Engaging and retaining customers with AI and employee service

Artificial intelligence (AI) permeates in service organisations as a tool to enhance operational efficiency and improve customer experience. Reports show that most consumers prefer human interactions with service employees. Drawing on this observation, the current study examines how customers' service experiences with employees and AI influence customer engagement and loyalty. Customers’ emotional intelligence is proposed as a moderator between service experience and customer engagement. The study was conducted with hotel customers in Australia. The results show that whilst both service experience with employees and AI are significantly related to customer engagement and loyalty, only certain dimensions make significant unique variances in the outcome variables. The findings indicate that customers prefer employee service. These service experiences also have significant partial mediation effects on customer loyalty. Emotional intelligence has a significant moderation effect on customer engagement. Discussion of these findings and implications derived from this study concludes this paper.     

From AI to digital transformation: The AI readiness framework

Strategies and means for selecting and implementing digital technologies that realize firms’ goals in digital transformation have been extensively investigated. The recent surge in artificial intelligence (AI) technologies has amplified the need for such investigation, as they are being increasingly used in diverse organizational practices, creating not only new opportunities for digital transformation but also new challenges for managers of digital transformation processes. In this article, I present a framework intended to assist efforts to address one of the first of these challenges: assessment of organizational AI readiness—that is, an organization’s ability to deploy AI technologies to enable digital transformation, in four key dimensions: technologies, activities, boundaries, and goals. I show that this framework can facilitate analysis both of an organization’s current sociotechnical AI status and of the prospects for the technology’s fuller value-adding, sociotechnical deployment. The AI readiness framework invites fuller theorizing of the roles that AI can—and will—play in digital transformation.     

Consumer response to economic crisis and lessons for marketers: The Turkish experience

Private consumption is the largest component of gross domestic product (GDP). It has a substantial impact on the speed of recovery from an economic crisis. This paper aims to examine the behavior of consumers, firms, and government in Turkey in response to the recent global economic crisis. Turkey was one of the few countries that emerged from the economic downturn relatively quickly. The demographics of consumers, the solidity of financial sector, and the government policies led to a speedy recovery from the crisis through an increase in consumption expenditures. During the initial shock, consumers switched to cheaper goods and decreased consumption expenditures in total. The government emphasized that the impact of crisis would be limited. The opening of credit lines, the temporary reduction in value-added tax and special consumption tax on certain commodities, aggressive marketing campaigns, and a rosy future drawn by chambers of commerce and NGOs in specific promotional activities were influential in increasing consumption. This paper discusses the consumer response to and the marketing lessons derived from this experience.     

NFT marketing: How marketers can use nonfungible tokens in their campaigns

Nonfungible tokens (NFTs) are a record of ownership of primarily digital media, where the NFT is stored on a blockchain. According to the 2021 Gartner Hype Cycle for Key Technologies, NFTs may significantly transform marketing functions. Marketing managers wishing to adopt NFTs therefore need to know something about the marketing implications. This article explains NFTs in broad terms and discusses the marketing implications using a modified AIDA hierarchy. These implications can give marketing managers and executives guidelines on how to persuade consumers to purchase NFTs owing to their unique characteristics, such as scarcity, nonfungibility, proven authenticity, proof of ownership, royalties, and direct distribution infrastructure.     

A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations

Recommender systems are used in e-Commerce websites to make product recommendations or deliver personalized content to users. We constructed a beer recommendation program using review data from existing online community to test the hypotheses. This research aims to bridge the gap between marketing and computer science by investigating the moderating effects of consumer knowledge (expertise) on the performance and evaluation of two widely-used recommendation systems – user-based collaborative filtering and content-based. The results show that expert consumers prefer user-based collaborative filtering systems, whereas there is no difference between the two systems among novice consumers. Theoretical and managerial implications are discussed.     

