Helping users find their size while shopping online
Helping users find their size while shopping online
Helping users find their size while shopping online
Helping users find their size while shopping online
B2C Mobile Feature / E-Commerce
Outcome - Shipped, SUS Score 78
Outcome - Shipped, SUS Score 78
Sizing is one of the biggest sources of friction in online fashion retail. At Tata CLiQ, high return rates and low user confidence made it clear the current experience was not helpful. To address this, I designed a layered guidance system to help users make more confident sizing choices. This included contextual nudges based on product type and user behavior, redesigned size chart with clearer, brand-specific information, and a fit assistant that supported cross-brand comparisons and personalized recommendations.
Together, these tools were designed to reduce decision fatigue, improve accuracy, and ultimately lower return rates. The solution achieved a usability score of 78 and parts of it have been shipped while some parts remain in RnD.
This project continues to influence how I think about designing for uncertainty, choice, and trust.
Sizing is one of the biggest sources of friction in online fashion retail. At Tata CLiQ, high return rates and low user confidence made it clear the current experience was not helpful. To address this, I designed a layered guidance system to help users make more confident sizing choices. This included contextual nudges based on product type and user behavior, redesigned size chart with clearer, brand-specific information, and a fit assistant that supported cross-brand comparisons and personalized recommendations.
Together, these tools were designed to reduce decision fatigue, improve accuracy, and ultimately lower return rates. The solution achieved a usability score of 78 and parts of it have been shipped while some parts remain in RnD.
This project continues to influence how I think about designing for uncertainty, choice, and trust.
WHAT I DID
WHAT I DID
Owned E-2-E design & research for this feature
Owned E-2-E design & research for this feature
OUTCOMES
OUTCOMES
Shipped, SUS score 78
Shipped, SUS score 78
DURATION
DURATION
4 Months / 2022
4 Months / 2022
TEAM
TEAM
Nikita S (Manager) ; Aditi L (Mentor) ; Anant K (UI & Illustrations)
Nikita S (Manager) ; Aditi L (Mentor) ; Anant K (UI & Illustrations)
THE BUSINESS PROBLEM
THE BUSINESS PROBLEM
Apparel Returns are Expensive and Hurt Brand Value
Apparel Returns are Expensive and Hurt Brand Value
Users struggled to choose their size while shopping online, especially across brands with inconsistent charts and fit standards. This led to frequent misorders, high apparel return rates, and over $1M in annual return processing costs for Tata. This was the primary business problem we aimed to solve for.
Users struggled to choose their size while shopping online, especially across brands with inconsistent charts and fit standards. This led to frequent misorders, high apparel return rates, and over $1M in annual return processing costs for Tata. This was the primary business problem we aimed to solve for.
Approximately 83.5% Returns were due to Incorrect Sizing this translates to $1 Million plus added costs of processing returns
Approximately 83.5% Returns were due to Incorrect Sizing this translates to $1 Million plus added costs of processing returns
Approximately 83.5% Returns were due to Incorrect Sizing this translates to $1 Million plus added costs of processing returns
RESEARCH
RESEARCH
Why are so many users returning products?
Why are so many users returning products?
To understand what the user challenges are I conducted research that included qualitative and quantitative insights to understand the root cause behind the return patterns.
To understand what the user challenges are I conducted research that included qualitative and quantitative insights to understand the root cause behind the return patterns.
Audit & Competitive Benchmarking
Audit & Competitive Benchmarking
370 Survey Responses
370 Survey Responses
10 User Interviews
10 User Interviews
App Data
Lack of guidance during shopping
Lack of guidance during shopping
The only sizing tool available was a static size chart, lacking the context users needed to make informed decisions. Key details—like material, fit, and size guidance—were scattered across the page and disconnected from the purchase flow.
The only sizing tool available was a static size chart, lacking the context users needed to make informed decisions. Key details—like material, fit, and size guidance—were scattered across the page and disconnected from the purchase flow.
Perceived confidence didn’t match outcomes
Perceived confidence didn’t match outcomes
Although over 65% of users said they felt confident about their size and referred to the size chart, actual usage was low—only 8% interacted with it. Additional we also saw a high apparel return rate. Interviews revealed that sizing decisions were often made based on gut feeling, memory, or visual cues—suggesting a reliance on fast, intuitive decision-making (System 1 thinking). This gap between stated behaviour and actual use highlighted the need for in-flow, low-effort sizing guidance.
