Can Companies Create a Machine That Feels and Cares? Using Generative AI to Provide Empathetic Customer Care

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 with customers. In a new Journal of Marketing article, we explore 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. This 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 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:

are designed for human interaction and communication,
can recognize and express empathetic understanding of user emotions by analyzing the user’s direct inputs,
can generate responses that demonstrate empathy, understanding, or support based on the context of the conversation,
and provide information, suggestions, or recommendations that may help address the user’s emotional challenges.

Bridging practice and the academic literatures in marketing and computer science, we develop an AI-enabled customer care journey that covers:

accurate emotion recognition,
empathetic response,
helpful emotion management, and finally,
establishment of an emotional connection.

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 customer utility, choice consideration, or consumption decisions.

We surveyed 305 U.S. chief marketing and customer officers from various industries and company sizes. In three open-ended questions, we 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, we offer 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, we find that 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.