scholar
  • Nugraha, J. A. M. (2020). Application of K-Means Algorithm for Customer Grouping. International Journal of Computer Theory and Engineering, 12(2). google scholar
  • Nwogu, E. C., Iwueze, I. S., & Nlebedim, V. U. (2016). Some tests for seasonality in time series data. Journal of Modern Applied Statistical Methods, 15(2), 24. google scholar
  • Rajagopal, D. (2011). Customer data clustering using data mining technique. arXiv preprint arXiv:1112.2663. google scholar
  • Riskadayanti, O., & Hisjam, M. (2019, April). Discrete-event simulation of a production process for increa-sing the efficiency of a newspaper production. In IOP Conference Series: Materials Science and Engine-ering (Vol. 495, No. 1, p. 012026). IOP Publishing. google scholar
  • Robinson, G., & Morley, C. (2006). Call centre management: responsibilities and performance. International Journal of Service Industry Management. google scholar
  • Rojlertjanya, P. (2019). Customer Segmentation Based on the RFM Analysis Model Using K-Means Cluste-ring Technique: A Case of I.T. Solution and Service Provider in Thailand. google scholar
  • Rudskaia, E., & Eremenko, I. (2019). Digital clustering in customer relationship management. In E3S Web of Conferences (Vol. 135, p. 04010). EDP Sciences. google scholar
  • Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship manage-ment. Technology in society, 24(4), 483-502. google scholar
  • Saberi, M., Hussain, O. K., & Chang, E. (2017). Past, present and future of contact centers: a literature revi-ew. Business Process Management Journal. google scholar
  • Sabuncu, İ., Türkan, E., & Polat, H. (2020). Customer Segmentation And Profiling With RFM Analysis. Turkish Journal of Marketing, 5(1), 22-36. google scholar
  • Sağlam, B., Salman, F. S., Sayın, S., & Türkay, M. (2006). A mixed-integer programming approach to the clustering problem with an application in customer segmentation. European Journal of Operational Re-search, 173(3), 866-879. google scholar
  • Sargent, R. G. (2013). Verification and validation of simulation models. Journal of simulation, 7(1), 12-24. google scholar
  • Shih, M. Y., Jheng, J. W., & Lai, L. F. (2010). A two-step method for clustering mixed categorical and nume-ric data. Tamkang Journal of Science and Engineering, 13(1), 11-19. google scholar
  • Shih, Y. Y., & Liu, C. Y. (2003). A method for customer lifetime value ranking—Combining the analytic hie-rarchy process and clustering analysis. Journal of Database Marketing & Customer Strategy Management, 11(2), 159-172. google scholar
  • Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018, April). Integration k-means clus-tering method and elbow method for identification of the best customer profile cluster. In IOP Conference Series: Materials Science and Engineering (Vol. 336, No. 1, p. 012017). IOP Publishing. google scholar
  • Thomas, M. R., & Shivani, M. P. (2020). Customer Profiling of Alpha. Ushus Journal of Business Manage-ment, 19(1), 75-86. google scholar
  • Troncoso-Palacio, A., Neira-Rodado, D., Ortı'z-Barrios, M., Jimenez-Delgado, G., & Hemandez-Palma, H. (2018, June). Using discrete-event-simulation for improving operational efficiency in laboratories: a case study in pharmaceutical industry. In International Conference on Swarm Intelligence (pp. 440-451). Springer, Cham. google scholar
  • Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmenta-tion. John Wiley & Sons google scholar
  • Umargono, E., Suseno, J. E., & Gunawan, S. V. (2020, October). K-Means Clustering Optimization Using the Elbow Method and Early Centroid Determination Based on Mean and Median Formula. In The 2nd International Seminar on Science and Technology (ISSTEC 2019) (pp. 121-129). Atlantis Press. google scholar
  • Uslu, B. Ç., & Fırat, S. Ü. O. (2019). A Comprehensive Study on Internet of Things Based on Key Artificial Intelligence Technologies and Industry 4.0. In Advanced Metaheuristic Methods in Big Data Retrieval and Analytics (pp. 1-26). IGI Global. google scholar
  • USLU, B. Ç. (2020). Capability model and competence measuring for smart hospital system: an analysis for turkey. International Journal of Health Services Research and Policy, 5(1), 41-50. google scholar
  • Van Buuren, M., Kommer, G. J., van der Mei, R., & Bhulai, S. (2017). EMS call center models with and without function differentiation: A comparison. Operations Research for Health Care, 12, 16-28. google scholar
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2019). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research. google scholar
  • Watson, J. (2012). The Requirements for Being an Analytics-Based Organization. Business Intelligence Jo-urnal, 17(2), 42-44. google scholar
  • Windarto, A. P., Siregar, M. N. H., Suharto, W., Fachri, B., Supriyatna, A., Carolina, I., ... & Toresa, D. (2019, August). Analysis of the K-Means Algorithm on Clean Water Customers Based on the Province. In Jour-nal of Physics: Conference Series (Vol. 1255, No. 1, p. 012001). IOP Publishing. google scholar
  • Windisch, J., Vaatainen, K., Anttila, P., Nivala, M., Laitila, J., Asikainen, A., & Sikanen, L. (2015). Discrete-event simulation of an information-based raw material allocation process for increasing the efficiency of an energy wood supply chain. Applied energy, 149, 315-325. google scholar
  • Ye, L., Qiuru, C., Haixu, X., Yijun, L., & Guangping, Z. (2013). Customer segmentation for telecom with the k-means clustering method. Information Technology Journal, 12(3), 409413. google scholar
  • Yemane, A., Gebremicheal, G., Meraha, T., & Hailemicheal, M. (2020). Productivity improvement through line balancing by using simulation modeling. Journal of Optimization in Industrial Engineering, 13(1), 153-165. google scholar
  • Zalaghi, Z., & Varzi, Y. (2014). Measuring customer loyalty using an extended RFM and clustering techni-que. Management Science Letters, 4(5), 905-912. google scholar


