DETERMINATION OF THE OPTIMAL CONTROLLABLE KEY INDICATOR OF CALL CENTER IN ORDER TO INCREASE EFFICIENCY FOR GENERATING INCOME
Keywords:call center, indicators, operator, call service center, data analysis, machine learning, call center quality, optimization
This paper focuses on call centers, which have become a common means of communication with potential customers in various companies. Specifically, this paper analyzes call center data and the importance of assessing key indicators for evaluating call center performance. The questions this paper addresses are the criteria for evaluating call center quality and the methods for analyzing call center data. Previous research has shown the significance of call centers as the "face of the company," with the quality of their work reflecting how efficiently a company will serve its customers ' requests in the future. The main goal of this paper is to fill a gap in previous research by identifying the main controlled key indicator for call center quality and to suggest ways to improve efficiency. By using analytical methods to examine call center data, this paper identifies the most important criteria for call center quality and provides recommendations for enhancing service quality.
The main findings of this paper show the importance of call center operator performance in determining call center performance which affects company revenue. By evaluating key indicators such as the number of operators, this paper demonstrates how call centers can reduce service costs and improve efficiency. During the analysis using call center data for two years, it turned out that the company had expenses 1/3 of the total amount of maintenance compared to the previous year, which is not effective in terms of economy. Operational planning has a direct impact on operators’ costs and the main cost component is the hourly cost of operators. If optimal planning turns out to be at least 10% better than the arrangement set in the call center, company will save a good amount. The significance of this paper lies in its potential to improve the quality of service in call centers and its contribution to the field of customer service management. By providing insight into the importance of call center efficiency, this research offers recommendations for predicting the optimal number of operators to improve the customer experience with reducing service costs.
McKinsey & Company. (2019, February 1). How advanced analytics can help contact centers put the customer first. https://www.mckinsey.com/capabilities/operations/our-insights/how-advancedanalytics-can-help-contact-centers-put-the-customer-first
Deloitte. (2021, June 30). Digital research determines contact centers will become experience hubs for brands in 2021 and beyond. https://www.deloitte.com/global/en/about/press-room/digital-research-determines-contact-centers-will-become-experience-hubs-for-brands-in-2021-and-beyond.html
PwC Advisory Services. (2018). Consumer Intelligence Series: Customer experience (CX): The way to a customer’s heart. https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/pwc-consumer-intelligence-series-customer-experience.pdf
Aamir, M., Mahfooz, O., & Memon, M. (2016). Role of Contact Center for Smart Cities. Pakistan Journal of Engineering, Technology & Science, 3. https://doi.org/10.22555/pjets.v3i1.689
Koole, G. (2013). Call center optimization. Lulu.com.
Leshchinskaya, E., & Tumanbayeva, K. (2014). Forecasting outgoing traffic of a call center. Vestnik AUES, (3), 60-66.
Goldstein, B.S., & Freinkman, V.A. (2006). Call-centers and Computer telephony. BHV.
Malov, A.V. (2010). Methods and means of ensuring fault tolerance and call centers based on IP telephony.
Gans, N., Koole, G., & Mandelbaum, A. (2003). Telephone call centers: Tutorial, review, and research prospects. Manufacturing & Service Operations Management, 5(2), 79-141. https://doi.org/10.1287/msom.188.8.131.5271
Bernett, H.G., Fischer, M.J., & Masi, D.M.B. (2002). Blended call center performance analysis. IT professional, 4(2), 33-38. https://doi.org/10.1109/MITP.2002.1000458
Ding, S., & Koole, G. (2022). Optimal call center forecasting and staffing. Probability in the Engineering and Informational Sciences, 36(2), 254-263.
Jouini, O., Koole, G., & Roubos, A. (2011). Performance indicators for call centers with impatience. Submitted for publication, 3(1).
Goldstein, B.S., Isaev, V.I., Mamontova, N.P., & Frankman, V.A. (2006). Analysis, synthesis and quality management of service centers infrastructure. Educational guide.
Balakayeva, G.T., & Darkenbayev, D.K. (2018). Modeling the processing of a large amount of data. Bulletin of the Kazakh National University. Series Mathematics, Mechanics, Computer Science, 97(1), 120-126.
Chen, B.P., & Henderson, S.G. (2001). Two issues in setting call centre staffing levels. Annals of operations research, 108, 175-192.
Gans, N., & Zhou, Y.P. (2002). Managing learning and turnover in employee staffing. Operations Research, 50(6), 991-1006.
Dantzig, G.B. (2002). Linear programming. Operations research, 50(1), 42-47.
Tyler Data & Insights. (2018, November 29). Citizen Service Request (CSR) Call Center Calls. https://data.cincinnatioh.gov/Efficient-Service-Delivery/Citizen-Service-Request-CSR-Call-Center-Calls/k2qr-ck2v
Lu, S.C., Swisher, C.L., Chung, C., Jaffray, D., & Sidey-Gibbons, C. (2023, February 14). On the importance of interpretable machine learning predictions to inform clinical decision making in oncology. Frontiers. https://doi.org/10.3389/fonc.2023.1129380
How to Cite
Copyright (c) 2023 Articles are open access under the Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish a manuscript in this journal agree to the following terms:
- The authors reserve the right to authorship of their work and transfer to the journal the right of first publication under the terms of the Creative Commons Attribution License, which allows others to freely distribute the published work with a mandatory link to the the original work and the first publication of the work in this journal.
- Authors have the right to conclude independent additional agreements that relate to the non-exclusive distribution of the work in the form in which it was published by this journal (for example, to post the work in the electronic repository of the institution or publish as part of a monograph), providing the link to the first publication of the work in this journal.
- Other terms stated in the Copyright Agreement.