B2c | E-Commerce | PRINT-ON-DEMAND | APPAREL DESIGN
B2c / E-Commerce / PRINT-ON-DEMAND
Redesigning the Sizing Experience at Jiffy Transfers
Redesigning the Sizing Experience at Jiffy Transfers
Jiffy Transfers is a DTF (Direct-to-Film) transfer printing platform where small business owners and hobbyists upload designs to print on apparel. During my time leading the design, the business grew from ~$750K to $1.5M in weekly revenue in 6 months.
This case highlights a key initiative that helped unlock that growth by improving conversion at a critical friction point.
Jiffy Transfers is a DTF (Direct-to-Film) transfer printing platform where small business owners and hobbyists upload designs to print on apparel. During my time leading the design, the business grew from ~$750K to $1.5M in weekly revenue in 6 months.
This case highlights a key initiative that helped unlock that growth by improving conversion at a critical friction point.
RESPONSIBILITIES
Research & strategy, design & prototyping, usability testing, stakeholder alignment
Research & strategy, design & prototyping, usability testing, stakeholder alignment
Tools
Mixpanel, Hotjar, Figma, Userlytics, Dovetail
Mixpanel, Hotjar, Figma, Userlytics, Dovetail
TIMELINE
12 weeks
12 weeks
Results
-62% drop-off rate
-62% drop-off rate
+45% new users conversion
+45% new users conversion
-34% support tickets
-34% support tickets
RESPONSIBILITIES
Research & strategy
Design & prototyping
Usability testing
Stakeholder alignment
Tools
Mixpanel
Hotjar
Figma
Userlytics
Dovetail
TIMELINE
12 weeks
Results
-62% drop-off rate
+45% new users conversion
-34% support tickets
The problem
At Jiffy Transfers, users upload artwork to print custom heat transfers.
Users dropped off after uploading a design due to lack of sizing guidance, no visual preview, and unclear expectations.
🚫 56% of users abandoned the process at this step.
This confusion caused repeated actions, low conversion, and high support ticket volume.
Project goal: Reduce drop-off after image upload and improve order speed.
DISCOVERY
DISCOVERY
Digging in: patterns, behavior and assumptions
To uncover the behavioral and structural friction behind high drop-off rates, I designed and ran a multi layer discovery process that combined analytics, surveys, interviews, and internal expertise.
To uncover the behavioral and structural friction behind high drop-off rates, I designed and ran a multi layer discovery process that combined analytics, surveys, interviews, and internal expertise.
Extracting Existing Data > Forming Hypothesis > Validating
Extracting Existing Data > Forming Hypothesis > Validating
The problem
At Jiffy Transfers, users upload artwork to print custom heat transfers.
Users dropped off after uploading a design due to lack of sizing guidance, no visual preview, and unclear expectations.
🚫 56% of users abandoned the process at this step.
This confusion caused repeated actions, low conversion, and high support ticket volume.
DISCOVERY
Digging in: patterns, behavior and assumptions
To uncover the behavioral and structural friction behind high drop-off rates, I designed and ran a multi layer discovery process that combined analytics, surveys, interviews, and internal expertise.
Extracting Existing Data > Forming Hypothesis > Validating
Extracting existing data
Extracting existing data
Mixpanel Analytics
Mixpanel Analytics
Confirmed drop-off timing and analyzed upload event patterns to understand user behavior
Confirmed drop-off timing and analyzed upload event patterns to understand user behavior
Critical Insight
Critical Insight
This behavior didn't explain the drop directly — but it surfaced something crucial: Users didn't just want to upload once they were trying to get multiple sizes of the same design.
Hotjar Sessions
Hotjar Sessions
Reviewed actual user sessions to see real-time behavior and identify friction points
Reviewed actual user sessions to see real-time behavior and identify friction points
User Journey Breakdown
User Journey Breakdown
Step 1: Users uploaded their design
Step 2: They toggled sizing fields — manually inputting width/height
Step 3: Then… they abandoned the process
Forming hypothesis
Forming hypothesis
Sizing Uncertainty
Sizing Uncertainty
The drop-off is driven by uncertainty around sizing, especially for new users who don't know what size to enter after uploading their image.
