Clustering consumers' shopping journeys: eye tracking fashion m-retail. Issue 3 (4th May 2020)

• Record Type: Journal Article
• Authors: Tupikovskaja-Omovie, Zofija; Tyler, David
• Is Part Of: Journal of fashion marketing and management. Volume 24:Issue 3(2020)
• Page Start: 381
• Page End: 398
• Publication Date: 2020-05-04
• Subjects: Consumer behaviour -- Mobile consumer -- Fashion consumer behaviour -- Consumer segmentation -- Eye tracking -- Shopping journey -- Fashion retailing -- Customer journey; Clothing trade -- Periodicals; Fashion merchandising -- Periodicals; Textile fabrics -- Periodicals; 687.0688
• DOI: 10.1108/JFMM-09-2019-0195
• Languages: English
• ISSNs: 1361-2026

Abstract

Abstract : Purpose: Despite the rapid adoption of smartphones among digital fashion consumers, their attitude to retailers' mobile apps and websites is one of increasing dissatisfaction. This suggests that understanding how mobile consumers use smartphones for fashion shopping is important in developing digital shopping platforms that fulfil consumer' expectations. Design/methodology/approach: For this research, mobile eye-tracking technology was employed in order to develop unique shopping journeys for 30 consumers, using fashion retailers' websites on smartphones, documenting their differences and similarities in browsing and purchasing behaviour. Findings: Based on scan path visualisations and observed shopping experiences, three prominent mobile shopping journeys and shopper types were identified: "directed by retailer's website", "efficient self-selected journey" and "challenging shopper". These prominent behaviour patterns were used to characterise mixed cluster behaviours; three distinct mixed clusters were identified, namely, "extended self-selected journey", "challenging shoppers directed by retailer's website" and "focused challenging shopper". Research limitations/implications: This research argues that mobile consumers can be segmented based on their activities and behaviours on the mobile website. Knowing the prominent shopping behaviour types any other complex behaviour patterns can be identified, analysed and described. Practical implications: The findings of this research can be used in developing personalised shopping experiences on smartphones by feeding these shopper types into retailers' digital marketing strategy and artificial intelligence (AI) systems. Originality/value: This paper contributes to consumer behaviour literature by proposing a novel mobile consumer segmentation approach based on detailed shopping journey analysis using mobile eye-tracking technology.