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Classifying potential users of live chat services and chatbots

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Abstract

Live chat services and chatbot functionalities are experiencing significant growth within companies in the financial sector and growing consumer interest. But which consumers (segments) are more or less interested in these tools? Companies with an online presence would be well advised to profile potential users of these two important tools. This study seeks to classify potential users of live chat services and chatbots. A telephone interview questionnaire is administered to 342 panelists of a recognized Canadian research firm. A two-step cluster analysis is used to reveal natural groupings in the data set. Research conducted identifies four distinct segments, namely Women divided interest, Men partially interested, Age 35–44 partially interested and Older disinterested. This study brings to the fore information essential to the development of effective marketing strategies for reaching the different segments [e.g. develop and evidence an online end-to-end purchasing process for segment (1); provide good Web visibility for segment (2); ensure the presence of competitive offers and Web comparators for segment (3); and, pursue a more traditional approach for segment (4)]. This study represents the first ever classification of individuals based on their interest in emerging services (live chat services and chatbots).

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References

  • Abu-Salim, T., O.P. Onyia, T. Harrison, and V. Lindsay. 2017. Effects of perceived cost, service quality, and customer satisfaction on health insurance service continuance. Journal of Financial Services Marketing 22 (4): 173–186.

    Google Scholar 

  • Adam, M., M. Wessel, and A. Benlian. 2020. AI-based chatbots in customer service and their effects on user compliance. Electronic Markets. https://doi.org/10.1007/s12525-020-00414-7.

    Article  Google Scholar 

  • Aljukhadar, M., and S. Senecal. 2011. Segmenting the online consumer market. Marketing Intelligence & Planning 29 (4): 421–435.

    Google Scholar 

  • Al-Wugayan, A., L.P. Pleshko, and S.M. Bager. 2008. An investigation of the relationships among consumer satisfaction, loyalty, and market share in Kuwaiti loan services. Journal of Financial Services Marketing 13 (2): 95–106.

    Google Scholar 

  • André, S., C. Dewilde, and R. Luijkx. 2017. The tenure gap in electoral participation: Instrumental motivation or selection bias? Comparing homeowners and tenants across four housing regimes. International Journal of Comparative Sociology 58 (3): 241–265.

    Google Scholar 

  • 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 Behavior 85: 183–189.

    Google Scholar 

  • Benassi, M., S. Garofalo, F. Ambrosini, R.P. Sant’Angelo, R. Raggini, G. De-Paoli, C. Ravani, S. Giovagnoli, M. Orsoni, and G. Piraccini. 2020. Using two-step cluster analysis and latent class cluster analysis to classify the cognitive heterogeneity of cross-diagnostic psychiatric inpatients. Frontiers in Psychology 11: 1085. https://doi.org/10.3389/fpsyg.2020.01085.

    Article  Google Scholar 

  • Berman, C. 2017. The disadvantages of web-based customer service, https://bizfluent.com/info-12107345-disadvantages-webbased-customer-service.html. Accessed August 2018.

  • Blut, M., and C. Wang. 2020. Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science 48 (4): 649–669.

    Google Scholar 

  • Brown, K. 2008. The Auslander test: or, ‘of bots and humans’. International Journal of Performance Arts and Digital Media 4 (2–3): 181–188.

    Google Scholar 

  • Chakrabarti, C., and G.F. Luger. 2015. Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics. Expert Systems with Applications 42 (20): 6878–6897.

    Google Scholar 

  • Chen, C.-H., W.P. Lee, and J.-Y. Huang. 2018. Tracking and recognizing emotions in short text messages from online chatting services. Information Processing & Management 54 (6): 1325–1344.

    Google Scholar 

  • Chiu, T., D.-P. Fang, J. Chen, Y. Wang, and C. Jeris. 2001. A robust and scalable clustering algorithm for mixed type attributes in large database environment. In Proceedings of the 7th ACM SIGKDDD international conference on knowledge discovery and data mining, ACM SIGKDDD, San Francisco, CA, 263–268.

  • Ciechanowski, L., A. Przegalinska, M. Magnuski, and P. Gloor. 2018. In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.01.055 (in press).