The drop-off is driven by uncertainty around sizing, especially for new users who don't know what size to enter after uploading their image.
The drop-off is driven by uncertainty around sizing, especially for new users who don't know what size to enter after uploading their image.
Multiple Size Needs
Multiple Size Needs
Users need to order the same design in multiple sizes for different apparel types, t-shirt sizes, and placements.
Users need to order the same design in multiple sizes for different apparel types, t-shirt sizes, and placements.
Users need to order the same design in multiple sizes for different apparel types, t-shirt sizes, and placements.
Validating hypothesis
Validating hypothesis
Customer support
insights
Customer support insights
Analyzed customer support tickets for direct user feedback and pain points
Analyzed customer support tickets for direct user feedback and pain points
User outreach
(drop-off cohort)
User outreach (drop-off cohort)
Proactively contacted users who abandoned the experience after upload to understand friction in the sizing step.
Proactively contacted users who abandoned the experience after upload to understand friction in the sizing step.
1:1 Interviews
with power users
1:1 Interviews with power users
Explored how experienced users approach sizing decisions and what they expect from the flow.
Explored how experienced users approach sizing decisions and what they expect from the flow.
Targeted surveys
(500+)
Targeted surveys (500+)
Collected input from users who uploaded the same image 5+ times.
Collected input from users who uploaded the same image 5+ times.
Demos
with Internal SMEs
Demos with Internal SMEs
Conducted hands-on testing with in-house crafters to validate sizing logic, terminology, and real-world usability from an expert’s perspective.
Conducted hands-on testing with in-house crafters to validate sizing logic, terminology, and real-world usability from an expert’s perspective.
Ecosystem deep dive
Reviewed 25+ YouTube tutorials, cheat sheets, Reddit threads, and community guides to map language, workflows, and edge cases.
Industry expert
interviews
Industry expert interviews
Spoke with 8 pro printers and shop owners to understand real-world sizing logic, material limitations, and transfer placement standards.
Spoke with 8 pro printers and shop owners to understand real-world sizing logic, material limitations, and transfer placement standards.
Ecosystem deep dive
Ecosystem deep dive
Reviewed 25+ YouTube tutorials, cheat sheets, Reddit threads, and community guides to map language, workflows, and edge cases.
Reviewed 25+ YouTube tutorials, cheat sheets, Reddit threads, and community guides to map language, workflows, and edge cases.
DISCOVERY
Research highlights
Research highlights
DISCOVERY
Support data confirmed a steady stream of tickets related to size confusion. Users weren’t sure how big their design should be or where it would land.
Support data confirmed a steady stream of tickets related to size confusion. Users weren’t sure how big their design should be or where it would land.
Support data confirmed a steady stream of tickets related to size confusion. Users weren’t sure how big their design should be or where it would land.
Internal demos with our in-house crafters revealed that the multi-size workflow was manual and repetitive. They had to upload the same design multiple times with slightly different dimensions.
Internal demos with our in-house crafters revealed that the multi-size workflow was manual and repetitive. They had to upload the same design multiple times with slightly different dimensions.
Internal demos with our in-house crafters revealed that the multi-size workflow was manual and repetitive. They had to upload the same design multiple times with slightly different dimensions.
Power users described multi-size orders as a standard flow, not an edge case, needed to support different placements (front, sleeve, back) or garment types (t-shirts vs. hoodies).
Power users described multi-size orders as a standard flow, not an edge case, needed to support different placements (front, sleeve, back) or garment types (t-shirts vs. hoodies).
Power users described multi-size orders as a standard flow, not an edge case, needed to support different placements (front, sleeve, back) or garment types (t-shirts vs. hoodies).
Together, these findings confirmed that the issue was a mismatch between real workflows and what the system supported.
Together, these findings confirmed that the issue was a mismatch between real workflows and what the system supported.
Together, these findings confirmed that the issue was a mismatch between real workflows and what the system supported.
