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Makine Öğrenmesi Kullanarak Çağrı Merkezine Gelen Çağrıların Tahmin Edilmesi

Year 2021, Volume: 4 Issue: 1, 96 - 101, 02.03.2021
https://doi.org/10.47495/okufbed.824870

Abstract

Çağrı merkezi, bir kuruluş için çok sayıda telefon görüşmesini idare edebilecek şekilde donatılmış bir ofistir ve aramaları tahmin etme yeteneği kilit bir faktördür. Bir şirket, arama sayısını doğru bir şekilde tahmin ederek personel ihtiyaçlarını planlayabilir, hizmet seviyesi gereksinimlerini karşılayabilir, müşteri memnuniyetini artırabilir ve diğer birçok optimizasyondan yararlanabilir. Bu çalışmada, bir çağrı merkezindeki gelen çağrı sayısını tahmin etmek için zaman gecikmeleri ile entegreli Çok Katmanlı Algılayıcı (Multilayer Perceptron - MLP) ve Uzun Kısa Vadeli Bellek (Long-Short Term Memory – LSTM) tabanlı modeller geliştirilmiştir. 12, 24, 36 ve 48’lik tahminler üretilip, tahmin modellerinin performansı Ortalama Mutlak Hata (Mean Absolute Error - MAE) kullanılarak değerlendirilmiştir. Sonuçlar, MLP tabanlı modellerin MAE değerlerinin 1,50 ile 13,58 arasında, LSTM tabanlı modellerin ise 19,99 ile 66,74 arasında değiştiğini göstermektedir.

References

  • Mehrotra, V., Ozlük, O., & Saltzman, R. Intelligent procedures for intra‐day updating of call center agent schedules. Production and Operations Management 2010; 19(3), 353-367.
  • Channouf, N., & L'Ecuyer, P. A normal copula model for the arrival process in a call center. International Transactions in Operational Research 2012; 19(6), 771-787.
  • Kim, T., Kenkel, P., & Brorsen, B. W. Forecasting hourly peak call volume for a rural electric cooperative call center. Journal of Forecasting 2012; 31(4), 314-329.
  • Millán‒Ruiz, D., & Hidalgo, J. I. Forecasting call centre arrivals. Journal of Forecasting 2013; 32(7), 628-638.
  • Bastianin, A., Galeotti, M., & Manera, M. Statistical and economic evaluation of time series models for forecasting arrivals at call centers. Empirical Economics 2016; 1-33.
  • Jalal, M. E., Hosseini, M., & Karlsson, S. Forecasting incoming call volumes in call centers with recurrent neural networks. Journal of Business Research 2016 ; 69(11), 4811-4814.
  • Mohammed, R. A. Using Personalized Model to Predict Traffic Jam in Inbound Call Center. EAI Endorsed Transactions on Scalable Information Systems 2017; 4(12).
  • S. Moazeni and R. Andrade, "A Data-Driven Approach to Predict an Individual Customer's Call Arrival in Multichannel Customer Support Centers," 2018 IEEE International Congress on Big Data (BigData Congress), San Francisco, CA, 2018, pp. 66-73.
  • Li S., Wang Q., Koole G. Predicting call center performance with machine learning. In INFORMS International Conference on Service Science 2018, pp. 193-199.
  • Barrow D., Kourentzes N. The impact of special days in call arrivals forecasting: A neural network approach to modelling special days. European Journal of Operational Research 2018; 264(3), 967-977.

Forecasting Call Center Arrivals Using Machine Learning

Year 2021, Volume: 4 Issue: 1, 96 - 101, 02.03.2021
https://doi.org/10.47495/okufbed.824870

Abstract

A call center is an office equipped to handle a large volume of telephone calls for an organization, for which the ability to forecast calls is a key factor. By forecasting the number of calls accurately, a company can plan staffing needs, meet service level requirements, improve customer satisfaction and benefit from many other optimizations. In this paper, we develop Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) based models combined with time lags to forecast the number of call arrivals in a call center. We forecast 12, 24, 36 and 48 values ahead and the performance of the forecasting models has been evaluated using the Mean Absolute Error (MAE). The MLP based model results show that the MAE values change between 1,50 and 13,58 and LSTM based model results show that the MAE values change between 19,99 and 66,74.

