From scrolling to selling
Structuring 63% of fashion trend seekers into scalable brand insights
1Closet is an early-stage fashion-tech platform designed to structure style discovery at scale.
I created a lightweight Instagram-based quiz that collects the user's pain points and style preferences, while silently building structured profiles for brands.
My focus: connecting individual style intent with smarter product targeting, affiliate optimisation, and scalable B2B data models.
Timeline
Jun 2024 - Present
Role
UX & Product Designer
Team
1x Product Designer
1x Product Manager
1x Back End Developer
1x Fashion Stylist
Impact
- 67% quiz completion on first prototype, validating engagement for style segmentation
- Tagging model mapped to affiliate-friendly clusters, enabling future targeted recommendations
- Built foundations for a B2B partner dashboard to drive style insights, lower returns, and optimise shopping experience
Turning style discovery into structured market data
In Brazil, over 480,000 shoppers browse fashion digitally — and 63% discover trends via social media. Yet brands miss the chance to capture real-time style intent, relying instead on demographics and historic purchases.
I saw the opportunity to structure discovery at the moment it happens — before taste signals are lost.
The personalisation gap in fashion retail
Without real-time style profiles, brands launch campaigns on guesswork:
- Discovery happens socially, but sales campaigns are generic
- Retargeting lacks aesthetic segmentation
- Loyalty is low, and return rates are high
Brands need better ways to listen, organise, and respond to style intent — not just click behaviour.
Poor data on individual style intent
Without user-specific insights, brands rely on demographics or past purchases, missing the real-time intent that drives fashion choices.
Generic shopping journeys
Users are shown broad, irrelevant recommendations — leaving them frustrated and reducing their likelihood to engage or purchase.
Missed opportunities for B2B insights
Brands struggle to convert user interactions into scalable insights, limiting their ability to improve targeting, reduce returns, and boost loyalty.
A system that personalises style and feeds brand insights
1Closet is intentionally lean — designed to deliver value quickly, with minimal effort. From day one, I focused on structuring user input into taggable, brand-usable data. That meant designing for both personalisation and segmentation at once.
Behind the screens, the system works quietly:
- Inputs like moodboard taps, colour picks and product clicks are invisibly tagged
- Style clusters (e.g. Minimal, Romantic, Street) form automatically based on behaviour
- Product suggestions build over time — informed by previous purchases.
This allows 1Closet to act as a "living" style engine: building trust with users, while helping brands target with precision.
Entry point flow sketch (Instagram DM button / Landing CTA)
01. Instant entry from Instagram or web CTA
Users arrived from different entry points, either Instagram followers and/or website visitors, but all had the same need: start styling immediately.
The flow starts when a user taps the "Message" button on our profile. A quick-reply button instantly launches the style quiz — no form fields, no loading screen, just instant interaction.
Strategic impact:
- Captures interest before drop-off
- Adapts natively to Instagram and web behaviour
- Establishes a seamless user expectation from first tap
02. Adaptive quiz builds style profile invisibly
The chat flow combined open questions, multiple choice replies and visual carousels (e.g. style moodboards, fabric preferences). Every choice was mapped to a tag — building a structured profile behind the scenes, without overwhelming users.
Design rationale
- Flexible enough to adapt to different behaviours
- No signup or onboarding friction
- Tied engagement directly to valuable style data
Quiz interaction sketch
Style profile result screen (highlight key style cluster traits)
03. Personalised style results before product push
Before suggesting any items, users received a shareable style guide based on their cluster. This built trust, giving them a sense of clarity before seeing product links.
Strategic impact:
- Delivered immediate value (not just a shopping link)
- Strengthened user confidence
- Built emotional buy-in through validation
04. Smart shopping suggestions via affiliate links
Product suggestions only appear after the user receives their style profile. Each recommendation is matched to their cluster and powered by affiliate links from major brands.
As users click, save or buy, the system learns what works. This closes the feedback loop, letting 1Closet improve future suggestions and segment users based on real product behaviour.
Strategic impact:
- Aligns product discovery with personal style
- Increases click and conversion likelihood
- Feeds purchase data back into the style engine
Shopping feed mockup (affiliated products tied to style profile)
Turning early users into valuable data profiles
A system designed not just to engage users, but to capture structured style profiles, enable affiliate partnerships, and support future product integrations. This work also established early design patterns to ensure consistency and efficiency as the product evolves.
1Closet validated a lightweight, scalable approach to style profiling, achieving strong early engagement and laying the foundation for B2B growth.
The sketches above sketches illustrate both entry points — via Instagram DM or the landing page CTA — while the included video focuses on the landing page flow to show how users are guided into the conversation.
By working closely with developers and stylists, I helped translate business goals into a user journey that balances immediate value with long-term data scalability.
Product impact
Foundation for long-term revenue loops
Process impact
Style-matching data model defined
People impact
Knowledge-first engagement strategy validated
Designing with intent, scaling with simplicity
This project reinforced the value of early research, simplicity in flow design, and close collaboration with cross-functional partners, building an intuitive user experience while laying the groundwork for long-term business impact.
Balancing both taught me to think beyond screens and focus on long-term impact — how each interaction feeds into smarter data, better targeting and a stronger affiliate loop. It also reinforced the value of early research and working closely with developers to prototype within constraints.