    Article  Google Scholar 

  • Corti, K., and A. Gillespie. 2016. Co-constructing intersubjectivity with artificial conversational agents: People are more likely to initiate repairs of mis- understandings with agents represented as human. Computers in Human Behavior 58: 431–442.

    Google Scholar 

  • Crutzen, R., G.-J.Y. Peters, S.D. Portugal, E.M. Fisser, and J.J. Grolleman. 2011. An artificially intelligent chat agent that answers adolescents’ questions related to sex, drugs, and alcohol: An exploratory study. Journal of Adolescent Health 48 (5): 514–519.

    Google Scholar 

  • Dale, R. 2016. The return of the chatbots. Natural Language Engineering 22 (5): 811–817.

    Google Scholar 

  • Elmorshidy, A. 2011. Benefits analysis of live customer support chat in e-commerce websites: Dimensions of a new success model for live customer support chat. In 10th International conference on machine learning and applications and workshops, Honolulu, USA, 2: 325–329.

  • Elmorshidy, A. 2013. Applying the technology acceptance and service quality models to live customer support chat for e-commerce Websites. Journal of Applied Business Research 29 (2): 589–596.

    Google Scholar 

  • Elmorshidy, A., M.M. Mostafa, I. El-Moughrabi, and H. Al-Mezen. 2015. Factors influencing live support chat services: An empirical investigation in Kuwait. Journal of Theoretical and Applied Electronic Commerce Research 10 (3): 63–67.

    Google Scholar 

  • eMarketer. 2017a. Preferred communication channel for customer service inquiries/issues according to US Internet users. http://totalaccess.emarketer.com/chart.aspx?r=210877&ipauth=y, Accessed September 2018.

  • eMarketer. 2017b. US Internet users' preferred channel for customer service, by demographic. http://totalaccess.emarketer.com/chart.aspx?r=212318. Accessed August 2018.

  • eMarketer. 2017c. Demographic profile of US virtual assistant users. http://totalaccess.emarketer.com/chart.aspx?r=208760&ipauth=y. Accessed August 2018.

  • eMarketer. 2017d. Reasons that US Internet users prefer to use a chatbot vs. speak with a human. http://totalaccess.emarketer.com/chart.aspx?r=215577&ipauth=y. Accessed 11 August 2018.

  • eMarketer. 2017e. Important factors when purchasing life insurance according to US Internet users. http://totalaccess.emarketer.com/chart.aspx?r=207308. Accessed August 2018.

  • eMarketer. 2018a. Industries that are using artificial intelligence (AI) to automate business processes according to executives/IT decision-makers worldwide. http://totalaccess.emarketer.com/chart.aspx?r=216439. Accessed September 2018.

  • eMarketer. 2018b. Internet users in select countries who have engaged with a chatbot when contacting a company. http://totalaccess.emarketer.com/chart.aspx?r=220032&ipauth=y. Accessed September 2018.

  • eMarketer. 2019. Demographic profile of US social media news users who get news from social media. https://chart-na1.emarketer.com/231143/demographic-profile-of-us-social-media-news-users-who-news-social-media-by-platform-july-2019-of-total. Accessed 12 October 2020.

  • eMarketer. 2020a. Channels used by US Internet users to communicate with companies, Jan 2020. https://chart-na1.emarketer.com/236680/channels-used-by-us-internet-users-communicate-with-companies-jan-2020-of-respondents. Accessed 8 October 2020.

  • eMarketer. 2020b. How has the coronavirus pandemic changed the way Internet users worldwide interact with companies? https://chart-na1.emarketer.com/238443/how-has-coronavirus-pandemic-changed-way-internet-users-worldwide-interact-with-companies-of-respondents-may-2020. Accessed 12 October 2020.

  • EY. 2014. Reimagining customer relationships. Key findings from the EY Global Consumer Insurance Survey 2014. http://www.ey.com/Publication/vwLUAssets/ey-2014-global-customer-insurance-survey/$FILE/ey-global-customer-insurance-survey.pdf. Accessed 10 September 2018.

  • Fastier, C. 2018. Three AI applications to transform your customer interactions. https://inmoment.wpengine.com/blog/three-ai-applications-to-transform-your-customer-interactions/. Accessed 10 October 2020.

  • Forbes. 2018. Chatbots: A powerful weapon in the business arsenal. https://www.forbes.com/sites/forbestechcouncil/2018/08/29/chatbots-a-powerful-weapon-in-the-business-arsenal/#1cb473494960. Accessed October 2018.