The problem
At Jiffy Transfers, users upload artwork to print custom heat transfers.
Users dropped off after uploading a design due to lack of sizing guidance, no visual preview, and unclear expectations.
🚫 56% of users abandoned the process at this step.
This confusion caused repeated actions, low conversion, and high support ticket volume.
INSIGHT #1
Confidence breaks down at the
Confidence breaks down at the sizing step
sizing step
After upload, many users hesitated to proceed. Without any visual confirmation or guardrails, they lacked confidence that their selected dimensions would print as expected — especially first-time or less experienced crafters.
“I just guess and hope it’s not too small when it prints.”
“I just guess and hope it’s not too small when it prints.”
INSIGHT #1
Confidence breaks down
at the sizing step
After upload, many users hesitated to proceed. Without any visual confirmation or guardrails, they lacked confidence that their selected dimensions would print as expected — especially first-time or less experienced crafters.
“I just guess and hope it’s not too small when it prints.”
INSIGHT #2
Users think in placement,
not inches
Most users didn’t think in inches. Their mental model was based on shirt type and design placement. Beginners lacked any frame of reference, while experienced users relied on familiar patterns like “10×10 for a front design.”
“I usually have to Google shirt design sizes or check my cheat sheet before I can even start, otherwise I have no idea what to enter.”
INSIGHT #3
Users create time -
consuming workarounds
to order multiple sizes
transfers
Users who needed different sizes of the same design manually re-uploaded the file multiple times (one upload per size). This repetitive workaround reflected a missing workflow for managing variation, leading to delays, frustration, and drop-offs.
"If I need three sizes, I upload the same file three times. It's just the same task over and over."
INSIGHT #4
Users lack visual context
for sizing decisions
Many users weren’t sure if the selected size would feel right on a real shirt. They found it difficult to imagine how the design would appear on a real shirt — especially when switching placements (e.g., front vs. sleeve) or garment types. This uncertainty contributed to hesitation and drop-off.
"I just couldn't tell if it would look too small or too big on the shirt. There's nothing that shows you how it'll actually turn out."
PROBLEM STATEMENT
Users struggle to choose the right transfer size after uploading a design, due to a lack of guidance, visual context, and clarity around placement.
This leads to confusion, repeated actions, and a drop-off rate among new users and directly impacts conversion and increasing support tickets.
INSIGHT #2
Users think in placement,
Users think in placement, not inches
not inches
Most users didn’t think in inches. Their mental model was based on shirt type and design placement. Beginners lacked any frame of reference, while experienced users relied on familiar patterns like “10×10 for a front design.”
“I usually have to Google shirt design sizes or check my cheat sheet before I can even start — otherwise I have no idea what to enter.”
“I usually have to Google shirt design sizes or check my cheat sheet before I can even start, otherwise I have no idea what to enter.”
INSIGHT #3
Users create time-consuming workarounds
Users create time-consuming
workarounds to order multiple
to order multiple sizes transfers
sizes transfers
Users who needed different sizes of the same design manually re-uploaded the file multiple times (one upload per size). This repetitive workaround reflected a missing workflow for managing variation, leading to delays, frustration, and drop-offs.
"If I need three sizes, I upload the same file three times. It's just the same task over and over."
"If I need three sizes, I upload the same file three times. It's just the same task over and over."
INSIGHT #4
Users lack visual context for sizing decisions
Users lack visual context for
sizing decisions
Many users weren’t sure if the selected size would feel right on a real shirt. They found it difficult to imagine how the design would appear on a real shirt — especially when switching placements (e.g., front vs. sleeve) or garment types. This uncertainty contributed to hesitation and drop-off.
“I just couldn’t tell if it would look too small or too big on the shirt. There’s nothing that shows you how it’ll actually turn out.”
"I just couldn't tell if it would look too small or too big on the shirt. There's nothing that shows you how it'll actually turn out."
PROBLEM STATEMENT
Users struggle to choose the right transfer size after uploading a design, due to a lack of guidance, visual context, and clarity around placement.
This leads to confusion, repeated actions, and a drop-off rate among new users and directly impacts conversion and increasing support tickets.
