This research explores the underutilisation of clothing in the UK, focusing on consumer perceptions and barriers to resale for first time buyers.
Resale is growing quickly, but there are still barriers that prevent some citizens from taking part. Interest is rising both online and in-store, with 54% of UK citizens stating they are happy to purchase preloved clothing. However, recognition of these routes is still low.
WRAP’s insights tell us that 45% of UK citizens purchase clothing at least once a month, with almost a quarter (23%) regularly buying clothes with the intention of wearing them for a short amount of time.
Using innovative methods like eye-tracking along with in-depth interviews, this research sought to identify how people interact with online resale platforms. Understanding these behaviours is key to cutting the amount of clothing sent to landfill and incineration, keeping more textiles in use for longer, and achieving the ambitions of the UK Textiles Pact.
The complexity for first-time buyers
The project highlights a complex picture facing first-time preloved consumers. Building on WRAP’s existing work, this qualitative research explores the market from the perspective of consumers who do not currently buy preloved items through resale platforms or apps. It provides deeper insight into the barriers, drivers, perceptions, and motivations shaping engagement with preloved clothing.
The aim was to generate evidence to inform the design of solutions that increase participation in circular business models and help cultivate a culture of reuse, focussing on unworn and undervalued clothing. Specifically, the research aimed to understand:
How non-users engage with online resale platforms when browsing for preloved clothing for the first time
How the convenience and appeal of purchasing preloved clothing through online resale platforms could be improved
Whether any specific clothing categories could act as gateways to behavioural change and consideration of buying preloved clothing
This work was carried out with the support of the Back to Baselines in Circular Fashion & Textiles sub-network, which forms part of the Circular Fashion and Textile Network Plus, a collaborative research initiative funded by the Natural Environment Research Council (NERC) under grant no. NE/Y004043/1, on behalf of the UK Research and Innovation Circular Fashion and Textiles trilateral partnership (NERC, Arts and Humanities Research Council (AHRC) and Innovate UK (IUK)).
Key findings
The research uncovered insights into shopping behaviours, attitudes, motivations and barriers to shopping preloved.
- Priorities: consumers tend to prioritise factors such as style, fit, quality, brand, and value for money
- Purchase triggers: include a special occasion, sales/promotions or are emotionally led (personal reward or stress-relief).
- Perceived benefits: include better value for money, access to unique finds and sustainability as a ‘feel good’ factor.
- Perceived barriers: include a general lack of awareness of preloved websites, cultural perceptions/ stigmas.
- Trust was a significant barrier: including concerns over misleading descriptions, hidden flaws, scams, quality and authenticity.
These research insights feed into WRAP’s wider textile initiatives, specifically the UK Textiles Pact Circular Business Models workstream. The findings reveal clear opportunities for the industry to encourage people choosing preloved for the first time.
What industry can do
To encourage first-time buyers of preloved, platforms need to address barriers to purchase whilst reinforcing motivations, ensuring consumers feel confident in value for money, product quality and authenticity.
Consider product categories that are more likely to be considered by new users: occasion wear, branded outerwear, and children’s clothing.
Ensure return and refund information is upfront and low risk to the buyer (e.g. free returns, no quibble guarantee)
Implement or showcase seller integrity, product quality and hygiene measures
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Citizen insights for online resale - Preloved experience.pdf
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