Chatbots are increasingly replacing human customer-service agents on companies’ websites, social media pages, and messaging services. Designed to mimic humans, these bots often have human names (e.g., Amazon’s Alexa), humanlike appearances (e.g., avatars), and the capability to converse like humans. The assumption is that having humanlike qualities makes chatbots more effective in customer service roles. However, a new Journal of Marketing study suggests that this is not always the case.
Our research team finds that when customers are angry, deploying humanlike chatbots can negatively impact customer satisfaction, overall firm evaluation, and subsequent purchase intentions. Why? Because humanlike chatbots raise unrealistic expectations of how helpful they will be.
To better understand how humanlike chatbots impact customer service, we conducted five studies.
In Study 1, we analyzed nearly 35,000 chat sessions between an international mobile telecommunications company’s chatbot and its customers. We found that when a customer was angry, the humanlike appearance of the chatbot had a negative effect on the customer’s satisfaction.
In Study 2, we created a series of mock customer-service scenarios and chats where 201 participants were either neutral or angry and the chatbot was either humanlike or non-humanlike. Again, we saw that angry customers displayed lower overall satisfaction when the chatbot was humanlike than when it was not.
In Study 3, we demonstrated that the negative effect extends to overall company evaluations, but not when the chatbot effectively resolves the problem (i.e., meets expectations). We had 419 angry participants engage in a simulated chat with a humanlike or non-humanlike chatbot, but their problems were either effectively resolved or not during the interaction. As expected, when their problems were not effectively resolved, participants reported lower evaluations of the company when they interacted with a humanlike chatbot compared to a non-humanlike one. Yet, when their problems were effectively resolved, the company evaluations were higher, with no difference based on the type of chatbot.
In Study 4, we conducted an experiment with 192 participants that provided evidence that this negative effect is driven by the increased expectations of the humanlike chatbot. People expect humanlike chatbots to be able to perform better than non-humanlike ones; those expectations are not met, leading to reduced purchase intentions.
In Study 5, we showed that explicitly lowering customer’s expectations of the humanlike chatbot prior to the chat reduced the negative response of angry customers to humanlike chatbots. When people no longer had unrealistic expectations of how helpful the humanlike chatbot would be, angry customers no longer penalized them with negative ratings.
Our findings provide a clear roadmap for how best to deploy chatbots when dealing with hostile, angry or complaining customers. It is important for marketers to carefully design chatbots and consider the context in which they are used, particularly when it comes to handling customer complaints or resolving problems. Firms should attempt to gauge whether a customer is angry before they enter the chat (e.g., via natural language processing), and then deploy the most effective, either humanlike or not humanlike, chatbot. If the customer is not angry, assign a humanlike chatbot; but if the customer is angry, assign a non-humanlike chatbot. If this sophisticated strategy is not technically feasible, companies could assign non-humanlike chatbots in customer service situations where customers tend to angry, such as complaint centers. Or companies could downplay the capabilities of humanlike chatbots (e.g., Slack’s chatbot introduces itself by saying “I try to be helpful (But I’m still just a bot. Sorry!” or “I am not a human. Just a bot, a simple bot, with only a few tricks up my metaphorical sleeve!”). These strategies should help avoid or mitigate the lower customer satisfaction, overall firm evaluation, and subsequent purchase intentions reported by angry customers towards humanlike chatbots.
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From: Cammy Crolic, Felipe Thomaz, Rhonda Hadi, and Andrew Stephen, “Blame the Bot: Anthropomorphism and Anger in Customer-Chatbot Interactions,” Journal of Marketing.
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Cammy Crolic is Associate Professor of Marketing, University of Oxford, UK.
Felipe Thomaz is Associate Professor of Marketing, University of Oxford, UK.
Rhonda Hadi is Associate Professor of Marketing, University of Oxford, UK.
Andrew T. Stephen is Associate Dean of Research and L’Oreal Professor of Marketing, University of Oxford, UK.