Fit Finder: Redesigning trust, not just size
I led the product design evolution of Fit Finder across two companies. First at Fit Analytics, then after Snap's acquisition in 2021. The redesign shipped in 2023 to clients including Zara, Hugo Boss, and ASOS, serving millions of shoppers monthly.
Conversion rate uplift via A/B test
Return rate reduction across retail clients
User satisfaction score post-launch
My role
Lead Product Designer
Timeline
5 months (2022–2023)
Team
PM, User Researcher, Data Scientists, Engineers
Platform
Web & Mobile (white-label SDK)
Context
A product I knew from the inside
Fit Finder is a white-label size recommendation tool embedded directly into retailer product pages. When a shopper clicks "Find my size," Fit Finder walks them through a short questionnaire about their body measurements and fit preferences, then surfaces a personalised size recommendation. The goal: reduce the uncertainty that drives returns, and give shoppers the confidence to buy.
I joined Fit Analytics in 2019 as a senior designer, working on the product long before Snap acquired the company in 2021. After the acquisition, Fit Finder became part of Snap's ARES Shopping Suite, and I continued leading design as we planned a major version update. That continuity mattered. I wasn't parachuted in to redesign something I'd never used. I knew what the data said, where users dropped off, and which parts of the product engineering had quietly flagged as technical debt.
Fit Finder (V4.2), Upper body / Woman — October 2022
Problem
What was actually breaking
V4.2 had a measurable drop-off problem. Analytics showed significant abandonment at the brand-selection screen; a step that asked users to select their preferred brand after entering body measurements. The intent was to calibrate recommendations to brand-specific sizing, but the data science team had been questioning its value for months.
Beyond that, usability testing pointed to a broader trust gap. Users were completing the flow but not acting on the recommendation. They'd see their size and then still check the size chart manually, or abandon the purchase. The interface felt bureaucratic; too many screens and inconsistent visual language across clients.
The core problem statement
How do we make users trust Fit Finder's recommendation enough to act on it, and can we get there with fewer steps?
I know I'm usually a medium, but this thing told me large. I didn't really trust it, so I just guessed anyway.
— Usability test participant, 2022
Research & Analysis
What the research actually told us
Two weeks of research before touching a single design file. Working alongside a dedicated User Researcher and the Data Science team, we ran interviews, usability testing, empathy mapping, journey mapping, analytics review, card sorting, and a similarity matrix. The goal was to build enough evidence to make decisions we could defend.
Who we were designing for
We started by defining three user archetypes from interview synthesis, then used them throughout the project to pressure-test design decisions. They weren't fictional personas. They were distilled from real patterns we heard across sessions.
The Confident Buyer
Knows her size, wants confirmation
Mia knows she's a medium. She uses Fit Finder to validate, not discover. If the recommendation contradicts her expectation without explanation, she ignores it and checks the size chart anyway. The trust gap on the recommendation reveal screen was her problem.
The Uncertain Measurer
Wants help, gets stuck on inputs
Omar knows his height and weight, but the unit toggle between metric and imperial gives him pause mid-flow. It's a small friction point, but for a user already uncertain about trusting the recommendation, doubt at the input stage compounds into doubt at the result.
Three archetypes built from interview synthesis — each one maps to a specific failure point in the V4.2 flow
Where the journey broke down
Journey mapping the V4.2 flow made the problem visible in a way that analytics alone couldn't. We mapped the emotional arc across every screen; from the moment a shopper clicked "Find my size" through to the recommendation reveal, and marked where confidence dropped, where confusion spiked, and where users abandoned the flow entirely.
Two moments stood out. The brand selection screen caused a sharp confidence dip for users whose brand wasn't listed, and the recommendation reveal triggered disbelief for users whose result didn't match their expectation. Neither moment had any recovery mechanism in V4.2.
The user journey map surfaced two critical failure points: the brand screen and the recommendation reveal. Neither had a recovery path in V4.2
Usability test findings mapped to each screen in the V4.2 flow
What the usability tests found, screen by screen
We ran moderated usability sessions with 12 participants across the full V4.2 flow, conducted remotely via Google Meet. The findings were specific enough to drive direct design decisions, not just general friction, but screen-by-screen evidence of where and why users lost confidence.
