Design Guide for Search Filters on Online Grocery Shopping Platforms

Abstract

Background This paper aims to improve the user experience of search filters when browsing products on online grocery shopping platforms to propose guiding principles for optimizing search filter designs based on food culture trends, product variations, and user preferences. Methods This study was conducted through a literature review, case studies, and quantitative/qualitative user surveys. Literature review was carried out to establish the direction of a filter design guide. Through the analysis of search filter cases in domestic and international food shopping malls, essential filters and specialized filters based on products and user types were identified. Based on them, a survey was conducted to extract key filters for each product. Contextual interviews were then conducted to derive insights and user types. Combining these findings, a food filter design guide and ideas were proposed. Results The guide for key filters by food category in online food shopping malls is as follows. Firstly, for processed foods, prioritize providing a [Brand] filter. Secondly, for refrigerated/frozen foods, prioritize offering a [Taste/Type] filter. Lastly, for fresh foods, prioritize providing an [Origin (Production Place)] filter, while agricultural and livestock products can share filters. Additionally, users’ behavior types in utilizing food product search tools are categorized into three: ‘Shopping Enthusiast’ prioritizes price and delivery date, ‘Stability Seeker’ considers quantity/price and expiration date, and ‘Homemaker’ focuses on taste preferences and reviews. Conclusions In this study, we identified the priorities of major food category search filters in online food shopping malls and proposed a food product search filter design guide that reflects user search behaviors. The research findings are expected to serve as crucial reference material for future design of food search AI algorithms.