Between Artificial Intelligence and Customer Experience: A Literature Review on the intersection

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Between Artificial Intelligence and Customer Experience: A Literature Review on the intersection

https://doi.org/10.21203/rs.3.rs-3612887/v1

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published 09 Jan, 2024

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This paper is a literature review of the intersection field between Artificial Intelligence (AI) and Customer Experience (CX). We analyzed and synthesized the most recent and prominent literature on the subject, providing an overview of the state of the art, through articles found in the Scopus database. Among the main findings, it is noteworthy that this intersection appears as an interdisciplinary topic of interest in the fields of Computer Science, Business and Management, and Engineering. Additionally, studies often examine conversational agents such as chatbots and voicebots, as well as machine learning prediction models and recommendation systems as a way to improve the Customer Experience. The most common sectors in the review are tourism, banking and e-commerce. Other segments and technologies appear less and may be underrepresented, thus a scope for future research agenda. Despite the existing literature, it is observed that there is still ample room for expansion and exploration, especially considering the emergence of new generative Artificial Intelligence models.

Artificial Intelligence

Customer Experience

User Experience

Literature Review

Marketing Strategies

Customer Experience (CX) turns out as a promising element of analysis when it comes to technology-mediated consumption, especially in light of the emergence of digital products. It presents itself as a factor that can be crucial for organizations. Conversely, in the field of Artificial Intelligence (AI), there is a substantial number of studies exploring User Experience (UX), a term that may be more common but inherently distinct. However, the quantity of research on Customer Experience and Artificial Intelligence is notably less than the amount that addresses Artificial Intelligence and User Experience. Therefore, it is important to explore the subject through a review of recent literature.

In this sense, this article aims to explore the existing literature on the relationship between Artificial Intelligence (AI) and Customer Experience (CX), as well as identify emerging theoretical points of discussion, providing an overview of the current state of the intersection between these concepts. By focusing on understanding the intersection between AI and CX, we emphasize the field of Customer Experience, rather than User Experience, recognizing that it has been less explored in the literature.

Due to the specificity of the topic, we chose to use a broader search string to ensure a significant sample of research for analysis and, in a second step, refine the search based on specific inclusion and exclusion criteria, as highlighted in the following sections. This approach allowed us to delve into the specific aspects of the Customer Experience domain influenced by Artificial Intelligence technologies. A bibliometric analysis of the articles found was conducted, and, subsequently, an in-depth reading of the most cited articles between 2019 and 2023, i.e., in the last 5 years. This analysis provides insights into the advancements and research directions in the AI + CX domain, representing valuable findings from this study.

In this regard, this article does not delve into the theoretical aspects of AI and/or CX but highlights the main fields of study, with a focus on providing an overview of the most recent and relevant research on the subject.

According to Veiga & Monteiro (2023), Customer Experience (CX) is distinct from User Experience (UX), because experience transcends usage functions, in this way the UX is encompassed by the CX concept. Therefore, they are not the same, nor disconnected areas but User Experience belongs to the set called Customer Experience (Veiga & Monteiro, 2023). Customer Experience (CX), at its core, involves the analysis of customer journeys, encompassing all elements of a company's offering in which each department strives to provide a positive experience – ranging from customer service and advertising to packaging, product reliability, and more (Meyer & Schwager, 2007). It is the response that customers have to any interaction with a company during the process of purchase, usage, and service (Meyer & Schwager, 2007).

As consumers increasingly seek experiences that cater to the specifics of their identities as consumers (Oyserman, 2009), the need for marketing professionals to consider people's online experiences leading to purchasing behaviors also grows (Martin, Mortimer, & Andrews, 2015). Hence, personalization becomes a crucial factor in the discussion of AI and CX. Technologies like augmented reality or virtual reality have gained traction in user experiments (Rese et al., 2017), while the implementation of chatbots, for example, enables process automation and quick response times, also capturing adoption rates of services or product recommendations (Martin, Mortimer, & Andrews, 2015).

