Researchers from National Taiwan University and the University of Maryland published a new Journal of Marketing article that examines how marketers can use GenAI to provide empathetic customer care.
The study, forthcoming in the Journal of Marketing, is titled “The Caring Machine: Feeling AI for Customer Care” and is authored by Ming-Hui Huang and Roland T. Rust.
Over the last decade or so, there has been a debate on whether artificial intelligence (AI) can handle customer emotions and replace humans when it comes to building long-term relationships. This new study explores how companies can use generative AI (GenAI) to provide empathetic customer care that can strengthen relationships and increase customer lifetime value.
There is increasing attention in marketing practice to customer care. Beyond helping customers make purchasing choices or solving product problems, customer care is about building and strengthening long-term relationships. Relationship building involves solidifying emotional connections with customers to give them a sense of belonging and being understood. As Rust explains, “customer care is not just an altruistic goal. If done well, it also increases firm profits because emotionally connected customers are loyal and bring steady profits.”
Advances in Deep Learning
GenAI refers to advanced deep-learning models—such as OpenAI’s GPT models, Microsoft’s Bing, Google’s Bard, and IBM’s Watsonx—that are designed to generate new content. These models utilize the vast data they have been trained on, combined with specific user inputs, to generate output. The pre-training learning from the huge amount of human-generated data makes GenAI able to generate humanlike responses, and the prompt response design enables the interactive and communicative capabilities of GenAI. Together, they make GenAI the new generation of “feeling AI” because they: (1) are designed for human interaction and communication, (2) can recognize and express empathetic understanding of user emotions by analyzing the user’s direct inputs, (3) can generate responses that demonstrate empathy, understanding, or support based on the context of the conversation, and (4) provide information, suggestions, or recommendations that may help address the user’s emotional challenges.
In this study, the researchers develop an AI-enabled customer care journey that covers:
- accurate emotion recognition,
- empathetic response,
- helpful emotion management, and finally,
- establishment of an emotional connection.
Huang says that “compared to the traditional customer journey, this sequence focuses on the feeling aspect, such as customer engagement, experience, and emotion, rather than the more typical thinking aspect, such as product characteristics or price.”
The researchers surveyed 305 U.S. chief marketing and customer officers from various industries and company sizes. In three open-ended questions, they asked them to list the major problems their company faces with customer care, the main pain points of using AI for customer care, and the main benefits of using AI for customer care.
Lessons for Chief Marketing Officers
Whether it is possible or desirable to fully automate the customer care journey is an ongoing debate. Bearing this in mind, the study offers points for marketers to consider along the customer care journey:
For emotion recognition, companies need to accurately identify customer problems and emotions to avoid miscommunications. Miscommunications escalate customer emotions; thus, recognizing customer emotion accurately is critical for deciding whether and how to care. GenAI can recognize expressed emotions accurately if given clear and honest customer input; however, accuracy may be compromised if the input is dishonest or imprecise and if GenAI lacks relevant knowledge for prediction. Thus, marketing practitioners need to cross-verify GenAI outputs.
For emotion understanding, companies need empathy: the ability to understand the customer’s emotions as if they were the customer and respond to the emotions appropriately. GenAI can take customers’ perspectives by learning from their direct inputs; however, the responses they generate may be less appropriate due to lack of commonsense knowledge. It is important for marketing practitioners to master prompting skills for probing customer thinking and deeper feeling.
For emotion management, companies need to provide helpful recommendations to assist customers in managing emotions. Generally, the recommendations should be specific to the customer’s situation and related to the service provided by the company. GenAI can provide generic recommendations, but the recommendations tend to be less personally helpful. Thus, marketing practitioners need to master response engineering skills to observe customer preferences in emotion management recommendations.
For emotional connection, companies need to develop a caring machine that has sufficient self-awareness (i.e., is aware of its own being and thus can have its own perspective) to distinguish itself from the customer and the firm. Thus, marketing practitioners need to align GenAI with the firm’s strategic goals and the customer’s intentions to make the caring machine strategic, and marketing researchers need to develop marketing strategies that can leverage GenAI strategically.
Full article and author contact information available at: https://doi.org/10.1177/00222429231224748
About the Journal of Marketing
The Journal of Marketing develops and disseminates knowledge about real-world marketing questions useful to scholars, educators, managers, policy makers, consumers, and other societal stakeholders around the world. Published by the American Marketing Association since its founding in 1936, JM has played a significant role in shaping the content and boundaries of the marketing discipline. Shrihari (Hari) Sridhar (Joe Foster ’56 Chair in Business Leadership, Professor of Marketing at Mays Business School, Texas A&M University) serves as the current Editor in Chief.
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Marilyn Stone is Director, Academic Communities and Journals, American Marketing Association.