References

  • Mehrotra, V., Ozlük, O., & Saltzman, R. Intelligent procedures for intra‐day updating of call center agent schedules. Production and Operations Management 2010; 19(3), 353-367.
  • Channouf, N., & L'Ecuyer, P. A normal copula model for the arrival process in a call center. International Transactions in Operational Research 2012; 19(6), 771-787.
  • Kim, T., Kenkel, P., & Brorsen, B. W. Forecasting hourly peak call volume for a rural electric cooperative call center. Journal of Forecasting 2012; 31(4), 314-329.
  • Millán‒Ruiz, D., & Hidalgo, J. I. Forecasting call centre arrivals. Journal of Forecasting 2013; 32(7), 628-638.
  • Bastianin, A., Galeotti, M., & Manera, M. Statistical and economic evaluation of time series models for forecasting arrivals at call centers. Empirical Economics 2016; 1-33.
  • Jalal, M. E., Hosseini, M., & Karlsson, S. Forecasting incoming call volumes in call centers with recurrent neural networks. Journal of Business Research 2016 ; 69(11), 4811-4814.
  • Mohammed, R. A. Using Personalized Model to Predict Traffic Jam in Inbound Call Center. EAI Endorsed Transactions on Scalable Information Systems 2017; 4(12).
  • S. Moazeni and R. Andrade, "A Data-Driven Approach to Predict an Individual Customer's Call Arrival in Multichannel Customer Support Centers," 2018 IEEE International Congress on Big Data (BigData Congress), San Francisco, CA, 2018, pp. 66-73.
  • Li S., Wang Q., Koole G. Predicting call center performance with machine learning. In INFORMS International Conference on Service Science 2018, pp. 193-199.
  • Barrow D., Kourentzes N. The impact of special days in call arrivals forecasting: A neural network approach to modelling special days. European Journal of Operational Research 2018; 264(3), 967-977.
There are 10 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section RESEARCH ARTICLES
Authors

Mohamed Ballouch 0000-0003-3275-0562

Fatih Akay

Sevtap Erdem 0000-0002-9332-2070

Mesut Tartuk 0000-0001-9021-1060

Taha Furkan Nurdağ 0000-0002-0259-2981

Hasan Hüseyin Yurdagül 0000-0002-6866-1644

Publication Date March 2, 2021
Submission Date November 12, 2020
Acceptance Date November 28, 2020
Published in Issue Year 2021 Volume: 4 Issue: 1

Cite

APA Ballouch, M., Akay, F., Erdem, S., Tartuk, M., et al. (2021). Forecasting Call Center Arrivals Using Machine Learning. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 4(1), 96-101. https://doi.org/10.47495/okufbed.824870
AMA Ballouch M, Akay F, Erdem S, Tartuk M, Nurdağ TF, Yurdagül HH. Forecasting Call Center Arrivals Using Machine Learning. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. March 2021;4(1):96-101. doi:10.47495/okufbed.824870
Chicago Ballouch, Mohamed, Fatih Akay, Sevtap Erdem, Mesut Tartuk, Taha Furkan Nurdağ, and Hasan Hüseyin Yurdagül. “Forecasting Call Center Arrivals Using Machine Learning”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 4, no. 1 (March 2021): 96-101. https://doi.org/10.47495/okufbed.824870.
EndNote Ballouch M, Akay F, Erdem S, Tartuk M, Nurdağ TF, Yurdagül HH (March 1, 2021) Forecasting Call Center Arrivals Using Machine Learning. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 4 1 96–101.
IEEE M. Ballouch, F. Akay, S. Erdem, M. Tartuk, T. F. Nurdağ, and H. H. Yurdagül, “Forecasting Call Center Arrivals Using Machine Learning”, Osmaniye Korkut Ata University Journal of Natural and Applied Sciences, vol. 4, no. 1, pp. 96–101, 2021, doi: 10.47495/okufbed.824870.
ISNAD Ballouch, Mohamed et al. “Forecasting Call Center Arrivals Using Machine Learning”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 4/1 (March 2021), 96-101. https://doi.org/10.47495/okufbed.824870.
JAMA Ballouch M, Akay F, Erdem S, Tartuk M, Nurdağ TF, Yurdagül HH. Forecasting Call Center Arrivals Using Machine Learning. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2021;4:96–101.
MLA Ballouch, Mohamed et al. “Forecasting Call Center Arrivals Using Machine Learning”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 4, no. 1, 2021, pp. 96-101, doi:10.47495/okufbed.824870.
Vancouver Ballouch M, Akay F, Erdem S, Tartuk M, Nurdağ TF, Yurdagül HH. Forecasting Call Center Arrivals Using Machine Learning. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2021;4(1):96-101.

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