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Journals / Istanbul Business Research / 2022 / Cilt: 51 - Sayı: 1

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

Nisan Güniz SERPER
Elif ŞEN
Banu ÇALIŞ USLU
Pages
189–208
Publication Date
2022-10-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:The K-means clustering technique was used to determine customer segmentation on 6-million-customer data.
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Conclusion & Impact: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%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Call Center Management · Simulation · Prioritization

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%.

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Cite This Article

SERPER, N. G., ŞEN, E., USLU, B. Ç. (2022). Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization. Istanbul Business Research, Cilt: 51 - Sayı: 1, 189–208. https://ajindex.com/articles/542738

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