Call Center Call Count Prediction Model by Machine Learning

Call Center Call Count Prediction Model by Machine Learning
  • Yunhwan Keon; 
  • Hyuna Kim; 
  • Jin Young Choi; 
  • 김동호; 
  • Su Young Kim; 
  • 외 1명

초록

This paper is to introduce efficient human resource management in call center industry by coming up with a model that predicts how much the staffing is most appropriate at such a call center for a given hour period. The project aims to understand the characteristics of call center data through exploratory data analysis, and to test the effectiveness of call center staffing by formulating the rule based on a machine learning model. We initially conducted exploratory data analysis to find insights and implemented a machine learning model to predict the value of the target variable which consequently translated into the number of human receptionists preferred. From our intensive experiments, we found that Random Forest gave the best performance for call center data analysis.

키워드

machine learning; prediction model; classification; random forest
제목
Call Center Call Count Prediction Model by Machine Learning
제목 (타언어)
Call Center Call Count Prediction Model by Machine Learning
저자
Yunhwan Keon; Hyuna Kim; Jin Young Choi; 김동호; Su Young Kim; Seonho Kim
DOI
10.14801/JAITC.2018.8.1.31.
발행일
2018-07
저널명
한국정보기술학회 영문논문지
권
8
호
1
페이지
31 ~ 42