Copy and paste a formatted citation or use one of the options to export in your chosen format



Serper, N.G., Şen, E., & Çalış Uslu, B. (2022). Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization. Istanbul Business Research, 51(1), 189-208.


Serper N G, Şen E, Çalış Uslu B. Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization. Istanbul Business Research. 2022;51(1):189-208.


Serper, N.G.; Şen, E.; Çalış Uslu, B. Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization. Istanbul Business Research, [Publisher Location], v. 51, n. 1, p. 189-208, 2022.

Chicago: Author-Date Style

Serper, Nisan Güniz, and Elif Şen and Banu Çalış Uslu. 2022. “Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization.” Istanbul Business Research 51, no. 1: 189-208.

Chicago: Humanities Style

Serper, Nisan Güniz, and Elif Şen and Banu Çalış Uslu. Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization.” Istanbul Business Research 51, no. 1 (Feb. 2023): 189-208.

Harvard: Australian Style

Serper, NG & Şen, E & Çalış Uslu, B 2022, 'Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization', Istanbul Business Research, vol. 51, no. 1, pp. 189-208, viewed 1 Feb. 2023,

Harvard: Author-Date Style

Serper, N.G. and Şen, E. and Çalış Uslu, B. (2022) ‘Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization’, Istanbul Business Research, 51(1), pp. 189-208. (1 Feb. 2023).


Serper, Nisan Güniz, and Elif Şen and Banu Çalış Uslu. Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization.” Istanbul Business Research, vol. 51, no. 1, 2022, pp. 189-208. [Database Container],


Serper NG, Şen E, Çalış Uslu B. Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization. Istanbul Business Research [Internet]. 1 Feb. 2023 [cited 1 Feb. 2023];51(1):189-208. Available from: doi: 10.26650/ibr.2022.51.951646


Serper, NisanGüniz - Şen, Elif - Çalış Uslu, Banu. Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization”. Istanbul Business Research 51/1 (Feb. 2023): 189-208.


Published Online26.01.2022


Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.


Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.