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The Role of AI in Enhancing Customer Experience: An Exploratory Analysis
Conference Paper · December 2023
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The Role of AI in Enhancing Customer Experience: An Exploratory Analysis
Nitin Seth, Eastern Institute of Technology - Te Pukenga
Surej John, Eastern Institute of Technology
Introduction and Research Aim
The evolving role of Artificial Intelligence (AI) in marketing has been a topic of interest among
researchers during the past few years. Most of these studies have focused on specific AI-based
applications such as chatbots (Pizzi, Vannucci, Mazzoli, & Donvito, 2023), voice assistance
technologies (Guerreiro & Loureiro, 2023; Wang et al., 2023), conversational agents (Diederich,
Brendel, Morana, & Kolbe, 2022) and other self-service technologies (Wirtz & Kowalkowski, 2023)
and their impacts on human-computer interactions in marketing and communication strategies.
Further, the existing literature has focused on any particular aspect of consumer behaviour such as
brand preferences (Ho & Chow, 2023), services evaluation (Shin, Bunosso, & Levine, 2023) and
online conversations (Li & Zhang, 2023). Despite the growing number of studies in this domain, the
overall understanding of the impacts of AI-enabled applications on enhancing consumers’ overall
shopping experience is still fragmented. To address this research gap, the current study examines the
impacts of diverse AI-enabled marketing applications on consumers’ shopping experiences during
their entire consumer decision-making process and purchase journey.
We address two research questions in line with this aim:
RQ1: How do we conceptualise the customer experience resulting from AI-Consumer
interactions?
RQ2: How do AI-enabled applications impact consumer experience and customer
journey?
Background and/or Conceptual Model
The conceptual model of the study is developed on the theoretical foundations of the Engel-Kollat-
Blackwell (EKB) model of consumer behaviour (Darley, Blankson, & Luethge, 2010). The study
examined the implications of AI-enabled applications on five stages of the consumer decision process
which are 1) problem recognition, 2) information search, 3) evaluation of alternatives, 4) purchase and
5) outcomes. Further, the study examined how four dimensions of customer experience: 1) sensory
experience, 2) cognitive experience, 3) affective experience and 4) conative experience are influenced
and impacted by the AI-human interactions in marketing, particularly in the context of retailing and
consumer services.
Methodology
The study involved two stages of qualitative data collection. In the first stage, a systematic literature
review of articles examining the role of AI in customer experience management is conducted. As a
methodology, systematic literature reviews help the researchers to identify, arrange, examine, and
synthesise the current literature in a specific subject area such as consumers’ shopping experience.
Further, this form of data analysis helps to conduct a comprehensive mapping and generate the most
up-to-date knowledge and summary on the subject topic (Paul et al., 2021). In the second stage, 25
semi-structured interviews with consumers who are experienced in various AI applications in retailing
are being conducted in the Auckland and Napier cities of New Zealand. However, the scope of this
paper is limited to discussing the methods and findings of the stage-1 data collection. The required
references for the research have been primarily obtained from Scopus, the world's largest academic
database. However, the articles were downloaded from various academic sources including
ScienceDirect, Wiley, ProQuest, and Sage. To identify the relevant articles, the study used keywords
such as “Artificial Intelligence” OR “AI” AND “customer experience”, OR “shopping experience”,
OR “customer journey”. These strategies helped us to identify 376 peer-reviewed articles at the initial
stage. However, to ensure the quality of our findings, we have excluded all non-ABDC-ranked
journals and non-English language articles in the next phase. This resulted in reducing the total
number of articles to 160. In the third stage of data filtering, we assessed the title, abstract and
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keywords of each of those 160 papers and further removed 19 further papers as they were found
irrelevant to our study resulting in 141 full papers for our structured review and analysis.
Results and/or Discussion and Contributions
Information technologies such as AI could play a critical role in redefining and shaping future
sustainable consumer behaviour. The study findings confirmed the role of artificial intelligence
applications in stimulating sensory, cognitive, affective, and conative customer experience during
various stages of the consumer journey. During the pre-purchase stage of the consumer journey,
customers mostly felt sensory and cognitive experiences from their AI interactions. During the
purchase and post-purchase stage, AI-based applications primarily provide an emotional and conative
shopping experience to consumers. The major implications of AI-enabled technologies in the
consumer decision process are presented in Table 1.