It is important to highlight that, Artificial Intelligence (AI) represents a broad term encompassing a variety of techniques and methodologies, with some terminological issues, where other terms are frequently used as the same, such as machine learning, algorithms, deep learning, robots, and so on Dwivedi et al. (2021). Due to this diversity, efforts were made to identify the specific techniques, methodologies, or "Artificial Intelligences" that the most prominent research addresses in this literature review. Therefore, it is of great significance that this literature review also points to the type of AI being analyzed by the scientific community. Thus, we highlight the most common AI technologies that emerge from the literature on AI + CX.

The bibliometric search was conducted using the string ("Artificial Intelligence" AND "Customer Experience") in the Scopus database. Although the string is quite broad, it was chosen to be kept in this way, and filters were applied later to refine the results, considering that "Customer Experience" is already a specific term. The search resulted in 374 articles, to which the following selection criteria were applied:

Open access and peer-reviewed: Conference papers, book chapters, and similar works were excluded, limiting the research to open-access articles in the English language.

Recency: Artificial Intelligence has undergone significant transformations in recent years, especially considering the post-pandemic scenario and, more importantly, the emergence of large language models (LLMs) or generative AI. Prioritizing studies from the last five years (2019–2023) allows for coverage of the impacts of these new technological advancements.

Relevance to the topic: From reading the abstracts, articles that only briefly mention Artificial Intelligence or use the term as a keyword but do not delve into the subject in their study were excluded.

With 52 articles remaining, a bibliometric analysis was conducted to identify general topics related to the subject. The final sample selection for this article comprises two sets, articles with a citation count above 10.0/year in the searched databases, considered the most prominent (9 papers) and articles from the current year (15 papers) as they may have reduced citations due to their recency. The process, string, filters and exclusion criteria is shown in Fig. 1.

These studies are synthesized in the results section.

This discussion begins with a comparative analysis of the quantity of publications on the topics of AI + CX and AI + UX. From Figure 2, it is evident that the intersection between Artificial Intelligence and Customer Experience is still substantially less researched than the intersection between Artificial Intelligence and user experience. Both topics show growing interest; however, AI + CX exhibits modest growth in publications compared to AI + UX since 2010.

Looking at the timeline presented in Figure 1 for the last five years, there is an increase in publications on the topic from 2018 to 2021, followed by a slight decrease in 2022. However, up to the current period, which encompasses the first half of 2023, the number of publications has already exceeded that of the previous year, reaching the same level as in 2021. Thus, there may be a trend of increasing publications on the topic.

There appears to be a balance in publications across three areas that encompass the majority of studies on Artificial Intelligence and Customer Experience: Computer Science, Business and Management, and Engineering. For comparison, a search that replaces "Customer Experience" with "user experience" shows a considerable difference between these areas, with a predominance of Computer Science in the AI + UX intersection, as can be observed in Figure 3. The field of Business and Management is more prevalent in research on Artificial Intelligence than in AI + UX, despite its dominance in studies that specifically focus on consumer experience, which is expected. In any case, the AI + CX intersection seems to exhibit an interdisciplinary balance among these areas, whereas AI + UX seems to be a topic of interest in the Computer Science and Engineering areas.

This review involves in-depth analysis and synthesis of the most relevant articles on the subject, along with the most recent. Therefore, we have selected the most cited articles between 2019 and 2023 for the integrative review. It is important to note that the analysis considered the average per year, rather than the total number of citations, as more recent articles tend to have fewer citations. The primary criterion comprises an average above 10.0 citations per year.

In Table 1, we summarize the types of Artificial Intelligence and/or technology analyzed in relation to both the most cited articles and the most recent ones published in 2023. Some authors address various types of AI systems or a combination of applications involving machine learning and conversation agents (chatbots/voicebots), hence they are listed on more than one line in Table 1.

Table 1 - Most recent and most cited research on AI + CX

Type of AI systems	Top cited references	Most recent references
Recommendation systems and other machine learning applications	Hamilton et. al (2021); Ameen et. al (2021); Samala et. al (2022);	Silva et al. (2023); Ho & Chow (2023); Yaiprasert &, Hidayanto (2023); Wulff & Finnestrand (2023); Jadhav et al. (2023); Ceccacci et al. (2023);
Voicebots and chatbots	Hamilton et. al (2021); McLean, & Osei-Frimpong (2019); Bawack, Wamba & Carillo (2021); Puntoni et. al (2021); Samala et. al (2022); Robinson et. al (2020); Nguyen, Chiu & Le (2021); Neuhofer, Magnus & Celuch (2021);	Hoang et al. (2023); Abdo & Yusof (2023); Abdelkader (2023); Sari & Adinda (2023); Shin et al. (2023); Ho & Chow (2023); Jan et al. (2023); Hasan, et al. (2023);
Other AI and technologies or AI systems in general	Samala et. al (2022); Neuhofer, Magnus & Celuch (2021); Puntoni et. al (2021);	None

