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Forecasting Customer Support Resolution Times Through Automated Machine Learning

Author

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  • Anton A. Gerunov

    (Faculty of Economics and Business Administration, Sofia University “St. Kliment Ohridski”, Sofia)

Abstract

This article focuses on modeling and forecasting the resolution time of customer support tickets. To this end we leverage data from a process aware information system and compare manual training of several state-of-the-art benchmark models (neural network, regression, k-Nearest neighbors, random forest, and support vector machine) to automated model training using the H2O framework. The best performer among the automated machine learning models has much higher forecast accuracy than the benchmark models. This indicates that automated machine learning is a feasible way to approach process modeling problems and may be fruitfully utilized to forecast relevant process metrics.

Suggested Citation

  • Anton A. Gerunov, 2022. "Forecasting Customer Support Resolution Times Through Automated Machine Learning," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 19(2), pages 1-11.
    • Handle: RePEc:neo:journl:v:19:y:2022:i:2:p:1-11
    DOI: 10.37708/em.swu.v19i2.1
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    More about this item

    Keywords

    customer support; resolution time; business process mining; prediction; automated machine learning; AutoML; H2O framework;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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