The Overview
Origin was a new brand aiming to increase conversions and encourage repeat purchases by building customer trust and shopping confidence.
The Problem
Gen Z shoppers want to buy clothes online, but they rarely feel sure enough to do it. Size, fabric and quality are hard to judge from a screen, and the buying process often feels slow and frustrating.
What I Did
As the sole product designer, I took Origin’s mobile store from zero to launch-ready: 5 user interviews, a 52-person survey, competitive analysis of 4 platforms, and the end-to-end purchase flow from product page to checkout.
The Outcome
All metrics are based on participant responses and observed behaviour during the prototype testing.
Based on testing 4 key user flows with 12 participants across the Origin website.
The Stakes
Understanding the business goals, shopper challenges, and constraints that shaped the direction.
Business Problem
Origin was launching into a market led by Myntra, Ajio and Meesho, with a small marketing budget and no name recognition. The goal was first-purchase conversion. With little budget to bring visitors back, every first-time shopper who left unsure was a lost sale, and every wrong-size order risked a return.
User Problem
Gen Z shoppers find clothes on Instagram and buy them on their phones. The product page is where it breaks: they look for reviews, can’t judge the fabric, and doubt their usual size will fit (“I have 0% confidence in fit”). Those who push past that often quit at checkout, stopped by forced logins and extra steps.
In a survey of 52 shoppers aged 18–25, 9 in 10 who left a product they liked blamed uncertainty, not price: unclear size (36.5%), unconvincing product details (36.5%) or distrust of the brand (17.3%).
Constraints
- New brand: no reviews and a small marketing budget
- Mobile-first: most shopping happens on a phone
- Short attention: pages must be scannable in seconds
- Return-sensitive: unclear sizing leads to returns
- MVP scope: one designer, so core features only, with extra features and animations held back
What We Learned
What shoppers told us, and what it meant for the design
Proof comes from other shoppers, not the brand.
65% named reviews as the single deciding factor. Interviewees dropped products when customer photos didn’t match the listing.
Shoppers want a way out before they commit.
71% said an easy return policy must be visible before they’d buy from a new brand, and 46% wanted COD. Only 8% picked discounts as what mattered most.
Size doubt ends in one of two ways: a lost sale or a return.
Shoppers default to their usual size and plan to “alter it later.” Every interviewee said they’d return a bad fit.
Login is where checkout breaks.
3 of 5 interviewees complained about forced sign-up mid-purchase.
Shoppers didn’t need persuading.They needed proof before buying, a safety net after, and nothing in the way between.
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Principles
Four rules we used to decide every design call.
Proof over promises.
Show what real customers and real measurements say before anything the brand says about itself.
Make it safe to say yes.
Show returns, COD and delivery dates where shoppers decide, so buying feels low-risk.
Few clicks to purchase.
Every extra step, login or popup between choosing and paying is a chance to leave.
Essentials first, details one tap away.
Shoppers should be able to scan a page in seconds without losing anything they need.
Decision 1: The Origin Fit Guide
Principle: Proof over promises
Problem
Online shoppers struggle to choose the right clothing size due to inconsistent brand sizing and unclear fit information. 36.5% (19/52) avoid adding products to their cart due to unclear size or fit information, while 67.3% (35/52) check size charts before buying. This uncertainty can reduce purchase confidence and contribute to poor fit, alterations, and returns.
What the page had to answer (survey + interviews)
- Personal fit : A size based on the shopper’s own body measurements and build, not standard S, M or L labels that change from brand to brand.
- Quick to use : Ask only for things shoppers already know, so finding a size takes seconds.
- Confidence to buy : Enough fit information to add to cart without guessing, altering later or planning a return.
Options Explored
A personalised size recommendation tool that asks shoppers for their height, weight, and body type (Slim, Average, or Broad) to suggest a suitable clothing size. This reduces guesswork during size selection and helps shoppers feel more confident about purchasing clothes online.
Version 1: Height and weight
Asked only for height and weight and returned one size, but shoppers with the same measurements can differ in build.
Version 2: Adding body type
Added Slim, Average and Broad so the size reflects build too. It still returned one size, assuming everyone wants the same fit.
Version 3: Three fit options
Shows three fits from the same inputs: Slim (M), Regular (L) and Oversized (XL), so shoppers pick the fit they prefer.
Trade-off
Initially, we planned to recommend one best-fit size based on height, weight, and body type. However, users have different fit preferences, from slim to oversized. So, we introduced three options: Slim Fit (M), Best Fit (L), and Oversized Fit (XL), allowing users to choose their preferred fit.