Observing Table 2, it's evident that chatbots and voicebots represent the majority of AI technologies analyzed in recent research, especially between the top cited ones. Machine learning applications also appear significantly in 2023 publications. However, it is not possible to infer a growth in research on this topic, as sparsely cited articles from 2019 to 2022 were excluded from the sample analyzed in this study. Nevertheless, it can be stated that conversational agents and machine learning are the primary AI technologies researched in the intersection between AI and CX.

The motivation behind the use of voicebots in online shopping is studied by McLean & Osei-Frimpong (2019), discovering that consumers of Amazon voice assistant, Alexa, are driven by utilitarian, symbolic, and social benefits. Personalization is also crucial for voice assistant adoption (Bawack, Wamba & Carillo, 2021). A virtual assistant integrated with VoIP technology can assist businesses in handling thousands of calls per day with optimal performance, thus creating a customer service campaign that quickly reaches many users and helping companies reduce operator costs through automated calls (Hoang et al, 2023). Also, incorporating AI chatbots with voice input and sentiment analysis improve Customer Experiences by increasing efficiency and convenience, while also can save cost and time for businesses organizations (Abdo & Yusof, 2023). However, privacy risk appears as a meaningful negative aspect according to customers (McLean & Osei-Frimpong, 2019; Hasan, et al., 2023).

Chatbots are often used to interact with customers as a means to enhance the experience. In this matter, it is possible to highlight that ease of use, satisfaction, and trust significantly impact customers' intention to use chatbots, while utility does not have as much of an effect (Hasan, et al., 2023). Other usage factors include convenience, ubiquity, and interactivity, as well as technology readiness (Jan et al., 2023). Information, accessibility, and customization are relevant for AI marketing, while interaction does not significantly affect the brand experience (Ho & Chow, 2023). Technology readiness is also a critical factor influencing chatbot usage, and marketing communication should minimize obstacles such as discomfort and insecurity (Jan et al., 2023). Regarding the interaction, the use of humor in chatbots has a positive impact on the experience, but the same is not observed when used by human agents (Shin et al., 2023). Furthermore, interactions between companies’ employees and customers (human or machines) can have negative effects when one is unsure if the other is a machine or a human (Robinson et al., 2020).

Customer needs can be identified through interactions with chatbots: Sari & Adinda (2023) use personas to identify them and find out that information, transactions, security, entertainment, and addressing complaints are the most common. Therefore, chatbots help maintain customer interest in using services (Nguyen, Chiu & Le, 2021), significantly reduce costs (Hoang et al., 2023), and play a role in the decision-making process as human companions (Hamilton et al., 2021). Abdelkader (2023) identified that familiarity and comfort with technology play a significant role in moderating Customer Experience (CX) and overall satisfaction in digital marketing, in a study involving ChatGPT. Research in this regard is expected to grow with the widespread adoption of LLMs around the globe. However, it is evident that there are still many subjects to be researched, considering the recent introduction of these models.

As mentioned before, machine learning applications like recommendation systems and prediction techniques also play a significant role in recent research on AI + CX intersection. Researches apply the technique to create more robust models for predicting efforts (Jadhav et al., 2023), to understand customer preferences and buying habits through segmentation for personalization (Yaiprasert & Hidayanto, 2023), since personalization improves service quality (Samala et al., 2022; Ameen et al., 2021). Ho & Chow (2023) examine the impact of recommendation systems on brand experience, brand preference, and repurchase intention, suggesting that information, accessibility, and customization are influential aspects of AI marketing, while interaction doesn't significantly affect brand experience (Ho & Chow, 2023). Recommendation systems are analyzed in Silva et al (2023), where the authors highlight opportunities for improvement in the high-price segment of the footwear industry, where Customer Experiences are less satisfactory compared to the low-price segment.

