쇼핑 챗봇의 검색 및 추천 서비스 개선 제안

Domestic shopping companies have introduced chatbots to increase the conversion rate of customers and reduce labor costs, but some companies are closing their services at low utilization rates. However this step is not to give up chatbot service but to re-operate it after development and improvement. Shopping chatbots use search and recommendation functions provided by existing shopping apps as their main functions, so comparative analysis with existing shopping apps is needed. In this study, we found problems in search and recommendation services of current domestic shopping chatbots through comparison between existing shopping apps and domestic shopping chatbots using in-depth interview methods and derived future improvements. Later, for verification of derived functions, we conducted a Kano model survey and analyzed it to propose the future direction of search and recommendation services of chatbots focusing on product recommendation, along with specific features: description for recommendation, category recommendation, and personalized recommendation functions. This study is significant in that it derived problems and improvement directions for current chatbot search and recommendation services through in-depth interviews, verified them through surveys, and presented future chatbot service improvement directions and specific functions together.