What empathy mapping revealed about trust
Empathy mapping sessions after the usability tests and interviews helped us understand what users were thinking and feeling at the moments that mattered most. The pattern was consistent: when a recommendation contradicted a user's expectation, they immediately looked for a reason and found nothing.
The product gave users a result but no reasoning. No context about why that size was recommended, no indication of how confident the model was, and no acknowledgement that their usual size might differ from what Fit Finder was suggesting. Without that, the recommendation felt arbitrary, and arbitrary doesn't convert.
Analytics review of the V4.2 flow: the steepest single drop-off in the flow occurred at Brand selection, with completion continuing to fall across every subsequent brand screen
What analytics and the similarity matrix confirmed
Analytics review revealed a clear pattern: users who made it through measurements, body shape, and personal data questions were abandoning the flow at a disproportionate rate once they hit the brand screens. The single steepest drop in the entire upper body funnel was from Age at 53% to Brand selection at 36% — a 17 point fall in one step. The brand screens then continued to erode completion across three consecutive steps, from 36% down to 20%, before only 14% of users ever reached a result.
Card sorting results; users consistently grouped height, weight, age, and bra size as "personal information" data, with brand preference treated as a separate category entirely
Card sorting provided the mental model evidence to support removing it. Shoppers consistently grouped measurements, weight, age, and bra size together as "personal details", and placed brand selection in a separate category entirely. We were forcing two distinct mental models into one flow.
The similarity matrix made that pattern quantitative. With 10 respondents, the blue clusters show near-perfect agreement; height, weight, chest shape, belly shape, hip shape, and bra size were grouped together by almost everyone. Brand information scored zero across those same groupings. The data wasn't ambiguous: these were two completely separate mental models, and the V4.2 flow had collapsed them into one.
Similarity matrix from the card sorting exercise — blue clusters show how users grouped personal measurements together and separated brand information into a distinct category
01
Brand data: no accuracy impact
Similarity matrix confirmed brand selection had no statistically significant effect on recommendation quality. The screen was legacy friction with no model benefit.
02
Two mental models, one broken flow
Card sorting showed users clearly separate "about me" data (measurements, age, bra size) from brand preference. V4.2 mixed both into a single linear flow which was a structural mismatch.
03
40% couldn't find their brand
Usability data showed 40% of testers couldn't locate their brand in the selection screen. Either it wasn't listed, or they couldn't find it in "More Brands." Drop-off followed immediately.
04
30% didn't trust the final recommendation
Post-flow interviews confirmed 30% of testers didn't act on the size recommendation. They second-guessed it and checked the size chart manually. The problem wasn't the model, it was the interface.
Key design decision
We removed the brand selection screens entirely. This was the most significant structural change in the redesign, informed by both the card sort data and the Data Science team's analysis. Fewer screens, cleaner mental model, no accuracy trade-off.
Design
From sketches to a system
Six weeks of design work, moving from Crazy 8s through sketches, wireframes, and high-fidelity screens. The process ran alongside an accessibility audit and regular design critiques. I also involved junior designers throughout as a way to both pressure-test decisions and develop the team's skills in parallel.
Evaluated V4.2 for heading hierarchy, focus order, screen reader compatibility, and WCAG colour contrast. Identified accessibility issues across the existing flow that needed resolution before any visual redesign.
Fast ideation on alternative approaches to the measurement input, recommendation reveal, and trust-building moments. Eight concepts in eight minutes, then down-selection based on feasibility and user insight alignment.
Mapped the simplified flow without brand screens. Validated with the PM and engineering lead before moving to high fidelity. No wasted polish on concepts that wouldn't ship.
Designed within, and contributed to, the ARES Shopping Suite design system. Built reusable components for measurement inputs, recommendation cards, and progress indicators that could scale across Snap's AR tools.
Interactive prototype tested with users before handoff. Iterated on the recommendation reveal screen twice based on feedback. The first version still felt flat; the second added contextual explanation of why the recommendation was made.
From research to decisions — annotated V5.0 screens
V5.0 desktop and mobile flows
The V5.0 recommendation reveal screen addressed the trust gap directly. A confidence bar, a plain-language explanation grounded in purchase behaviour at scale, and a sizing context note for cases where the result differed from a user's usual size.