  • Fregolente, A., I. Junqueira, P. Medeiros, and P. Yung. 2019. Active and wealthy Brazilian older adults: Identity and consumption motivations. Journal of Consumer Marketing 36 (5): 633–642.

    Google Scholar 

  • Fryer, L.K., M. Ainley, A. Thompson, A. Gibson, and Z. Sherlock. 2017. Stimulating and sustaining interest in a language course: An experimental comparison of chatbot and human task partners. Computers in Human Behavior 75: 461–468.

    Google Scholar 

  • Geethanjali, S., and A.M.J. Birunda. 2017. Towards building a competent chatbot—An analogy of development framework, design techniques and intelligence. International Journal of Innovative Research in Science, Engineering and Technology 6 (11): 554–562.

    Google Scholar 

  • Gelbard, R., O. Goldman, and I. Spiegler. 2007. Investigating diversity of clustering methods: An empirical comparison. Data and Knowledge Engineering 63 (1): 155–166.

    Google Scholar 

  • Go, E., and S.S. Sundar. 2019. Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior 97: 304–316.

    Google Scholar 

  • Goyat, S. 2011. The basis of market segmentation: A critical review of literature. European Journal of Business and Management 3 (9): 45–54.

    Google Scholar 

  • Gu, Q., H.Q. Zhang, B. King, and S. Huang. 2018. Wine tourism involvement: A segmentation of Chinese tourists. Journal of Travel & Tourism Marketing 35 (5): 633–648.

    Google Scholar 

  • Hill, J., W.R. Ford, and I.G. Farreras. 2015. Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior 49: 245–250.

    Google Scholar 

  • Hussain, S., Sianaki O. Ameri, and N. Ababneh. 2019. A Survey on conversational agents/chatbots classification and design techniques. In Web, artificial intelligence and network applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol. 927, ed. L. Barolli, M. Takizawa, F. Xhafa, and T. Enokido. Cham: Springer. https://doi.org/10.1007/978-3-030-15035-8_93

    Chapter  Google Scholar 

  • Insurance Bureau of Canada. 2017. Assurance habitation: 37 % des locataires jouent avec le feu. http://www.bac-quebec.qc.ca/data/fr/2017-09-06_Communique_Assurance_locataires.pdf. Accessed March 2019.

  • Jayawardhena, C., A. Kuckertz, H. Karjaluoto, and T. Kautonen. 2009. Antecedents to permission based mobile marketing: An initial examination. European Journal of Marketing 43 (3/4): 473–499.

    Google Scholar 

  • Kang, L., X. Wang, C.H. Tan, and J.L. Zhao. 2014. Understanding the antecedents and consequences of live-chat use in e-commerce context. In Proceedings of the international conference on HCI in business, Greece, 504–515.

  • Kau, A.K., Y.E. Tang, and S. Ghose. 2003. Typology of online shoppers. Journal of Consumer Marketing 20 (2): 139–156.

    Google Scholar 

  • Kitunen, A., S. Rundle-Thiele, K. Kubacki, and T. Dietrich. 2018. Generating consumer insights into physical activity patterns for three different segments. Journal of Strategic Marketing 26 (2): 188–202.

    Google Scholar 

  • Lees, G., M. Maxwell Winchester, and S. De Silva. 2016. Demographic product segmentation in financial services products in Australia and New Zealand. Journal of Financial Services Marketing 21 (3): 240–250.

    Google Scholar 

  • Letheren, K. and C. Glavas. 2017. Embracing the bots: How direct to consumer advertising is about to change forever. https://theconversation.com/embracing-the-bots-how-direct-to-consumer-advertising-is-about-to-change-forever-70592. Accessed October 2018.

  • Liu, S. 2020. Chatbot market revenue worldwide from 2018 to 2027. https://www.statista.com/statistics/1007392/worldwide-chatbot-market-size/. Accessed 20 December 2020.

  • Lockwood, J. 2017. An analysis of web-chat in an outsourced customer service account in the Philippines. English for Specific Purposes 47 (July): 26–39.

