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

A Telecommunication Company Case Study

DOI

10.2991/978-94-6463-080-0_14How to use a DOI?

Keywords

Transactional Net Promoter Score (tNPS); Prediction; Machine learning; Telecommunication company; Case study

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.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rathimala Kannan
AU  - Chee Yoong Yan
AU  - Kannan Ramakrishnan
AU  - Dedy Rahman Wijaya
PY  - 2022
DA  - 2022/12/26
TI  - Prediction of Customer Transactional Net Promoter Score (tNPS) Using Machine Learning
BT  - Proceedings of the International Conference on Technology and Innovation Management (ICTIM 2022)
PB  - Atlantis Press
SP  - 166
EP  - 179
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-080-0_14
DO  - 10.2991/978-94-6463-080-0_14
ID  - Kannan2022
ER  -