Clustering consumers' shopping journeys: eye tracking fashion m-retail
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 32 - Art and Design: History, Practice and Theory
- Output identifier
- 258037
- Type
- D - Journal article
- DOI
-
10.1108/jfmm-09-2019-0195
- Title of journal
- Journal of Fashion Marketing and Management: An International Journal
- Article number
- -
- First page
- 381
- Volume
- 24
- Issue
- 3
- ISSN
- 1361-2026
- Open access status
- Exception within 3 months of publication
- Month of publication
- May
- Year of publication
- 2020
- URL
-
https://e-space.mmu.ac.uk/625760/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
D - Fashion
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The rapid expansion of digital platforms for retailing fashion products has focused on websites designed for laptops and tablets, despite the growing awareness that digital consumers spend more time using smartphones. It is recognised that significant dissatisfaction with retail mobile apps is problematic, and there is recognition that most m-consumers would welcome improvements. This paper analyses the shopping journeys of mobile consumers as they search, choose and purchase fashion products. There have been a few studies involving simulated experiences, but this is the first to document actual m-customer experiences on real fashion retailers’ apps. This research finds that m-consumers can be segmented based on their activities and behaviours. Gender differences were not observed during the course of this research. Once the behaviour type is known, it becomes possible to develop personalised shopping experiences affecting a retailer’s digital marketing strategy, and indeed as it develops its omnichannel marketing approach. There are applications here for artificial intelligence systems. The outcome can be to provide m-consumers with an interface tuned to match their behaviour and thereby to increase satisfaction levels when using an app. Quantitative data was gathered using eye-tracking technology and qualitative data emerged from post-purchase reflection by the participants. The methodologies were developed during a PhD research programme and refined during a Post-Doc Fellowship. The methods used have extended the literature base from simulations to real purchases; from choosing products to actually purchasing them. Unique shopping journeys for 30 consumers were obtained using two fashion retailers’ apps. One of the retailers collaborated in identifying customers willing to participate and subsequently providing a promotional voucher code as an incentive. These studies have documented differences and similarities in browsing and purchasing actions, with three broad types of behaviour. They set a new standard for research in fashion m-consumer behaviour.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -