Abstract
It is of utmost importance for marketing academics and service industry practitioners to understand the factors that influence customer satisfaction. This study proposes a novel framework to analyze open-ended survey data and extract drivers of customer satisfaction. This is done automatically via deep learning models for natural language processing. According to 11 drivers acknowledged by the marketing literature to determine customer experience, the data is cast into a multi-label classification problem. This expert system not only supports the automatic analysis of new data but also ranks the drivers according to their importance to various service industries and provides important insights into their applications. Experiments carried out using 25,943 customer survey responses related to 39 service companies in 13 different economic sectors show that the drivers can be identified accurately.
| Original language | English |
|---|---|
| Article number | 118309 |
| Journal | Expert Systems with Applications |
| Volume | 209 |
| DOIs | |
| State | Published - 15 Dec 2022 |
Bibliographical note
Funding Information:The authors gratefully acknowledge financial support from ANID , PIA-BASAL AFB180003 and FONDECYT-Chile , grants 1200221 and 12200007 . The authors would like to thank Slodoban Ivanovic for his valuable work on this project, and Alco Consulting for providing the necessary information for this research. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions for improving the quality of the paper.
Publisher Copyright:
© 2022 Elsevier Ltd
Keywords
- Analytics
- BERT
- Customer feedback
- Customer satisfaction
- Deep learning
- Natural language processing