    Google Scholar 

  • Lv, Z., Y. Jin, and J. Huang. 2018. How do sellers use live chat to influence consumer purchase decision in China? Research article Electronic Commerce Research and Applications 28 (March–April): 102–113.

    Google Scholar 

  • Mathwick, C. 2001. Understanding the online consumer: A typology of online relational norms and behavior. Journal of Interactive Marketing 16 (1): 40–55.

    Google Scholar 

  • McLean, G., and K. Osei-Frimpong. 2017. Examining satisfaction with the experience duringa livechat service encounter-implications for website providers. Computers in Human Behavior 76: 494–508.

    Google Scholar 

  • McLean, G., K. Osei-Frimpong, A. Wilson, and V. Pitardi. 2020. How live chat assistants drive travel consumers’ attitudes, trust and purchase intentions. International Journal of Contemporary Hospitality Management 32 (5): 1795–1812.

    Google Scholar 

  • McQuitty, S., A. Finn, and J.B. Wiley. 2000. Systematically varying consumer satisfaction and its implications for product choice. Academy of Marketing Science Review. http://www.amsreview.org/articles/mcquity10–2000.pdf. Accessed August 2018.

  • Mero, J. 2018. The effects of two-way communication and chat service usage on consumer attitudes in the e-commerce retailing sector. Electronic Markets 28 (2): 205–217.

    Google Scholar 

  • Ng, M., K.P. Coopamootoo, E. Toreini, M. Aitken, K. Elliot, and A. van Moorsel. 2020. Simulating the effects of social presence on trust, privacy concerns & usage intentions in automated bots for finance. In IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Genoa, Italy, 1, 190–199. https://doi.org/10.1109/EuroSPW51379.2020.00034.

  • Norusis, M.J. 2011. IBM SPSS 19.0 guide to data analysis. New Jersey: Prentice Hall.

    Google Scholar 

  • Ogonowski, A., A. Montandon, E. Botha, and M. Reyneke. 2014. Should new online stores invest in social presence elements? The effect of social presence on initial trust formation. Journal of Retailing and Consumer Services 21: 482–491.

    Google Scholar 

  • Oktay, B., and R. Yetki̇n Özbük. 2020. Segmentation of customers based on behavioral intention to use multi-channel banking and experience. Pazarlama İçgörüsü Üzerine Çalışmalar 4 (1): 13–26.

    Google Scholar 

  • Parida, V., R. Mostaghel, and P. Oghazi. 2016. Factors for elderly use of social media for health-related activities. Psychology & Marketing 33 (12): 1134–1141.

    Google Scholar 

  • Patterson, P.G. 2007. Demographic correlates of loyalty in a service context. Journal of Services Marketing 21 (2): 112–121.

    Google Scholar 

  • Peker, S., A. Kocyigit, and P.E. Eren. 2017. LRFMP model for customer segmentation in the grocery retail industry: A case study. Marketing Intelligence & Planning 35 (4): 544–559.

    Google Scholar 

  • Piercy, N., C. Campell, and D. Heinrich. 2011. Suboptimal segmentation: Assessing the use of demographics in financial services advertising. Journal of Financial Services Marketing 16 (3–4): 173–182.

    Google Scholar 

  • Pozza, I.D., A. Brochado, L. Texier, and D. Najar. 2018. Multichannel segmentation in the after-sales stage in the insurance industry. International Journal of Bank Marketing 36 (6): 1055–1072.

    Google Scholar 

  • Rabino, S., S.R. Onufrey, and H. Moskowitz. 2009. Examining the future of retail banking: Predicting the essentials of advocacy in customer experience. Journal of Direct, Data and Digital Marketing Practice 10 (4): 307–328.

    Google Scholar 

  • Radziwill, N. and M. Benton. 2017. Evaluating quality of chatbots and intelligent conversational agents. https://arxiv.org/pdf/1704.04579.pdf. Accessed August 2018.

  • Rajaobelina, L., I. Brun, and É. Toufaily. 2013. A relational classification of online banking customers. International Journal of Bank Marketing 31 (3): 187–205.

    Google Scholar 

  • Rajaobelina, L., I. Brun, and L. Ricard. 2019. Classification of live chat services users in the banking industry. International Journal of Bank Marketing 37 (3): 838–857.