Every change in V5.0 was driven by a specific finding from usability testing; brand screens removed, radio buttons replaced with buttons, arrows and ellipses stripped from body shape screens, and help text added to the bra size screen to reduce discomfort
Design system contribution
Fit Finder V5.0 was designed within the ARES Shopping Suite design system; a shared component library built to unify Snap's AR tools including virtual try-on and interactive product displays. I created the sizing-specific components (measurement inputs, recommendation cards, fit preference selectors) so they could be reused across future ARES products without redesign.
Fit Finder components built into the ARES Design System for reuse across Snap's Shopping Suite
Results
Three months, three metrics, one decision
V5.0 launched in Q1 2023. Over a three-month A/B test across 20 retail partners, including global clients in apparel, footwear, and lifestyle, the PM and Data Science team tracked conversion rate, return rate, and user satisfaction against predefined success criteria.
Before the test began, the team aligned on three conditions for a full rollout decision: statistical significance at 95% confidence on conversion rate; no regression on return rate; and a neutral or positive shift in user satisfaction. All three were met. Return rate and satisfaction both exceeded the minimum bar rather than just clearing it.
No single metric told the full story; the three results only made sense together.
Conversion rate uplift via A/B test
Return rate reduction
User satisfaction score
A conversion uplift alone can mean users are being persuaded to buy things that don't fit. A return rate reduction alone could simply mean fewer people are buying. A satisfaction score increase alone could reflect a smoother flow that users enjoyed but didn't trust enough to act on. It was the convergence of all three; more purchases, fewer returns, and users who felt confident throughout, that confirmed the redesign had worked at every level: the flow was easier, the recommendation was trusted, and that trust was warranted.
The brand screens removal was the most impactful single change both in terms of drop-off reduction and the downstream effect on recommendation trust. Shorter flows with less friction correlated directly with higher satisfaction scores. The improved recommendation reveal, which now included a brief contextual explanation alongside a sizing context note for brands that run large or small, was flagged by qualitative feedback as a meaningful trust signal. Two design decisions, each grounded in research, each pulling in the same direction.
| Metric | Fit Analytics client range | V5.0 a/b test result |
|---|---|---|
| Conversion rate | +1% to +22% (avg. ~+8%) | +14% |
| Return rate reduction | –2% to –20% (avg. ~–10%) | –12% |
| User satisfaction | No published benchmark | +22% vs V4.2 baseline |
With all three success criteria met, statistical significance confirmed, and results that outperformed the broader Fit Analytics client benchmark, the PM and engineering teams moved to full rollout across all retail partners. V5.0 became the new baseline for all Fit Finder deployments going forward. The decision to roll out fully was straightforward.
Reflection
What I'd do differently
Honest retrospective
Two changes didn't make it into V5.0. The body shape illustrations , used on the belly and hip shape screens, had been flagged as visually dated and not representative enough of the range of body types Fit Finder serves. Updating them was scoped and prioritised, then pushed in favour of the structural flow changes. They shipped as-is, which was a compromise we weren't entirely comfortable with.
The fit preference screen also retained "Very Loose" and "Very Tight" at either end of the scale. The argument for removing them was straightforward, almost no one selects the extremes, and their presence made the middle options feel less decisive. That change got deprioritised for the same reason: lower impact relative to the brand-screen removal and the recommendation reveal work. Both are on the backlog. Both should have shipped with V5.0.
Appendix
Role & collaborators
My contribution
Lead Product Designer across the full project lifecycle; from defining the research approach and running the accessibility audit through to high-fidelity design, design system contribution, prototyping, and developer handoff. Beyond the craft, I drove the key structural decision to remove the brand screens, working directly with the PM and Data Science team to validate it against the recommendation model before it went anywhere near a design file. I also ran weekly design critiques with junior designers, using the project as a structured mentorship opportunity.
Collaborators who made this work
Engineering Lead + Developers — implementation, handoff, QA
Product Designer — ARES Design System co-development
Data Scientists — analytics review, recommendation model validation, A/B analysis
User Researcher — interview facilitation, usability testing, synthesis
Product Manager — project scoping, stakeholder alignment, A/B test oversight