Users struggle to choose the right transfer size after uploading a design, due to a lack of guidance, visual context, and clarity around placement.
This leads to confusion, repeated actions, and a drop-off rate among new users and directly impacts conversion and increasing support tickets.
ITERATION #1: DETOUR
ITERATION #1: DETOUR
Lightweight multi-size support, a fast strategic release
Discovery revealed multiple opportunities. We prioritized two interconnected issues that could reduce workflow friction while building for scale:
1. Users were unsure what size to enter after uploading
2. Many re-uploaded the same image multiple times to order different sizes
Discovery revealed multiple opportunities. We prioritized two interconnected issues that could reduce workflow friction while building for scale:
1. Users were unsure what size to enter after uploading
2. Many re-uploaded the same image multiple times to order different sizes
Discovery revealed multiple opportunities. We prioritized two interconnected issues that could reduce workflow friction while building for scale:
1. Users were unsure what size to enter after uploading
2. Many re-uploaded the same image multiple times to order different sizes
We prioritized this to
We prioritized this to
Reduce workflow friction
Reduce workflow friction
Allow users to select all needed sizes in one
upload, eliminating unnecessary steps and
repeat actions.
Allow users to select all needed sizes in one upload, eliminating unnecessary steps and repeat actions.
Allow users to select all needed sizes in one upload, eliminating unnecessary steps and repeat actions.
Optimize resource use
Optimize resource use
Each image was enhanced once per session, reducing backend strain and saving AI prompt credits.
Each image was enhanced once per session, reducing backend strain and saving AI prompt credits.
Ship impact fast
Ship impact fast
Deliver measurable UX and system
improvements in weeks, without major
architectural changes.
Deliver measurable UX and system
improvements in weeks, without major
architectural changes.
Deliver measurable UX and system
improvements in weeks, without major architectural changes.
Build for what’s next
Build for what’s next
Create the structural foundation needed for
future features like smart size suggestions and
visual guidance.
Create the structural foundation needed for future features like smart size suggestions and visual guidance.
Create the structural foundation needed for future features like smart size suggestions and
visual guidance.
THE OPPORTUNITY
THE OPPORTUNITY
Discovery revealed that users were already mentally grouping sizes together.
"I need a 10×10 for the front and 6×6 for the sleeve".
But the system forced them to re-upload the same image for each size increasing frustration, repetition, and drop-off.
Discovery revealed that users were already mentally grouping sizes together.
"I need a 10×10 for the front and 6×6 for the sleeve".
But the system forced them to re-upload the same image for each size increasing frustration, repetition, and drop-off.
THE SOLUTION
THE SOLUTION
Enable users to add multiple sizes of the same design within a single upload session.
This small but strategic change aligned with real user behavior, reduced friction, cut backend strain, and laid the groundwork for a more scalable experience.
Enable users to add multiple sizes of the same design within a single upload session.
This small but strategic change aligned with real user behavior, reduced friction, cut backend strain, and laid the groundwork for a more scalable experience.
Enable users to add multiple sizes of the same design within a single upload session.
This small but strategic change aligned with real user behavior, reduced friction, cut backend strain, and laid the groundwork for a more scalable experience.
Ideation & rapid prototyping
Ideation & rapid prototyping
Mapped scope and impact
Mapped scope and impact
Led a focused working session with product and engineering to define a minimal solution that would deliver clear user value without introducing technical debt.
Led a focused working session with product and engineering to define a minimal solution that would deliver clear user value without introducing technical debt.
Mapped scope and impact
Led a focused working session with product and engineering to define a minimal solution that would deliver clear user value without introducing technical debt.
Built directly into the flow
Built directly into the flow
Designed and prototyped the multi-size interaction using existing upload logic, ensuring seamless integration and fast developer handoff.
Designed and prototyped the multi-size interaction using existing upload logic, ensuring seamless integration and fast developer handoff.
Built directly into the flow
Designed and prototyped the multi-size interaction using existing upload logic, ensuring seamless integration and fast developer handoff.
