In an increasingly digitalised world, dynamic customer expectations are challenging companies to seamlessly and effectively provide customers with just-in-time, targeted product and service information (van Bommel et al., 2014). Putting this into the context of retail banking, customers want their bank to not only understand their needs, but also to take their wants and preferences with regard to financial products and services into consideration. Consequently, retail banks are increasingly challenged to design compelling customer experiences across both online and offline touchpoints (Maechler et al., 2018; Edelman, 2010).
Emerging technologies like AI enable companies to use a vast amount of gathered customer data to offer the right products and services to the right customers at the right time (Martinez-Lopez & Casillas, 2013; Jiang et al., 2009). In particular, both traditional and non-traditional retail banks have recently started to look into statistical machine learning algorithms to enable them to provide customers with personalised and relevant recommendations of financial products and services (Gigli et al., 2017). Recommender systems have the potential to substantially improve the decision-making process of customers and to enhance the effectiveness of bank personnel (Zibriczky, 2016). Nevertheless, as stated by Chirkina and Rankov (2018), due to the complexity and high-involving nature of financial products and services, implementing a recommender system in a retail banking setting is particularly challenging.
Connecting this challenge with the increasing importance in customer experience management and the emerging technology of recommender systems, this thesis aimed at uncovering the necessary factors for implementing a recommender system in retail banking to improve customer experience. These necessary factors consist of the appropriate customer journey phases, touchpoints, recommendation characteristics, and brand elements for a recommender system implementation that positively influences customer experience in retail banking.
Thereby, the thesis contributes to existing literature by connecting underlying theories and constructs of customer experience management with the emerging technology of AI-driven recommender systems.
Conclusion
The findings of the thesis indicate that in terms of timing, recommender systems should be implemented during the entire pre-purchase phase and at the end of the post-purchase phase of the customer decision journey. This substantially supports customers to navigate through the overwhelming amount of information and complexity with respect to financial products and services to improve decision-making processes, which ultimately leads to an enhanced customer experience. With respect to the appropriate touchpoints for recommender system implementation, banks should focus on mobile banking apps, online banking websites as well as personal bank advisors. These touchpoints are in line with the increasing emphasis for digital and mobile-centric customer experiences in today’s retail banking landscape. Nevertheless, personal contacts should not be underestimated due to the substantial financial impact of banking products and services. In order to have a positive effect on customer experience, recommender systems should focus on meeting customer preferences and matching them with suitable items by deploying a hybrid system. Furthermore, customers value recommendations that are novel, transparent, trustworthy, and make them feel confident in making a purchase decision. Overall, the findings show that the implementation of a recommender system makes customers feel a stronger emotional bond to their banks by feeling more supported and perceiving them as more innovative. As the brand elements ‘trust’ and ‘promises’ are negatively affected by the technology’s implementation, banks should consider to not explicitly point out the usage of a recommender system.
As a final note, in order to account for recommender system success, banks should focus on removing organisational silos and start emphasising interdisciplinary teams to enable comprehensive data analyses and a constant tracking of recommender system’s performance metrics. To sum up, it is of utmost importance for retail banks to find the right balance in timing recommendations, personalising touchpoints, and understanding customer needs and preferences to design compelling customer experiences with the aim to achieve long-term, sustainable brand resonance.
List of References
List of References
Accenture. (2018). Space Odyssey - Fjord Trends 2019. Retrieved from https://trends.fjordnet.com/trends/space-odyssey
Accenture. (2019). Creating the Best Customer Experience. Retrieved from https://www.accenture.com/us-en/interactive-index
Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2011). Recommender Systems, Consumer Preferences, and Anchoring Effects. RecSys 2011 Workshop on Human Decision Making in Recommender Systems, 35-42.
Baesens, B. (2014). Analytics in a Big Data World - The Essential Guide to Data Science and Its Applications. Wiley & Sas Business.
Banafa, A. (2016, June 6). What is Affective Computing?. Retrieved from https://www.bbvaopenmind.com/en/what-is-affective-computing/
Beerli, A., Martín, J. D., & Quintana, A. (2004). A Model of Customer Loyalty in the Retail Banking Market. European Journal of Marketing, 38(1/2), 253-275.
Brakus, J. J., Schmitt, B. H., & Zarantonello, L. (2009). Brand Experience: What is It? How is it Measured? Does it Affect Loyalty? Journal of Marketing, 73(3), 52-68.
Bruner, G., Hensel, P., & James, K. (2005). Marketing Scales Handbook, Volume IV: A Compilation of Multi-Item Measures for Consumer Behavior & Advertising. CA:
Thomson Higher Education.
Buchanan, B. G. (2006). A (Very) Brief History of Artificial Intelligence. AI Magazine, 26(4), 53-60.