Customer purchase history can be a very important source for training datasets in machine learning applications, providing insights for segmentation, customer preferences and buying habits (Yaiprasert & Hidayanto, 2023). A method proposed by Yaiprasert & Hidayanto (2023) can increase the algorithm's accuracy, reducing the probability of wrong decisions, allowing companies to better understand customers’ needs and invest in marketing efforts that fit their expectations. Additionally, data from emotion recognition systems and facial expression analysis can assist artistic directors in estimating audience satisfaction levels and predicting overall customer satisfaction (Ceccacci et al., 2023).

In a broader sense, some researchers do not necessarily focus on one AI system to conduct the study, which can be useful to managers and marketing staff to understand the big picture in organizational AI efforts. Puntoni et. al (2021) presents a framework that conceptualizes AI as an ecosystem with four capabilities: data capture, classification, delegation, and social. For each of them, the authors suggest emerging research questions, aiming to assist managers in understanding how consumers may not perceive value in organizations' investments in AI. Neuhofer, Magnus & Celuch (2021) employ scenario techniques to simulate future situations, envisioning Artificial Intelligence agents surpassing existing technological capabilities to become an autonomous agent in co-creating experiences and value (Neuhofer, Magnus & Celuch, 2021). Furthermore, there is only one research in the analyzed sample that explores Explainable AI (XAI) and how it affects Customer Experience. Wulff & Finnestrand (2023) provides a theoretical contribution to the field of AI and organizational design by highlighting the importance of minimizing the need for explainable AI, and a practical contribution to design processes by splitting explainability goals into two groups (pattern and experience goals).

The literature review of the analyzed research in this study provides insights into how science and academic research have been addressing the subject in recent years, with a notable emphasis on chatbots (often referred to as conversational agents) and machine learning, which encompasses various functions, including personalization, facial recognition, clustering, and more. The banking sector, as mentioned, is significantly represented in research on chatbots and Customer Experience, along with the tourism sector. E-commerce in the footwear industry and beauty brands also appear, while other segments appear to be less representative in the AI + CX research. Various models and frameworks were proposed as outcomes of the analyzed studies, showing some diversity in the models and theories used to conceptualize the topic.

The launch of new generative Artificial Intelligence technologies and large language models, such as ChatGPT in November 2022, may have impacted research interest in this domain, but we can only speculate that, therefore, an aspect that can be explored in future studies. In this proposal, only one study was found that sought to relate Customer Experience to generative Artificial Intelligences, indicating a field that may be explored in the future. Since these generative AI are relatively recent, there is still a lot to be explored about how they relate to Customer Experience.

Lastly, it is important to note that the analysis of this study was limited to selected articles within the defined scope, using the search string "('Artificial Intelligence' AND 'Customer Experience')." Therefore, there may be highly relevant research, especially regarding User Experience, that was not included in our sample. Due to the ever-evolving nature of AI, ongoing research and updates are imperative, and can be conducted through different databases, periods of time, or other exclusion criteria.

This article aimed to conduct a literature review covering studies at the intersection of Artificial Intelligence (AI) and Customer Experience (CX). The reviewed studies encompass various AI applications, from chatbots and virtual assistants to personalized recommendations. Through this review, the goal was not only to highlight the most relevant works and provide an overview of the current landscape but also to identify recent interests in the field, given the rapid evolution of technological advancements in AI. The analysis and synthesis of these works shed light on the role of Artificial Intelligence in Customer Experience across various industries and its impacts on different areas of study. We expect that this review can outline a future research agenda to address emerging challenges, and serves as a resource for future researchers in identifying opportunities to advance knowledge and practice regarding the impacts of Artificial Intelligence on Customer Experience.

Author Contribution

M.P. and G.M.S. conducted the data analysis and the review. J.M.T acted as a guiding professor in structuring the paper and designed the methodology figure.M.P. wrote the main manuscript text and prepared figures.J.M.T. and G.M.S. made contributions to the text. All authors reviewed the manuscript.

DATA AVAILABILITY STATEMENT

No datasets were generated or analyzed during the current study except for the papers in the references section. Figures 2 and 3 were elaborated by the authors using the result of the string in Scopus database.

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No competing interests reported.

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Between Artificial Intelligence and Customer Experience: A Literature Review on the intersection

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