Manchester Metropolitan University's Research Repository

    Clustering consumers' shopping journeys: eye tracking fashion m-retail

    Tupikovskaja-Omovie, Zofija and Tyler, David ORCID logoORCID: https://orcid.org/0000-0001-9736-4000 (2020) Clustering consumers' shopping journeys: eye tracking fashion m-retail. Journal of Fashion Marketing and Management: An International Journal, 24 (3). pp. 381-398. ISSN 1361-2026

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    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.

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