DETERMINATION OF THE OPTIMAL CONTROLLABLE KEY INDICATOR OF CALL CENTER IN ORDER TO INCREASE EFFICIENCY FOR GENERATING INCOME

Authors

DOI:

https://doi.org/10.37943/15SNLS1783

Keywords:

call center, indicators, operator, call service center, data analysis, machine learning, call center quality, optimization

Abstract

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.

References

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.5.2.79.16071

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

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Published

2023-09-30

How to Cite

Amirgaliyev, B., Abdirakhmanova, M., Baishemirov, Z. ., & Yegemberdiyeva, G. . (2023). DETERMINATION OF THE OPTIMAL CONTROLLABLE KEY INDICATOR OF CALL CENTER IN ORDER TO INCREASE EFFICIENCY FOR GENERATING INCOME. Scientific Journal of Astana IT University, 15(15), 5–15. https://doi.org/10.37943/15SNLS1783

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