To overcome the challenge of seamlessly designing and managing compelling customer experiences, companies are starting to shift towards the application of artificial intelligence to provide customers with personalised and just-in-time product and service recommendations. In this context, this thesis investigates how artificially intelligent recommender systems can improve customer experience in the retail banking industry. This is achieved by reviewing literature in the fields of customer experience, artificial intelligence, and recommender systems as well as by performing a statistical analysis. As such, a deductive and quantitative methodological approach is chosen to conduct a questionnaire with 147 current retail banking clients. The findings of the thesis indicate that recommender systems in retail banking should be primarily implemented during the entire pre- and at the end of the post-purchase phases of the customer journey in order to maximise the positive influence on customer experience. Furthermore, the results imply that in terms of customer touchpoints, emphasis should be placed on mobile banking apps, online banking websites as well as personal bank advisors. Moreover, the findings suggest that recommender systems should – most importantly – manifest accuracy, novelty, transparency, and trustworthiness to enhance customer experience. Lastly, the study implies that customers perceive their banks to be more innovative and supportive when implementing recommender systems, which strengthens emotional bonds. However, the trustworthiness of banks could decrease leading to a potential misperception in brand promise. In sum, the thesis not only contributes to existing theory by combing emerging streams of research, but also provides retail banking executives with insights on what to consider when implementing recommender systems.
|Educations||MSc in Brand and Communications Management, (Graduate Programme) Final Thesis|
|Number of pages||120|