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

Year 2022, Volume: 51 Issue: 1, 189 - 208, 01.05.2022

Nisan Serper Elif Şen Banu Çalış

https://doi.org/10.26650/ibr.2022.51.951646

Abstract

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%.

Keywords

Call Center Management, Simulation, Prioritization, Data Analytics, Customer Segmentation

Thanks

We would like to thank the editor and anonymous reviewers for their time and valuable contribution.

References

• 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.
• Aktekin, T. (2014). Call center service process analysis: Bayesian parametric and semi-parametric mixture modeling. European Journal of Operational Research, 234(3), 709-719.
• Alotaibi, Y., & Liu, F. (2013). Average waiting time of customers in a new queue system with different classes. Business Process Management Journal.
• Andrade, C. (2019). The P-value and statistical significance: misunderstandings, explanations, challenges, and alternatives. Indian Journal of Psychological Medicine, 41(3), 210-215.
• Anton, J. (2000). The past, present, and future of customer access centers. International Journal of Service Industry Management.
• 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.
• Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152.
• 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.
• Banks, J., Carson II, J. S., & Barry, L. (2005). Discrete-event system simulation fourth edition
• 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).
• Calis, B. (2016). Agent-Based Simulation Model for Profit Maximization. Journal of Management and Information Science, 4(1), 26-33.
• Carnein, M., & Trautmann, H. (2019, April). Customer segmentation based on transactional data using stream clustering. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 280-292). Springer, Cham.
• Cui, M. (2020). Introduction to the K-Means Clustering Algorithm Based on the Elbow Method. Accounting, Auditing and Finance, 1(1), 5-8.
• David, F. R. (2013). Strategic Management: Concepts and Cases. Pearson.
• 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 response (IVR) and the Internet. Social science research, 38(1), 1-18.
• Doomun, R., & Jungum, N. V. (2008). Business process modelling, simulation and reengineering: call centres. Business Process Management Journal.
• Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
• Farajian, M. A., & Mohammadi, S. (2010). Mining the banking customer behavior using clustering and association rules methods.
• Farruh, K. (2019). Consumer Life Cycle and Profiling: A Data Mining Perspective. In Consumer Behavior and Marketing. IntechOpen.
• Feinberg, R., De Ruyter, K., & Bennington, L. (2005). Cases in call center management: great ideas (th) at work. Purdue University Press.
• 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.
• Figueiredo, V., Duarte, F. J., Rodrigues, F., Vale, Z., & Gouveia, J. (2003, September). Electric energy customer characterization by clustering. In Proc. ISAP.
• 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 Computation Technologies (ICICT) (pp. 570-575). IEEE.
• Greco, F., & Polli, A. (2020). Emotional Text Mining: Customer profiling in brand management. International Journal of Information Management, 51, 101934.
• 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.
• Hahnke, J. (2000). The CRM Lifecycle–Without CRM Analytics, Your Customers Won’t Even Know You’re There. Defying the Limits, 159-164.
• 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).
• Hung, P. D., Lien, N. T. T., & Ngoc, N. D. (2019, March). Customer segmentation using hierarchical agglomerative clustering. In Proceedings of the 2019 2nd International Conference on Information Science and Systems (pp. 33-37).
• Ibrahim, R., Ye, H., L’Ecuyer, P., & Shen, H. (2016). Modeling and forecasting call center arrivals: A literature survey and a case study. International Journal of Forecasting, 32(3), 865-874.
• 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.
• Kadir, M. A., & Achyar, A. (2019). Customer Segmentation on Online Retail using RFM Analysis: Big Data Case of Bukku. id.
• Klement, P., & Snášel, V. (2011). Using SOM in the performance monitoring of the emergency call-taking system. Simulation Modelling Practice and Theory, 19(1), 98-109.
• Koole, G., & Pot, A. (2006). An overview of routing and staffing algorithms in multi-skill customer contact centers.
• Lam, K., & Lau, R. S. M. (2004). A simulation approach to restructuring call centers. Business Process Management Journal.
• 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 Liu, F., & Deng, Y. (2020). Determine the number of unknown targets in Open World based on Elbow method. IEEE Transactions on Fuzzy Systems.
• 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 Maheshwari, K., Khapekar, R., Bahl, A., & Bhatia, K. (2019). Credit Profile of E-Commerce Customer.
• 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.
• 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. 135-143). Winter Simulation Conference.
• 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.
• 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).
• Nainggolan, R., Perangin-angin, R., Simarmata, E., & Tarigan, A. F. (2019, November). Improved the Performance 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.
• Namvar, M., Gholamian, M. R., & KhakAbi, S. (2010, January). A two phase clustering method for intelligent customer segmentation. In 2010 International Conference on Intelligent Systems, Modelling and Simulation (pp. 215-219). IEEE.
• Niyagas, W., Srivihok, A., & Kitisin, S. (2006). Clustering e-banking customers using data mining and marketing segmentation. ECTI Transactions on Computer and Information Technology (ECTI-CIT), 2(1), 63-69.
• Nugraha, J. A. M. (2020). Application of K-Means Algorithm for Customer Grouping. International Journal of Computer Theory and Engineering, 12(2).
• 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. Rajagopal, D. (2011). Customer data clustering using data mining technique. arXiv preprint arXiv:1112.2663.
• Robinson, G., & Morley, C. (2006). Call centre management: responsibilities and performance. International Journal of Service Industry Management.
• Rojlertjanya, P. (2019). Customer Segmentation Based on the RFM Analysis Model Using K-Means Clustering Technique: A Case of I.T. Solution and Service Provider in Thailand.
• Rudskaia, E., & Eremenko, I. (2019). Digital clustering in customer relationship management. In E3S Web of Conferences (Vol. 135, p. 04010). EDP Sciences.
• Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in society, 24(4), 483-502.
• Saberi, M., Hussain, O. K., & Chang, E. (2017). Past, present and future of contact centers: a literature review. Business Process Management Journal.
• Sabuncu, İ., Türkan, E., & Polat, H. (2020). Customer Segmentation And Profiling With RFM Analysis. Turkish Journal of Marketing, 5(1), 22-36.
• 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 Research, 173(3), 866-879.
• Sargent, R. G. (2013). Verification and validation of simulation models. Journal of simulation, 7(1), 12-24.
• Shih, M. Y., Jheng, J. W., & Lai, L. F. (2010). A two-step method for clustering mixed categorical and numeric data. Tamkang Journal of Science and Engineering, 13(1), 11-19.
• Shih, Y. Y., & Liu, C. Y. (2003). A method for customer lifetime value ranking—Combining the analytic hierarchy process and clustering analysis. Journal of Database Marketing & Customer Strategy Management, 11(2), 159-172.
• Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018, April). Integration k-means clustering 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.
• Thomas, M. R., & Shivani, M. P. (2020). Customer Profiling of Alpha. Ushus Journal of Business Management, 19(1), 75-86.
• Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmentation. John Wiley & Sons
• 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.
• 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.
• 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.
• 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.
• 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.
• Watson, J. (2012). The Requirements for Being an Analytics-Based Organization. Business Intelligence Journal, 17(2), 42-44.
• 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 Journal of Physics: Conference Series (Vol. 1255, No. 1, p. 012001). IOP Publishing.
• 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.
• Zalaghi, Z., & Varzi, Y. (2014). Measuring customer loyalty using an extended RFM and clustering technique. Management Science Letters, 4(5), 905-912.