Validation & Iteration
(5 participants)We tested this approach with five participants, and all five responded positively. However, the small sample limits how broadly we can generalise the results. The feature needs further testing with a larger user group and refinement based on real-world usage, feedback, and fit outcomes.
Decision 2: Simplified checkout
Principle: Few clicks to purchase
Problem
Checkout friction caused by multiple steps and repeated data entry can discourage users from completing purchases, directly impacting Origin’s primary success metric: conversion rate. With 46.2% (24/52) preferring checkout without creating an account, there is an opportunity to simplify the journey through guest checkout and fewer steps.
What the page had to answer (survey + interviews)
Increase purchase completion by reducing friction between adding a product to the cart and placing the order.
- Fewer steps : Reduce the steps it takes to reach payment.
- Familiar flow : Keep the checkout familiar and intuitive.
- Less clutter : Remove unnecessary elements.
- Confidence to order : Present the essential information that builds purchase confidence, so shoppers complete their orders without confusion or doubt.
Final Choice
Single-page cart, a separate payment step, and guest checkout
- We kept Option 1’s single-page cart: items, delivery address, offers and the bill on one page.
- Payment methods moved to the next page, alongside an overview of the order. This prevents accidental Cash on Delivery orders.
- We then added guest checkout from Option 2, so shoppers can buy without creating an account.
Trade-off
The cart still holds a lot of detail, which may overwhelm some users. So we removed extras like product suggestions and kept only what shoppers need to buy with confidence.
Checkout still took two pages. For single-product purchases, we added Quick Buy on the product page, which goes straight to checkout and isn’t available on competitor platforms.
Validation & Iteration
(12 participants)All 12 participants completed checkout without confusion. However, 4 of 12 looked for offers and coupons on the cart page, where other platforms place them. We had placed them on the checkout page with the payment methods, so we moved them to the cart.
Other Key Decisions
A few other decisions shaped by the research and survey.
Emotion-Led Reviews
Replaced traditional star ratings with emotion-based expressions, ranging from sad to extremely satisfied, to make customer feedback more relatable and help shoppers connect with real customer experiences, potentially building trust in a new brand.
Brand-Level Trust Signals
When a product has no reviews, showcase brand-level trust signals such as overall customer ratings, feedback, and product quality assurances to reassure shoppers and build confidence in purchasing from a new brand.
Making Customer Photos Discoverable
Added a customer photo shortcut below product images, making real customer photos and reviews easier to discover, a key trust signal for the 65% of respondents who valued customer reviews.
Tailored Shopping Experiences
Based on research, users fell into two groups: occasional buyers who prioritise festival and sale offers, and frequent buyers who focus on trends. To cater to both, I introduced two dedicated pages: Sale, featuring discounted products, and Lookbook, showcasing current trends through outfit imagery with shoppable products.
Validation
Moderated usability test on Maze with 12 participants. Each person completed all 4 tasks. I observed and never guided participants at any point.
Finding 1
Users needed multiple clicks to reach the return page.
What we saw
All 12 participants completed the return task, but multiple clicks and hesitation slowed them down before reaching the return page.
What I changed
I added a post-delivery pop-up to collect ratings and reviews, with a direct return or exchange option for users who give low ratings.
Impact
Pre-launch usability testing with 12 participants across 4 tasks.
Easier Product Discovery
(Research: 46.2% said too many product options held them back.)9 of 12 reported no challenges finding a product, while 3 struggled with browsing and identifying a suitable item.
More Informed Purchase Decisions
(Research: 36.5% avoided adding products to cart due to unclear size or fit information.)11 of 12 found it easy to evaluate the product, but 6 had some doubts about the Fit Guide’s size recommendations.
Clearer Checkout Experience
(Research: 46.2% preferred checkout without creating an account.)9 of 12 said the checkout flow was very clear, while all 12 felt confident about the checkout information and handling of their details.
Successful Exchange Completion
(Research: 71.2% said an easy return policy would increase their confidence in buying from a new brand.)All 12 participants completed the exchange task, with 10 experiencing no or only minor hesitation during the process.
After launch, we’d track success through
- Primary: Conversion rate
- Supporting: Add-to-cart rate, checkout completion rate, return rate
- Business: Repeat purchase rate
Reflection
- All 5 early testers liked the Fit Guide, but 6 of 12 later doubted its size. A recommendation only builds trust when shoppers can see why it fits them, so a positive small test is a start, not proof.
- 4 of 12 looked for coupons on the cart because that’s where other platforms put them. For a new brand, I learned to stay familiar by default and save new ideas for real doubts, like fit, reviews and Quick Buy.
- Owning every stage alone kept research and design closely linked, but nobody challenged my early ideas. Next time, I’d get feedback sooner and plan to track fit accuracy and returns after launch.