Prediction of Customer Transactional Net Promoter Score (tNPS) Using Machine Learning

Rathimala Kannan (), Chee Yoong Yan, Kannan Ramakrishnan and Dedy Rahman Wijaya
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Rathimala Kannan: Multimedia University, Department of Information Technology, Faculty of Management
Chee Yoong Yan: Multimedia University, Faculty of Management
Kannan Ramakrishnan: Multimedia University, Faculty of Computing and Informatics
Dedy Rahman Wijaya: Telkom University, School of Applied Science

A chapter in Proceedings of the International Conference on Technology and Innovation Management (ICTIM 2022), 2022, pp 166-179 from Springer

Abstract: Abstract In many retail organisations, transactional Net Promoter Score (tNPS) is used to quantify customer satisfaction. It is also one of the alternative measures used in customer retention strategies and assessing customer loyalty. Customers who are dissatisfied rarely express their dissatisfaction before leaving. This makes customer retention strategies more difficult for business organisations. Machine learning can be leveraged to predict the tNPS using the past data which would assist in data-driven decision making to identify the unhappy customers. Case study company provided the tNPS report dataset comprises 10715 rows and 30 columns, and the service request report dataset has 28,7729 rows and 41 columns. Five machine learning models were developed by following Cross-Industry Standard Process for Data Mining research method. The best model is selected by the F-Score metric. Multilayer perceptron neural network performed the best compared to Decision Tree, Random Forest, Gradient Boosted Trees, and Logistic Regression with F- Score 0. 876. This finding would be useful to identify the customers service request that will score a high tNPS. The implications and limitations are discussed.

Keywords: Transactional Net Promoter Score (tNPS); Prediction; Machine learning; Telecommunication company; Case study (search for similar items in EconPapers)
Date: 2022
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DOI: 10.2991/978-94-6463-080-0_14

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