From customer experience to human experience: Uses of systematized and non-systematized knowledge

The conceptual framework proposed herein reveals how firms might establish a human experience focus, using both systematized and non-systematized knowledge to identify points of pain and gain. Such efforts align with the critical need for firms to develop their approaches to the customer experience, by moving beyond addressing how customers respond to their offerings and toward thinking about the human experience of how firms respond to customers’ ambitions, beliefs, values, and feelings to interact in the manner customers prefer. To create a human experience, firms must manage it in relation to touchpoints, personalization, operations, and company culture, using systematized knowledge to monitor the experience, identify problems, and make improvements, together with non-systematized knowledge to innovate in relation to the experience.     

The convenience of shopping via voice AI: Introducing AIDM

The purpose of this paper is to propose an updated view of consumer choice based on AI and inherent convenience addiction to smart speakers. Following the MacInnis framework for developing conceptual contributions of summarization, integration, and delineation, we review the current consumer decision-making literature and theory to demonstrate consumers' increasing tendency to outsource decisions to AI. Today's customers value convenience: the less time and effort they spend on a purchase, the better they perceive the transaction. AI is taking convenience to higher levels for consumers as they outsource their decisions to bots and inherent algorithms. This is particularly accurate for low-involvement everyday purchases. Our study's contribution is fourfold. First, we introduce a new model of AI-influenced decision-making (AIDM) processes. Second, our conceptual model suggests that managers need to change their interpretation of their customers' decision-making-processes in the new, AI-influenced marketplace. The shift in consumers' behavior toward reliance on home voice bots for purchase has significant implications for the retail sector. Third, our model differentiates between high and low involvement AI-influenced decision-making processes. Fourth, our study highlights how branding as we know it is challenged in an AI-dominated environment.     

Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel

The B2B sales process is undergoing substantial transformations fueled by advances in information and communications technology, specifically in artificial intelligence (AI). The premise of AI is to turn vast amounts of data into information for superior knowledge creation and knowledge management in B2B sales. In doing so, AI can significantly alter the traditional human-centric sales process. In this article, we describe how AI affects the B2B sales funnel. For each stage of the funnel, we describe key sales tasks, explain the specific contributions AI can bring, and clarify the role humans play. We also outline managerial considerations to maximize the contributions from AI and people in the context of B2B sales.     

In bot we trust: A new methodology of chatbot performance measures

Chatbots are used frequently in business to facilitate various processes, particularly those related to customer service and personalization. In this article, we propose novel methods of tracking human-chatbot interactions and measuring chatbot performance that take into consideration ethical concerns, particularly trust. Our proposed methodology links neuroscientific methods, text mining, and machine learning. We argue that trust is the focal point of successful human-chatbot interaction and assess how trust as a relevant category is being redefined with the advent of deep learning supported chatbots. We propose a novel method of analyzing the content of messages produced in human-chatbot interactions, using the Condor Tribefinder system we developed for text mining that is based on a machine learning classification engine. Our results will help build better social bots for interaction in business or commercial environments.     

Artificial intelligence and knowledge management: A partnership between human and AI

Emerging artificial intelligence (AI) capabilities will likely pervade nearly all organizational contours and activities, including knowledge management (KM). This article aims to uncover opportunities associated with the implementation of emerging systems empowered by AI for KM. In doing so, we explicate the potential role of AI in supporting fundamental dimensions of KM: creation, storage and retrieval, sharing, and application of knowledge. We then propose practical ways to build the partnership between humans and AI in supporting organizational KM activities and provide several implications for the development and management of AI systems based on the components of people, infrastructures, and processes.     

Inside your social media ring: How to optimize online corporate reputation

Although it is well accepted that corporate communication has a direct impact on corporate reputation, little is known about the link between firms’ social media communication strategies and the formation of firms’ reputations in an online environment. This article contributes to this body of knowledge by studying the impact of social media communication strategies on firms’ reputations. The setting for our study is the hotel industry. The results offer insights regarding the challenges of developing online communication strategies that affect corporate reputation.     

Innovation analytics: Leveraging artificial intelligence in the innovation process

Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature—coupled with our experiences as practitioners—suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.     