    Google Scholar 

  • Richad, R., G. Vivensius, S. Sfenrianto, and E.R. Kaburuan. 2019. Analysis of factors influencing millennial’s technology acceptance of chatbot in banking industry in Indonesia. International Journal of Civil Engineering and Technology 10 (4): 1270–1281.

    Google Scholar 

  • Riquelme, H.E., and R.E. Rios. 2010. The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing 28 (5): 328–341.

    Google Scholar 

  • Ritchie, B.W., P.M. Chien, and M. Sharifpour. 2017. Segmentation by travel related risks: An integrated approach. Journal of Travel & Tourism Marketing 34 (2): 274–289.

    Google Scholar 

  • Robson, J. 2015. General insurance marketing: A review and future research agenda. Journal of Financial Services Marketing 20 (4): 282–291.

    Google Scholar 

  • Rogers, A. 2017. What your customers love and hate about live chat support. https://www.kayako.com/blog/live-chat-pros-and-cons. Accessed August 2018.

  • Rundle-Thiele, S., K. Kubacki, A. Tkaczynski, and J. Parkinson. 2015. Using two-step cluster analysis to identify homogeneous physical activity groups. Marketing Intelligence & Planning 33 (4): 522–537.

    Google Scholar 

  • Schuster, L., K. Kubacki, and S. Rundle-Thiele. 2015. A theoretical approach to segmenting children’s walking behaviour. Young Consumers 16 (2): 159–171.

    Google Scholar 

  • Singh, S., D.H. Rylander, and T.C. Mims. 2018. Understanding credit card payment behavior among college students. Journal of Financial Services Marketing 23 (1): 38–49.

    Google Scholar 

  • Sivaramakrishnan, S., F. Wan, and Z. Tang. 2007. Giving an “e-human touch” to e-tailing: The moderating roles of static information quantity and consumption motive in the effectiveness of an anthropomorphic information agent. Journal of Interactive Marketing 21 (1): 60–75.

    Google Scholar 

  • Syam, N., and A. Sharma. 2018. Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management 69 (2): 135–146.

    Google Scholar 

  • Tesfom, G., and N.J. Birch. 2011. Do switching barriers in the retail banking industry influence bank customers in different age groups differently? Journal of Service Marketing 25 (5): 371–380.

    Google Scholar 

  • Trivedi, J. 2019. Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce 18 (1): 91–111.

    Google Scholar 

  • Venkatesh, V., J.Y.L. Thong, and X. Xu. 2012. Consumer acceptance and use of Information Technology: Extending the unified theory of acceptance and use of technology. MIS Quaterly 36 (1): 157–178.

    Google Scholar 

  • Vincze, J. 2017. Virtual reference librarians (Chatbots). Library Hi Tech News 34 (4): 5–8.

    Google Scholar 

  • Wang, X., P. Zhao, G. Wang, and J. Liu. 2007. Market segmentation based on customer satisfaction-loyalty links. Frontiers of Business Research in China 1 (2): 211–221.

    Google Scholar 

  • Wieland, H., F. Polese, S.L. Vargo, and R.F. Lusch. 2012. Toward a service (eco) systems perspective on value creation. International Journal of Service Science, Management, Engineering, and Technology 3 (3): 12–25.

    Google Scholar 

  • Yang, K., and L.D. Jolly. 2008. Age cohort analysis in adoption of mobile data services: Gen Xers versus baby boomers. Journal of Consumer Marketing 25 (5): 272–280.

    Google Scholar 

  • Zhang, T., R. Ramakrishnon, and M. Livny. 1996. BIRCH: An efficient data clustering method for very large databases. In The ACM SIGMOD conference on management of data proceedings, Montreal, Canada, 103–114.

  • Zuccaro, C., and M. Savard. 2010. Hybrid segmentation of internet banking users. International Journal of Bank Marketing 28 (6): 448–464.

    Google Scholar 

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Correspondence to Lova Rajaobelina.

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The authors wish to thank the Social Sciences and Humanities Research Council of Canada (SSHRC) and Chair in Financial Services Management for their financial contributions to the project, as well as CEFRIO for data collection.

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Rajaobelina, L., Ricard, L. Classifying potential users of live chat services and chatbots. J Financ Serv Mark 26, 81–94 (2021). https://doi.org/10.1057/s41264-021-00086-0

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