Burns, E. (2018, October). Current State of AI is Poorly Understood by the Public. Retrieved from https://searchenterpriseai.techtarget.com/opinion/Current-state-of-AI-is-poorly-understood-by-the-public
Buttle, F. (1996). SERVQUAL: Review, Critique, Research Agenda. European Journal of Marketing, 30(1), 8-32.
Chen, L., De Gemmis, M., Felfernig, A., Lops, P., Ricci, F., & Semeraro, G. (2013). Human Decision Making and Recommender Systems. ACM Transactions on Interactive Intelligent Systems, 3(3), 17-24.
List of References
Chirkina, A., & Rankov, B. (2018, August 23). A Recommender System for Private Banking [Blog Post]. Retrieved from https://www.incubegroup.com/blog/recommender-system-for-private-banking/
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
Corea, F. (2018, August 22). AI Knowledge Map: How to Classify AI Technologies. Forbes.
Retrieved from https://www.forbes.com/sites/cognitiveworld/2018/08/22/ai-knowledge-map-how-to-classify-ai-technologies/#6606c9367773
Cunningham, L. F., Gerlach, J., & Harper, M. D. (2005). Perceived Risk and E-Banking Services: An Analysis from the Perspective of the Consumer. Journal of Financial Services Marketing, 10(2), 165-178.
Da Silveira, C., Lages, C., & Simões, C. (2013). Reconceptualizing Brand Identity in a Dynamic Environment. Journal of Business Research, 66(1), 28-36.
Davenport, T. H., & Patil, D. J. (2012). Data Scientist: The Sexiest Job of the 21st Century.
Harvard Business Review, 90(10), 70-76.
Descartes, R. (1637). Part 5. In Discourse on the Method.
Desmangles, L., Sachse, S., Vasy, B., Dupas, M., De T'Serclaes, J. W., & Walsh, I. (2018, May 29). Global Retail Banking 2018: The Power of Personalization. Retrieved from
https://www.bcg.com/publications/2018/global-retail-banking-2018-power-personalization.aspx
Dhebar, A. (2013). Toward a Compelling Customer Touchpoint Architecture. Business Horizons, 56(2), 199-205.
Edelman, D. C. (2010). Branding in the Digital Age - You're Spending Your Money in All the Wrong Places. Harvard Business Review Spotlight on Social Media and the New Rules of Branding.
Flasiński, M. (2016). Chapter 1 - History of Artificial Intelligence. In Introduction to Artificial Intelligence. Basingstoke, England: Springer.
Flyverbom, M., & Madsen, A. K. (2015). Sorting Data Out – Unpacking Big Data Value Chains and Algorithmic Knowledge Production. ResearchGate.
List of References
Frow, P., & Payne, A. (2007). Towards the ‘Perfect’ Customer Experience. Journal of Brand Management, 15(2), 89-101.
Gentile, C., Spiller, N., & Noci, G. (2007). How to Sustain theCustomer Experience: An Overview of Experience Components that Co-create Value With the Customer.
European Management Journal, 25(5), 395-410.
Gigli, A., Lillo, F., & Regoli, D. (2017, August). Recommender Systems for Banking and Financial Services. Poster session presented at 11th ACM Conference on
Recommender Systems - ResSys 2017, Como, Italy.
Gorgoglione, M., & Panniello, U. (2011). Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a Retail Bank by a Recommender System Approach.
Journal of Intelligent Learning Systems and Applications, 03(02), 90-102.
Gour, R. (2018, November 15). What is Expert System in Artificial Intelligence? How it Solves Problems. Retrieved from https://medium.com/@rinu.gour123/what-is-expert-system-in-artificial-intelligence-how-it-solve-problems
Gyrd-Jones, R. I., Helm, C., & Munk, J. (2013). Exploring the Impact of Silos in Achieving Brand Orientation. Journal of Marketing Management, 29(9-10), 1056-1078.
Halevy, A., Norvig, P., & Pereira, F. (2009). The Unreasonable Effectiveness of Data. IEEE Intelligent Systems, 24(2), 8-12.
Hallowell, R. (1996). The Relationships of Customer Satisfaction, Customer Loyalty, and Profitability: An Empirical Study. International Journal of Service Industry Management, 7(4), 27-42.
Hardt, D. (2018, October 26). Artificial Intelligence in the Marketplace - Lecture 8: Neural Networks and Deep Learning [PDF]. Retrieved from
https://learn.cbs.dk/pluginfile.php/953823/mod_resource/content/1/session8.pdf Holbrook, M. B., & Hirschman, E. C. (1982). The Experiential Aspects of Consumption:
Consumer Fantasies, Feelings, and Fun. Journal of Consumer Research, 9(2), 132-140.
