Ah, Alright, Okay! Communicating Understanding in Conversational Product Search

Abstract

When talking about products, people often express their needs in vague terms with vocabulary that does not necessarily overlap with product descriptions written by retailers. This poses a problem for chatbots in online shops, as the vagueness and vocabulary mismatch can lead to misunderstandings. In human-human communication, people intuitively build a common understanding throughout a conversation, e.g., via feedback loops. To inform the design of conversational product search systems, we investigated the effect of different feedback behaviors on users’ perception of a chatbot’s competence and conversational engagement. Our results show that rephrasing the user’s input to express what was understood increases conversational engagement and gives the impression of a competent chatbot. Using a generic feedback acknowledgment (e.g., “right” or “okay”), however, does not increase engagement or perceived competence. Auto-feedback for conversational product search systems therefore needs to be designed with care.