Exploring Voice of Customers to Chatbot for Customer Service with Sentiment Analysis

Authors

  • Anggun Siwi Murwati School of Business and Management, Institut Teknologi Bandung, Indonesia
  • Leo Aldianto School of Business and Management, Institut Teknologi Bandung, Indonesia

DOI:

https://doi.org/10.12695/ajtm.2022.15.2.4

Abstract

Abstract. Chatbots have been widely employed across a wide variety of companies and industries, from small- and medium-sized businesses to large corporations, and from e-commerce to financial institutions. Although chatbots have proven to be far more efficient and quicker than human agents, they do not always provide customers with a satisfactory experience because they lack a personal touch. Customer issues are often left unresolved and many are unsatisfied with chatbot services. This is unfavorable for firms that use chatbots for customer services as this jeopardizes their relationship with valued consumers. Thus, customer input is essential to streamline the product innovation process. This study uses a hybrid method involving lexicon-based TextBlob and logistic regression techniques to identify the sentiments of consumers toward chatbots for customer services based on user-generated content on Twitter. The results show that although people generally have positive encounters with chatbots, the gap between positive and negative sentiments is relatively small. This research provides insights that businesses can use to improve chatbot technology based on the voice of the customer to provide users with higher quality customer services in the future, especially since unsatisfied customers could be a threat to a business’s performance. 

Keywords:  chatbot, customer services, sentiment analysis, social media mining, voice of customers.

