research-article

Interpretation of net promoter score attributes using explainable AI

Authors:

Ioannis Rallis,

Yannis Markoulidakis,

Ioannis Georgoulas,

George Kopsiaftis,

Maria Kaselimi,

Nikolaos Doulamis,

Anastasios DoulamisAuthors Info & Claims

PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments

Pages 113 - 117

https://doi.org/10.1145/3529190.3529212

Published: 11 July 2022 Publication History

Abstract

Net promoter score (NPS) is a market research metric that measures customer’s satisfaction and its analysis is combined with various parameters/drivers. The paper addresses a core problem in customer experience analytics which is related with the deeper understanding of the drivers of indices in NPS and defines the key drivers that are of utmost importance in describing the customer’s experience. To this end, state of the art Explainable Artificial Intelligence (XAI) techniques are applied so as to reveal the role of certain customer experience features and support companies in their decision making process.

References

[1]

Michal Bugaj, Krzysztof Wrobel, and Joanna Iwaniec. 2021. Model Explainability using SHAP Values for LightGBM Predictions. In 2021 IEEE XVIIth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH). 102–106. https://doi.org/10.1109/MEMSTECH53091.2021.9468078

[2]

Yung-Chun Chang, Chih-Hao Ku, and Chien-Hung Chen. 2020. Using deep learning and visual analytics to explore hotel reviews and responses. Tourism Management 80(2020), 104129.

[3]

Kejia Chen, Jian Jin, and Jiayi Luo. 2021. Big consumer opinion data understanding for Kano categorization in new product development. Journal of Ambient Intelligence and Humanized Computing (2021), 1–20.

[4]

Michael Conklin, Ken Powaga, and Stan Lipovetsky. 2004. Customer satisfaction analysis: Identification of key drivers. European journal of operational research 154, 3 (2004), 819–827.

[5]

Thomas Davenport, Abhijit Guha, Dhruv Grewal, and Timna Bressgott. 2020. How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science 48, 1 (2020), 24–42.

[6]

Md Afnan Hossain, Shahriar Akter, and Venkata Yanamandram. 2022. Customer analytics capabilities in the big data spectrum: a systematic approach to achieve sustainable firm performance. In Research Anthology on Big Data Analytics, Architectures, and Applications. IGI Global, 888–901.

[7]

Maria Kaselimi, Nikolaos Doulamis, Athanasios Voulodimos, Anastasios Doulamis, and Eftychios Protopapadakis. 2021. EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation. IEEE Open Journal of Signal Processing 2 (2021), 1–16. https://doi.org/10.1109/OJSP.2020.3045829

[8]

Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems. 4768–4777.

[9]

Ioannis Markoulidakis, Ioannis Rallis, Ioannis Georgoulas, George Kopsiaftis, Anastasios Doulamis, and Nikolaos Doulamis. 2021. Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. Technologies 9, 4 (2021). https://doi.org/10.3390/technologies9040081

[10]

Sérgio Moro, Paulo Cortez, and Paulo Rita. 2014. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems 62 (2014), 22–31.

[11]

Ioannis Rallis, Ioannis Markoulidakis, Ioannis Georgoulas, and George Kopsiaftis. 2020. A novel classification method for customer experience survey analysis. In Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments. 1–9.

Digital Library

[12]

Amit Sheoran, Sonia Fahmy, Matthew Osinski, Chunyi Peng, Bruno Ribeiro, and Jia Wang. 2020. Experience: towards automated customer issue resolution in cellular networks. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1–13.

Digital Library

[13]

Hanan Yakubu and CK Kwong. 2021. Forecasting the importance of product attributes using online customer reviews and Google Trends. Technological Forecasting and Social Change 171 (2021), 120983.

[14]

Mohamed Zaki, Dalia Kandeil, Andy Neely, and Janet R McColl-Kennedy. 2016. The fallacy of the net promoter score: Customer loyalty predictive model. Cambridge Service Alliance 10 (2016), 1–25.

[15]

Lei Zhang, Xuening Chu, and Deyi Xue. 2019. Identification of the to-be-improved product features based on online reviews for product redesign. International Journal of Production Research 57, 8 (2019), 2464–2479.

Cited By

Ghadiridehkordi AShao JBoojihawon RWang QLi H(2024)Leveraging sentiment analysis via text mining to improve customer satisfaction in UK banksInternational Journal of Bank Marketing10.1108/IJBM-05-2024-0323Online publication date: 29-Nov-2024
https://doi.org/10.1108/IJBM-05-2024-0323

Index Terms

Interpretation of net promoter score attributes using explainable AI

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments

June 2022

704 pages

ISBN:9781450396318

DOI:10.1145/3529190

Conference Chair:
Fillia Makedon

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2022

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This paper is supported by the research project: 4DBeyond: 4DAnalysis Beyond the Visible Spectrum in Real-Life Engineering Applications, project No. HFRI-FM17-2972 funded by the Hellenic Foundation for Research Innovation.

Conference

PETRA '22

PETRA '22: The15th International Conference on PErvasive Technologies Related to Assistive Environments

June 29 - July 1, 2022

Corfu, Greece

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Cited By

Ghadiridehkordi AShao JBoojihawon RWang QLi H(2024)Leveraging sentiment analysis via text mining to improve customer satisfaction in UK banksInternational Journal of Bank Marketing10.1108/IJBM-05-2024-0323Online publication date: 29-Nov-2024
https://doi.org/10.1108/IJBM-05-2024-0323

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