Validated with diverse users
Validated with diverse users
Ran targeted testing sessions with both new and returning users to confirm the solution improved clarity, reduced effort, and eliminated repeat uploads.
Ran targeted testing sessions with both new and returning users to confirm the solution improved clarity, reduced effort, and eliminated repeat uploads.
Validated with diverse users
Ran targeted testing sessions with both new and returning users to confirm the solution improved clarity, reduced effort, and eliminated repeat uploads.
Validation results
Validation results
Usability testing confirmed the multi-size workflow solved the repetitive upload problem and all participants easily found and used 'Add Another Size.' However, testing revealed the deeper issue: participants consistently expressed uncertainty about choosing dimensions ('I don't know what size to put for S, M, L'). One participant specifically requested preset labels like 'Adult' or 'ideal for Kids'
Usability testing confirmed the multi-size workflow solved the repetitive upload problem and all participants easily found and used 'Add Another Size.'
However, testing revealed the deeper issue: participants consistently expressed uncertainty about choosing dimensions ('I don't know what size to put for S, M, L'). One participant specifically requested preset labels like 'Adult' or 'ideal for Kids'
Usability testing confirmed the multi-size workflow solved the repetitive upload problem - all participants easily found and used 'Add Another Size.' However, testing revealed the deeper issue: participants consistently expressed uncertainty about choosing dimensions ('I don't know what size to put for S, M, L'). One participant specifically requested preset labels like 'Adult' or 'ideal for Kids'
94%
94%
Task Completion Rate
Task Completion Rate
15+ users across segments
15+ users across segments
Low
Low
Implementation Risk
Implementation Risk
Clear technical path
Clear technical path
3wks
3wks
Time to Market
Time to Market
vs. 2+ months for full solution
"Very simple, intuitive, step-by-step process worked beautifully"
"Very simple, intuitive, step-by-step process worked beautifully"
Usability testing participant
I would be confused, as I don't know what size to put for S, M, L... I might want to see the t-shirt size, so I can put in right transfer size for it.
I would be confused, as I don't know what size to put for S, M, L... I might want to see the t-shirt size, so I can put in right transfer size for it.
Usability testing participant
ITERATION #2: SOLVING THE MAIN PAIN POINTS REVEALED
ITERATION #2: SOLVING THE MAIN PAIN POINTS REVEALED
Defining the new standard in transfers sizing
Defining the new standard in transfers sizing
Defining the new standard in transfers sizing
Back to the main problem
Back to the main problem
PROBLEM STATEMENT
Users struggle to choose the right transfer size after uploading a design, due to a lack of guidance, visual context, and clarity around placement.
This leads to confusion, repeated actions, and a drop-off rate among new users and directly impacts conversion and increasing support tickets.
Users struggle to choose the right transfer size after uploading a design, due to a lack of guidance, visual context, and clarity around placement.
This leads to confusion, repeated actions, and a drop-off rate among new users and directly impacts conversion and increasing support tickets.
PROBLEM STATEMENT
Users struggle to choose the right transfer size after uploading a design, due to a lack of guidance, visual context, and clarity around placement.
This leads to confusion, repeated actions, and a drop-off rate among new users and directly impacts conversion and increasing support tickets.
Iteration #2 Objectives
Iteration #2 Objectives
With multi-size support in place, our next objective was
Close the confidence gap around sizing, especially for less experienced users, and
Streamline the workflow so users could select multiple options without manually entering width and height each time.
With multi-size support in place, our next objective was
Close the confidence gap around sizing, especially for less experienced users, and
Streamline the workflow so users could select multiple options without manually entering width and height each time.
Key business goals: Reduce post-upload drop-off, increase order value via multi-size, cut sizing support tickets.
Key business goals: Reduce post-upload drop-off, increase order value via multi-size, cut sizing support tickets.
Key business goals: Reduce post-upload drop-off, increase order value via multi-size, cut sizing support tickets.
Design challenge: one order → eight print sizes
Design challenge:
one order → eight print sizes
A crafter receives a single order with back + left-chest prints and mixed adult/youth/toddler counts.