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References

Adam, M., Wessel, M. & Benlian, A. (2020). AI-based chatbots in customer service and their effects on user compliance. (“Sci-Hub | AI-based chatbots in customer service and their ...”) Electron Markets 31, 427–445. doi: 10.1007/s12525-020-00414-7
Araujo, T., (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behaviours 85, 183-187.
Arsovski, S., Wong, S. and Cheok, A. D. (2018). Open-domain neural conversational agents: The step towards artificial general intelligence. International Journal of Advanced Computer Science and Applications, 9(6), pp. 402-408. doi: 10.14569/IJACSA.2018.090654
Bashir, N., Papamichail, K. N., & Malik, K. (2017). Use of social media applications for supporting new product development processes in multinational corporations. Technological Forecasting and Social Change, 120, 176–183. doi: 10.1016/j.techfore.2017.02.028
Celardo, L., Iezzi, D. F., Vichi, M. (2016). Multi-mode partitioning for text clustering to reduce dimensionality and noises. In: Mayaffe, D., Poudat, C., Vanni, L., Magri, V., Follette, P. (eds) JADT 2016: Statistical Analysis of Textual Data. Les Press de Fac Imprimeur, Nizza.
Charlton, G. (2013). Consumers prefer live chat for customer service: stats. Retrieved from https://econsultancy.com/consumers-prefer-live-chat-for-customer-service-stats\
Chew, C., & Eysenbach, G. (2010). Pandemics in the age of twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE, 5(11), e14118. doi: 10.1371/journal.pone.0014118
Choi, J., Oh, S., Yoon, J., Lee, J.-M., & Coh, B.-Y. (2020). Identification of time-evolving product opportunities via social media mining. Technological Forecasting and Social Change, 156, 120045. doi: 10.1016/j.techfore.2020.120045
Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595. doi: 10.1016/j.jbusres.2018.10.004
Collier, J. E., & Kimes, S. E. (2013). Only if it is convenient. Journal of Service Research, 16(1), 39–51. doi: 10.1177/1094670512458454
Daim, T. U., Chiavetta, D., Porter, A. L., & Saritas, O. (Eds.). (2016). Anticipating future innovation pathways through large data analysis. Berlin: Springer International Publishing.
Følstad, A., & Skjuve, M. (2019). Chatbots for customer service. Proceedings of the 1st International Conference on Conversational User Interfaces - CUI ’19. doi: 10.1145/3342775.3342784
Gnewuch, U., Morana, S. and Maedche, A. (2017). "Towards Designing Cooperative and Social Conversational Agents for Customer Service". Short Paper, to appear in: Proceedings of the International Conference on Information Systems (ICIS)
Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1–6. doi: 10.1016/j.jretai.2016.12.008
Griffin, A., & Hauser, J. R. (1993). The Voice of the Customer. Marketing Science, 12(1), 1–27. doi: 10.1287/mksc.12.1.1
Grover, P., Kar, A. K., Janssen, M., & Ilavarasan, P. V. (2019). Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions – insights from user-generated content on Twitter. Enterprise Information Systems, 13(6), 771–800. doi: 10.1080/17517575.2019.1599446
Hennig-Thurau, T., Andre ́ M., & Paul M. (2010).‘‘When Consumers are Agents: Can Recommender Systems Help Make Better Choices,’’ working paper, Bauhaus-University of Weimar.
Huang, M.-U., Rust, R.T. (2018). Artificial intelligence in service. J. Serv. Res. 21 (2), 155–172.
Kärkkäinen, H., Piippo, P., Puumalainen, K., & Tuominen, M. (2001). Assessment of hidden and future customer needs in Finnish business-to-business companies. R&D Management, 31(4), 391–407.
Katadata.co.id. (2018). Kata.ai, Startup Di Balik Layanan Chatbot Veronika dan Sabrina. Retrieved October 14, 2019, from katadata.co.id: https://katadata.co.id/berita/2018/06/14/kataai-startup-di-balik-layanan-chatbot-veronika-dansabrina.
Kata.ai. (n.d.). About Jemma Unilever. Retrieved October 6, 2019, from kata.ai: https://kata.ai/story/jemma.
Katadata.co.id. (2018). Telkomsel dan Indosat Bersaing Kembangkan Internet of Things. Retrieved October 14, 2019, from Katadata.co.id: https://katadata.co.id/berita/2018/07/25/telkomsel-dan-indosat-bersaing-kembangkan-internet-ofthings.
Kristensson, P., Matthing, J., and Johansson, N. (2008). Key strategies for the successful involvement of customers in the co-creation of new technology-based services. International Journal of Service Industry Management, 19(4), 474-491
Luger, E., & Sellen, A. (2016). Like having a really bad PA: The gulf between user expectation and experience of conversational agents. In: Proceedings of the CHI Conference on Human Factors in Computing Systems.
Manchulenko, N. (2001). Applying Axiomatic Design Principles to the House of Quality. M.Sc. Dissertation, The University of Windsor, Canada
Maudlin, M. (1994). ChatterBots, Tiny Muds, and the Turing Test: Entering the Loebner Prize competition. In Proceedings of the Eleventh National Conference on Artificial Intelligence. AAAIPress.
Moore, S. (2018). Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020. Retrieved February 20, 2018, from https://www.gartner.com/newsroom/id/3858564
Müller, Vincent C., and Bostrom, Nick (forthcoming 2014), Future progress in artificial intelligence: A Survey of Expert Opinion, in Vincent C. Müller (ed.), Fundamental Issues of Artificial Intelligence (Synthese Library; Berlin: Springer).
Murtarelli, G., Gregory, A., Stefania, R., (2020). A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots. Journal of Business Research 129 (7).
Ngai, E. W. T., Lee, M. C. M., Luo, M., Chan, P. S. L., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, 101098. doi: 10.1016/j.elerap.2021.101098
Nguyen, M.-H., 2020. The Latest Market Research, Trends, and Landscape in the Growing AI Chatbot Industry. Business Insider. Retrieved from. https://www.businessinsider. com/chatbot-market-stats-trends?r=AU&IR=T.
Pak, A., & Paroubek, P. (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREC.
Pantano, E., & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services, 55, 102096. doi: 10.1016/j.jretconser.2020.102096
Reddy, T. (2017b). How chatbots can help reduce customer service costs by 30%. Retrieved from https://www.ibm.com/blogs/watson/2017/10/how-chatbots-reduce-customer-service-costs-by-30-percent/
Roman, E. (2010). Voice-of-the-customer marketing: A revolutionary 5-step process to create customers who care, spend, and stay. McGraw Hill Professional.
Saura, J. R., Palacios-Marqués, D., & Ribeiro-Soriano, D. (2021). Using data mining techniques to explore security issues in smart living environments in Twitter. Computer Communications, 179, 285–295. doi: 10.1016/j.comcom.2021.08.021
Schneider, C. (2017). Ten reasons why AI-powered, automated customer service is the future. https://www.ibm.com/blogs/watson/2017/10/10-reasons-ai-powered-automated-customer-service-future/
Sheehan, B., Jin, H. S., & Gottlieb, U., (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research Volume 115, July 2020, Pages 14-24.
Shumanov, M., &Johnson, L. (2021). Making conversations with chatbots more personalized. Computers in Human Behaviours 117.
Sibona, C., Walczak, S., & White Baker, E. (2020). A Guide for Purposive Sampling on Twitter. Communications of the Association for Information Systems, 46, pp-pp. doi: 10.17705/ 1CAIS.04622
Sohangir, S., Petty, N., & Wang, D. (2018, January). Financial sentiment lexicon analysis. 2018 IEEE 12th International Conference on Semantic Computing (ICSC). doi: 10.1109/icsc.2018.00052
Song, M., X. Xing, Y. Duan, J. Cohen, and J. Mou. 2022. Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer Services 66: 102900.
Tran, A. D., Pallant, J. I., & Johnson, L. W. (2021). Exploring the impact of chatbots on consumer sentiment and expectations in retail. Journal of Retailing and Consumer Services, 63, 102718. doi: 10.1016/j.jretconser.2021.102718
Wang, C., Harris, J., & Patterson, P. (2013). The roles of habit, self-efficacy, and satisfaction in driving continued use of self-service technologies. Journal of Service Research, 16(3), 400–414. doi: 10.1177/1094670512473200
Woodruff, R. B. (1997). Customer Value: The Next Source for Competitive Advantage. Journal of the Academy of Marketing Science 25 (2), 139-154.
Xu, Y., Shieh, C.-H., van Esch, P., & Ling, I. L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal, 28(4), 189-199. doi:10.1016/j.ausmj.2020.03.005

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Submitted

2022-06-07

Accepted

2022-10-27

Published

2022-12-05

How to Cite

Murwati, A. S., & Aldianto, L. (2022). Exploring Voice of Customers to Chatbot for Customer Service with Sentiment Analysis. The Asian Journal of Technology Management (AJTM), 15(2), 141–153. https://doi.org/10.12695/ajtm.2022.15.2.4

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Articles