Howard, J. A., & Sheth, J. N. (1969). The theory of buyer behavior. Hoboken, NJ: John Wiley
& Sons.
List of References
Ieva, M., & Ziliani, C. (2017). Customer Experience in Retail Banking: What Touchpoints Matter for Customer Loyalty? In European Association for Education and Research in Commercial Distribution.
Ind, N., Iglesias, O., & Markovic, S. (2017). The Co-Creation Continuum: From Tactical Market Research Tool to Strategic Collaborative Innovation Method. Journal of Brand Management, 24(4), 310-321.
Jiang, Y., Shang, J., & Liu, Y. (2009). Maximizing Customer Satisfaction Through an Online Recommendation System: A Novel Associative Classification Model. Decision Support Systems, 48(3), 470-479.
Keller, K. (2001). Building Customer-Based Brand Equity: A Blueprint for Creating Strong Brands. Marketing Science Institute, 01(107), 7-25.
Knapp, S. (2006, July 24). Artificial Intelligence: Past, Present, and Future. VOX of Dartmouth, 25(2). Retrieved from
http://www.dartmouth.edu/~vox/0607/0724/ai50.html
Konstan, J. A., & Riedl, J. (2012). Recommender Systems: From Algorithms to User Experience. User Modeling and User-Adapted Interaction, 22(1-2), 101-123.
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised Machine Learning: A Review of Classification Techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160, 3-24.
Kumar, C. (2018, August 31). Artificial Intelligence: Definition, Types, Examples, Technologies. Medium. Retrieved from
https://medium.com/@chethankumargn/artificial-intelligence-definition-types-examples-technologies-962ea75c7b9b
Kumar, V., Petersen, J., & Leone, R. (2007). How Valuable is Word of Mouth? Harvard Business Review, 85(10), 139-144.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96.
Levitt, T. (1960). Marketing Myopia. Harvard Business Review, 45-56.
List of References
Lewis, B. R., & Soureli, M. (2006). The Antecedents of Consumer Loyalty in Retail Banking.
Journal of Consumer Behaviour, 5(1), 15-31.
Maechler, N., Michael, J., Schiff, R., & Smith, T. (2018, October). Managing a Customer-Experience Transformation in Banking. Retrieved from
https://www.mckinsey.com/industries/financial-services/our-insights/managing-a-customer-experience-transformation-in-banking
Marr, B. (2018, February 14). The Key Definitions if Artificial Intelligence (AI) that Explain its Importance. Forbes. Retrieved from
https://www.forbes.com/sites/bernardmarr/2018/02/14/the-key-definitions-of-artificial-intelligence-ai-that-explain-its-importance/#46bd031c4f5d
Martinez-Lopez, F. J., & Casillas, J. (2013). Artificial Intelligence-Based Systems Applied in Industrial Marketing: An Historical Overview, Current and Future Insights. Industrial Marketing Management, 42(4), 489-495.
Massachusetts Institute of Technology. (2018). MIT Media Lab: Affective Computing Group.
Retrieved from https://affect.media.mit.edu/
Mayer-Schönberger, V., & Cukier, K. (2013). Correlation. In Big Data - A Revolution That Will Transform How We Live, Work and Think. Boston, MA: Houghton Mifflin Harcourt.
McCarthy, J. (1998). What is Artificial Intelligence? Stanford Education.
Mendes da Silva, S. (2019, February 7). AI: Terminology, History and How to Get Started...
[PDF]. Retrieved from
https://drive.google.com/drive/u/0/folders/1kiK3ZIG2pLcAwx789I_zJcUWeWbwdt5f Meyer, C., & Schwager, A. (2007). Understanding Customer Experience. Harvard Business
Review, 1-11.
Norris, E. (2018, July 12). Retail Banking vs. Corporate Banking. Retrieved from
https://www.investopedia.com/articles/general/071213/retail-banking-vs-commercial-banking.asp
Pallant, J. (2013). A Step by Step Guide to Data Analysis Using IBM SPSS. Australian and New Zealand Journal of Public Health, 37(6), 597-598.
Paraschakis, D. (2017). Towards an Ethical Recommendation Framework. ResearchGate.
List of References
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing, 49(4), 41-50.
Park, S. C. (2017). The Fourth Industrial Revolution and Implications for Innovative Cluster Policies. AI & SOCIETY, 33, 433-445.
Portugal, I., Alencar, P., & Cowan, D. (2017). The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review. Expert Systems with Applications, 97, 205-227.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media, Inc.
Pu, P., Chen, L., & Hu, R. (2011, October). A User-Centric Evaluation Framework for Recommender Systems. Paper presented at Fifth ACM Conference on Recommender Systems - RecSys 2011, Chicago, Illinois, USA.