Memorable customer experiences and autobiographical memories: From service experience to word of mouth

The aim of this paper is to study the relationship between customer experience and word of mouth (WOM) through two mediating variables: customer satisfaction and memorable characteristics of service experience (MCSE). Memory theory is adopted as a theoretical background, with emphasis on the key role of episodic or autobiographical memory. Memorable customer experience (MCE) is defined from this perspective, thereby enabling the connection between human memory theory and service-dominant (S-D) logic. As for the impact of the internet on WOM, two types of WOM behaviours are examined: positive WOM (pWOM) and eWOM. A methodology was designed from a survey that used a questionnaire based on measurement scales validated by the literature. The sample is of 1476 tourists who visited seven Spanish tourism destinations. The results show service experience precedes customer satisfaction and MCSE. Both pWOM and eWOM are significantly influenced by customer satisfaction and MCSE. Various scenarios are put forward in which several combinations of customer satisfaction and MCSE give rise to different intensities and valence of pWOM and eWOM.     

How to date your clients in the 21st century: Challenges in managing customer relationships in today's world

Since its first entry into the literature discussion in the 1980s, customer relationship management (CRM) has found its way into nearly every company. Concepts like personalization, loyalty programs, and customer valuation are used regularly to interact with and prioritize customers. Unsurprisingly, this more widespread use has changed our understanding of CRM substantially and as a consequence, the field has seen a remarkable transformation in the past 3 decades. Yet, the CRM strategies implemented by many firms today are frequently still fundamentally based on an understanding of CRM from the early days. The purpose of this article is to outline the origins of CRM and to present the main wisdoms that firms believed to be true about customers 3 decades ago. I then discuss the key insights that academics and managers have obtained in recent years that increasingly challenge those wisdoms. The article ends with an outlook of CRM in years to come and presents some of the major challenges practitioners and researchers will have to deal with in the near future.     

How to retain customers in omnichannel retailing: Considering the roles of brand experience and purchase behavior

The opportunities and challenges of omnichannel in retail industry have been widely discussed, yet despite these benefits, the key elements that constitute an effective omnichannel and how customers respond to omnichannel retailing strategies remain unclear. This research conducted online surveys to test the effects of omnichannel elements on various brand experiences and customer retention, considering the moderating role of purchase behavior. The results indicate that omnichannel elements (integration, individualization, and interaction) are generally helpful in retaining customers, through omnichannel elements influence brand experiences differently. In addition, these omnichannel elements have different influences on customer retention due to different purchase behaviors. The findings suggest that retailers can use different omnichannel strategies to attract customers’ purchases and provide insights for practitioners who want to use omnichannel strategies to deliver superior experiences for customers.     

Economic corollaries of personalized recommendations

The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales.     

The future of service: The power of emotion in human-robot interaction

Astoundingly, recent technological advancements have enabled robots to display emotions. Yet, while emotional expression is valued in the field of service, understanding emotions in human-robot interaction remains underexplored. Since emotions are contagious/transmittable, this study utilised Instagram data to uncover how emotional robots influence potential consumers’ affective feelings. By employing machine learning algorithms and sentiment analysis, the findings suggest that the expressions of surprise and happiness are key to creating positive impacts on potential consumers. The cross-disciplinary nature of this study lays the groundwork for next-level social, design, and creative experiences in artificial intelligence research regarding consumer service and experience contexts.     

Using AI Techniques in the Grocery Industry: Identifying the Customers Most Likely to Defect

The food retailing market has reached a mature stage where companies need to be competitive if they are to survive. Customers are ever more demanding and retailers need to design and introduce new ways of learning about their customers if they are to retain them (Leeflang & Van Raaij ). This article examines the efficiency of the LAMDA classifier (Learning Algorithm Machine for Data Analysis) (Aguado ; Aguado et al. ) in identifying customer's behaviour; specifically examining which customers are most likely to defect when a new retailer appears on the scene. The study carried out in this project is based on data gathered from a Spanish grocery chain: Supermercats Pujol, SA – ‘Plus Fresc’, winner of the 1998 Global Electronic Marketing Award, www.plusfresc.es