Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Istanbul University Press
2022-04-01
|
| Series: | Istanbul Business Research |
| Subjects: | |
| Online Access: | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/BC7BFE82CDD84756AE31EDDB8337542E |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849314248506736640 |
|---|---|
| author | Nisan Güniz Serper Elif Şen Banu Çalış Uslu |
| author_facet | Nisan Güniz Serper Elif Şen Banu Çalış Uslu |
| author_sort | Nisan Güniz Serper |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-0967b256706c4aa19e26b4b5aa59bba1 |
| institution | Kabale University |
| issn | 2630-5488 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Istanbul University Press |
| record_format | Article |
| series | Istanbul Business Research |
| spelling | doaj-art-0967b256706c4aa19e26b4b5aa59bba12025-08-20T03:52:32ZengIstanbul University PressIstanbul Business Research2630-54882022-04-0151118920810.26650/ibr.2022.51.951646123456Discrete Event Simulation Model Performed with Data Analytics for a Call Center OptimizationNisan Güniz Serper0https://orcid.org/0000-0001-8981-3048Elif Şen1https://orcid.org/0000-0002-0056-3204Banu Çalış Uslu2https://orcid.org/0000-0001-8214-825XMarmara Üniversitesi, İstanbul, TürkiyeMarmara Üniversitesi, İstanbul, TürkiyeMarmara Üniversitesi, İstanbul, TürkiyeOptimization 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%.https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/BC7BFE82CDD84756AE31EDDB8337542Ecall center managementsimulationprioritizationdata analyticscustomer segmentation |
| spellingShingle | Nisan Güniz Serper Elif Şen Banu Çalış Uslu Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization Istanbul Business Research call center management simulation prioritization data analytics customer segmentation |
| title | Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization |
| title_full | Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization |
| title_fullStr | Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization |
| title_full_unstemmed | Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization |
| title_short | Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization |
| title_sort | discrete event simulation model performed with data analytics for a call center optimization |
| topic | call center management simulation prioritization data analytics customer segmentation |
| url | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/BC7BFE82CDD84756AE31EDDB8337542E |
| work_keys_str_mv | AT nisangunizserper discreteeventsimulationmodelperformedwithdataanalyticsforacallcenteroptimization AT elifsen discreteeventsimulationmodelperformedwithdataanalyticsforacallcenteroptimization AT banucalısuslu discreteeventsimulationmodelperformedwithdataanalyticsforacallcenteroptimization |