Abstract
Online retail has become a popular alternative to in-store shopping. However, unlike in traditional stores, users of online shops need to find the right product on their own without support from expert salespersons. Conversational search could provide a means to compensate for the shortcomings of traditional product search engines. To establish design guidelines for such virtual product search ...
Abstract
Online retail has become a popular alternative to in-store shopping. However, unlike in traditional stores, users of online shops need to find the right product on their own without support from expert salespersons. Conversational search could provide a means to compensate for the shortcomings of traditional product search engines. To establish design guidelines for such virtual product search assistants, we studied conversations in a user study (N = 24) where experts supported users in finding the right product for their needs. We annotated the conversations concerning their content and conversational structure and identified recurring conversational strategies. Our findings show that experts actively elicit the users’ information needs using funneling techniques. They also use dialogue-structuring elements and frequently confirm having understood what the client was saying by using discourse markers, e.g., “mhm”. With this work, we contribute insights and design implications for conversational product search assistants.
Altmetric