Consumer Relevant AI
Decision Impacts of AI and related technologies on Marketing tools or
Process applications
Emails, marketing
Personalised discounts and offers on customers' desired automation tools,
Problem items; Effective segmentation and target marketing; CRM software
recognition Communicating the new product arrivals to the relevant (e.g., Jasper AI,
consumers Marketing blocks
AI, Postaga)
Chatbots, voice
assistants,
conversational
Enhanced product /service recommendations; Better
Information agents, AR, VR
human-computer interactions; Enhanced (rich, detailed,
Search and MR tools,
and relevant) data availability, and accessibility;
SEO (e.g.,
Getgenie ai,
SurferSEO)
Chatbots, voice
assistants,
Generating favourable consumer attributes including conversational
Evaluation perceptions, trust, and motivation through human-AI agents, website
of interactions; Efficient comparison of alternatives through optimisation,
alternatives in-depth product information, price comparison and SEO, and search
personalisation marketing tools
(e.g., Cosmos AI,
copy.ai)
Enhanced in-store and online shopping experience (e.g.,
humour-oriented interactions through chatbots); enhanced
omni channel integration quality and management; Social media;
Increase the firm's ability to offer personalised products Location-based
Purchase via enhancing supply chain efficiency and production services, AR/ VT
flexibility; Increased value co-creation opportunities and MR tools,
through engagement and collaboration; Customisation and Robots
connectedness; Enhanced system quality (visual
appearance, time distortion, interaction speed)
Advanced self-expressive and emotional states (e.g., self- Emails,
esteem) for consumers; Increased affective, behavioural, marketing
and intellectual experiences with the retailer/ brand; automation tools,
Outcomes Increased post-purchase engagement; Profitable customer- CRM software
brand relationships; Increased brand trustworthiness, (e.g., Postaga, Ho
Enhanced customer decision-making attributes (skills, High Level,
challenge, abilities); Elastic email)
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Table 1: Role of AI in enhancing customer experience.
Implications for Theory and Practice
The findings of our study provided valuable suggestions for implementing sustainable marketing
strategies. Further, the study provides valuable contributions to the theory of consumer behaviour. The
paper discussed the implications of AI and related technologies in each stage of the consumer journey
and decision-making. These findings are expected to help marketers in adopting and using appropriate
AI applications that enable them to understand and meet changing customer needs and deploy
appropriate strategies for a sustainable brand-consumer relationship.
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References
Darley, W. K., Blankson, C., & Luethge, D. J. (2010). Toward an Integrated Framework for Online
Consumer Behavior and Decision Making Process: A Review. Psychology & Marketing, 27(2),
94–116.
Diederich, S., Brendel, A. B., Morana, S., & Kolbe, L. (2022). On the Design of and Interaction with
Conversational Agents: An Organizing and Assessing Review of Human-Computer Interaction
Research. Journal of the Association for Information Systems, 23(1), 96–138.
Guerreiro, J., & Loureiro, S. M. C. (2023). I am attracted to my Cool Smart Assistant! Analyzing
Attachment-Aversion in AI-Human Relationships. Journal of Business Research, 161(February).
Ho, S. P. S., & Chow, M. Y. C. (2023). The role of artificial intelligence in Consumers’ brand
preference for retail banks in Hong Kong. Journal of Financial Services Marketing,
(0123456789).
Li, C. Y., & Zhang, J. T. (2023). Chatbots or me? Consumers’ switching between human agents and
conversational agents. Journal of Retailing and Consumer Services, 72(August 2022), 103264.
Paul, J., & Benito, G. R. G. (2018). A review of research on outward foreign direct investment from
emerging countries, including China: what do we know, how do we know and where should we
be heading? Asia Pacific Business Review, 24(1), 90–115.
Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and
rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer
Studies.
Pizzi, G., Vannucci, V., Mazzoli, V., & Donvito, R. (2023). I, chatbot! the impact of
anthropomorphism and gaze direction on willingness to disclose personal information and
behavioural intentions. Psychology and Marketing, (March), 1372–1387.
Shin, H., Bunosso, I., & Levine, L. R. (2023). The influence of chatbot humour on consumer
evaluations of services. International Journal of Consumer Studies, 47(2), 545–562.
Wang, L., Huang, N., Hong, Y., Liu, L., Guo, X., & Chen, G. (2023). Voice-based AI in call centre
customer service: A natural field experiment. Production and Operations Management,
2030(February 2021), 1002–1018.
Wirtz, J., & Kowalkowski, C. (2023). Putting the “service” into B2B marketing: key developments in
service research and their relevance for B2B. Journal of Business and Industrial Marketing,
38(2), 272–289.
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