Although over 65% of users said they felt confident about their size and referred to the size chart, actual usage was low—only 8% interacted with it. Additional we also saw a high apparel return rate. Interviews revealed that sizing decisions were often made based on gut feeling, memory, or visual cues—suggesting a reliance on fast, intuitive decision-making (System 1 thinking). This gap between stated behaviour and actual use highlighted the need for in-flow, low-effort sizing guidance.
How confident are you in choosing the right size when shopping online?
Fit type mattered: skinny, loose, or baggy
Fit type mattered: skinny, loose, or baggy
However, this dimension of fit was missing from the existing sizing solution. Interviews with 10+ users and 370 survey responses consistently emphasized the need for this information.
However, this dimension of fit was missing from the existing sizing solution. Interviews with 10+ users and 370 survey responses consistently emphasized the need for this information.
I ordered my usual size, but the skinny jeans were way too tight, but straight-leg ones fit perfectly.
23 Yr Old, Curvy Shopper
The 100% cotton jeans fit snug at first, but they loosened up after a few wears. I wish brands gave better information on fabric type.
46 yr old, New to online shopping
I always size up for slim-fit jeans because they never fit the same as relaxed-fit ones.
35 Yr Old, Frequent Online Shopper
Inconsistent sizing across 6,000+ brands on Tata Cliq.
Inconsistent sizing across 6,000+ brands on Tata Cliq.
App data revealed that return rates were higher for certain brands—especially those with non-standard size charts. Without cross-brand sizing guidance, users defaulted to their usual size. Returns were significantly more likely when shopping from a brand for the first time.
App data revealed that return rates were higher for certain brands—especially those with non-standard size charts. Without cross-brand sizing guidance, users defaulted to their usual size. Returns were significantly more likely when shopping from a brand for the first time.
SOLUTION
Highlight the Right Information During Size Selection
Highlight the Right Information During Size Selection
SOLUTION
SOLUTION
Personalised Size Recommendations
Personalised Size Recommendations
SOLUTION
SOLUTION
Size Chart Redesign
Size Chart Redesign
OUTCOMES & REFLECTION
OUTCOMES & REFLECTION
Outcomes, Next Steps
Outcomes, Next Steps
I tested iteratively with 5 users and the stakeholders to reach our final solution. The final usability test highlighted interesting insights and laid out next steps for the team and organisation:
Usability: Users moved through the Fit Assistant flow in under a minute on average. We received a SUS score of 78, suggesting the experience felt intuitive and easy to use.
We found that users were more likely to look at sizing information with our new design as compared to the previous design.
Potential Accuracy Gaps: While the experience felt smooth, we hadn’t yet tested the technical accuracy of the size recommendations—a key next step.
Cultural Insight: During beta testing, we noticed an unexpected behavior pattern: in Indian households, it’s common for people to shop for friends or family using a shared account. This broke our assumption of a one-user-to-one-account model and led to mismatches in automated sizing.
I tested iteratively with 5 users and the stakeholders to reach our final solution. The final usability test highlighted interesting insights and laid out next steps for the team and organisation:
Usability: Users moved through the Fit Assistant flow in under a minute on average. We received a SUS score of 78, suggesting the experience felt intuitive and easy to use.
We found that users were more likely to look at sizing information with our new design as compared to the previous design.
Potential Accuracy Gaps: While the experience felt smooth, we hadn’t yet tested the technical accuracy of the size recommendations—a key next step.
Cultural Insight: During beta testing, we noticed an unexpected behavior pattern: in Indian households, it’s common for people to shop for friends or family using a shared account. This broke our assumption of a one-user-to-one-account model and led to mismatches in automated sizing.
If I had more time, I’d focus on designing for shared purchasing behaviors that emerged during testing. A few potential directions:
Tagging recipients post-checkout, so the Fit Assistant could learn patterns based on who the purchase was for.
Adding multi-profile support within accounts, making it easier to shop for loved ones without disrupting recommendations.
Finally, I’d conduct validation testing to measure the accuracy of the fit suggestions and iterate based on confidence levels and returns data.
If I had more time, I’d focus on designing for shared purchasing behaviors that emerged during testing. A few potential directions:
Tagging recipients post-checkout, so the Fit Assistant could learn patterns based on who the purchase was for.
Adding multi-profile support within accounts, making it easier to shop for loved ones without disrupting recommendations.
Finally, I’d conduct validation testing to measure the accuracy of the fit suggestions and iterate based on confidence levels and returns data.