Qualtrics. (2018, January 1). Sample Size Calculator. Retrieved March 16, 2019, from https://www.qualtrics.com/blog/calculating-sample-size/
Reichheld, F. (2003). The One Number You Need to Grow. Harvard business review, 81(12), 46-54.
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender Systems: Introduction and
Challenges. In Recommender Systems Handbook (2nd ed., pp. 2-32). New York City, NY: Springer Science & Business Media.
Rust, R. T., & Chung, T. S. (2006). Marketing Models of Service and Relationships.
Marketing Science, 25(6), 560-580.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Understanding Research Philosophies and Approaches. In Research Methods for Business Students (5th ed., pp. 106-135).
London, England: Pearson Education.
Schmitt, B., & Zarantonello, L. (2013). Consumer Experience and Experiential Marketing: A Critical Review. Review of Marketing Research, 10, 25-61.
Sekaran, U., & Bougie, R. (2010). Research Methods for Business: A Skill Building Approach (5th ed.). Chichester, United Kingdom: John Wiley & Sons.
Shah, D., Rust, R. T., Parasuraman, A., Staelin, R., & Day, G. S. (2006). The Path to Customer Centricity. Journal of Service Research, 9(2), 113-124.
List of References
Sheth, J. N., Sisodia, R. S., & Sharma, A. (2000). The Antecedents and Consequences of Customer-Centric Marketing. Journal of the Academy of Marketing Science, 28(1), 55-66.
Smith, B., & Linden, G. (2017). Two Decades of Recommender Systems at Amazon.com.
IEEE Computer Society, 17, 12-18.
Soni, D. (2018, March 22). Supervised vs. Unsupervised Learning - Understanding the Differences Between the Two Main Types of Machine Learning Methods. Retrieved from
https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d
Srinivas, V., Wadhwani, R., Ramsay, T., Jain, A., & Singh, Y. (2018). 2019 Banking and Capital Markets Outlook - Reimagining Transformation. Retrieved from
https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Financial-Services/gx-fsi-dcfs-2019-banking-cap-markets-outlook.pdf
Stefanou, C. J., Sarmaniotis, C., & Stafyla, A. (2003). CRM and Customer‐Centric Knowledge Management: An Empirical Research. Business Process Management Journal, 9(5), 617-634.
Sterne, J. (2017). Artificial Intelligence for Marketing - Practical Applications. Hoboken, NJ:
John Wiley & Sons, Inc.
Turing. (1950). Computing Machinery and Intelligence. Mind, New Series, 59(236), 433-460.
Vallaster, C., & Von Wallpach, S. (2013). An Online Discursive Inquiry into the Social Dynamics of Multi-Stakeholder Brand Meaning Co-Creation. Journal of Business Research, 66(9), 1505-1515.
Van Bommel, E., Edelman, D., & Ungerman, K. (2014, June). Digitizing the Consumer Decision Journey. Retrieved from
https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/digitizing-the-consumer-decision-journey Vandermerwe, S. (1993). Jumping into the Customer's Activity Cycle. The Columbia Journal
of World Business, 28(2), 46-65.
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer Engagement: Exploring Customer Relationships Beyond Purchase. Journal of Marketing Theory and Practice, 20(2), 127-145.
List of References
Voorhees, C. M., Fombelle, P. W., Gregoire, Y., Bone, S., Gustafsson, A., Sousa, R., &
Walkomwiak, T. (2017). Service Encounters, Experiences and the Customer Journey:
Defining the Field and a Call to Expand our Lens. Journal of Business Reseach, 79, 269-280.
Walker, B. K. (2011). Welcome to the Era of Agile Commerce - eBusiness Execs Must Evolve Channel Strategies to Embrace Market Changes. Forrester Research.
Zaki, M., Kandeil, D., Neely, A., & McColl-Kennedy, J. R. (2016). The Fallacy of the Net Promoter Score: Customer Loyalty Predictive Model. Cambridge Service Alliance, 1-25.
Zibriczky, D. (2016). Recommender Systems Meet Finance: A Literature Review. In 2nd International Workshop on Personalization and Recommender Systems in Financial Services.
Østergaard, P., & Jantzen, C. (2000). Shifting Perspectives in Consumer Research: From Buyer Behaviour to Consumption Studies. In Interpretive Consumer Research.
Paradigms, Methodologies, and Applications (pp. 9-24). Copenhagen, Denmark:
Copenhagen Business School Press.
Appendices
List of Appendices
Appendix 1: Customer Activity Cycle based on Vandermerwe (1993 & 2000) ... 122
Appendix 2: Customer Touchpoint Blueprint by Dhebar (2013) ... 122
Appendix 3: Research Onion based on Saunders et al. (2009) ... 122
Appendix 4: Questionnaire ... 123
Appendix 5: Codebook for data analysis ... 128
Appendices