A crafter receives a single order with back+ left-chest prints and mixed adult/youth/toddler counts.
Multiple placements
back + left chest
Mixed apparel & sizes
adult (L/M), youth, toddler
Quantities per size
12, 8, 6, 2
Result
8 different sizes
Multiple placements
back + left chest
Mixed apparel & sizes
adult (L/M), youth, toddler
Quantities per size
12, 8, 6, 2
Result
8 different sizes
Quantities per size
12 • 8 • 6 • 2
Multiple placements
back + left chest
Mixed apparel & sizes
adult (L/M), youth, toddler
Quantities per size
12, 8, 6, 2
Implications
8 different sizes
Quantities per size
12 • 8 • 6 • 2
Multiple placements
back + left chest
Mixed apparel & sizes
adult (L/M), youth, toddler
Quantities per size
12, 8, 6, 2
Implications
8 different sizes
Quantities per size
12 • 8 • 6 • 2
Current process diagram
Current process diagram
Current process diagram
Ideation & Strategic Direction
Ideation & Strategic Direction
Based on research revealing that users think in placement contexts while the system required dimensional inputs, I facilitated cross-functional workshops to reframe the challenge.
Based on research revealing that users think in placement contexts while the system required dimensional inputs, I facilitated cross-functional workshops to reframe the challenge.
Key design questions
Key design questions
Key design questions
How might we help users feel confident choosing sizes without thinking in dimensions?
How might we help users feel confident choosing sizes without thinking in dimensions?
How might we help users feel confident choosing sizes without thinking in dimensions?
How might we support multiple placements without overwhelming the interface?
How might we support multiple placements without overwhelming the interface?
How might we support multiple placements without overwhelming the interface?
How might we serve both beginners and experts in the same flow?
How might we serve both beginners and experts in the same flow?
How might we serve both beginners and experts in the same flow?
I led workshops with Product, Engineering, Customer Support, and Operations to generate solution approaches, then worked with PM to refine viable flows and identify technical constraints.
I led workshops with Product, Engineering, Customer Support, and Operations to generate solution approaches, then worked with PM to refine viable flows and identify technical constraints.
Exploring four distinct approaches to the sizing interface,
from manual dimension input to automated placement-based calculation.
Exploring four distinct approaches to the sizing interface, from manual dimension input to automated placement-based calculation.
Strategic direction: placement-driven smart sizing
Strategic direction: placement-driven smart sizing
Instead of trying to teach users about dimensions, we decided to automate the sizing logic based on both placement and apparel size.
Users choose placement first (Full Front vs. Left Chest) and size group (Adult, Youth, Kids), then the system generates appropriate dimensions automatically.
Instead of trying to teach users about dimensions, we decided to automate the sizing logic based on both placement and apparel size. Users choose placement first (Full Front vs. Left Chest) and size group (Adult, Youth, Kids), then the system generates appropriate dimensions automatically.
Why this approach?
Why this approach?
Research showed professionals think in placement-specific terms ("10x10 works for full front adult"), while beginners have no reference point. By letting the system translate placement + size intent into exact dimensions, we eliminate guesswork while maintaining accuracy.
Research showed professionals think in placement-specific terms ("10x10 works for full front adult"), while beginners have no reference point. By letting the system translate placement + size intent into exact dimensions, we eliminate guesswork while maintaining accuracy.
Key decisions
Key decisions
Contextual sizes
Contextual sizes
System calculates dimensions based on placement and size selection
System calculates dimensions based on placement and size selection
System calculates dimensions based on placement and size selection
Progressive disclosure
Progressive disclosure
Start simple, reveal complexity only when needed
Start simple, reveal complexity only when needed
Start simple, reveal complexity only when needed
Manual controls
Manual controls
Manual dimension input remains accessible for power users
Manual dimension input remains accessible for power users
Manual dimension input remains accessible for power users
This transforms a complex sizing decision into two simple choices (placement + size category), with automation handling the dimensional calculations.
This transforms a complex sizing decision into two simple choices (placement + size category), with automation handling the dimensional calculations.
Cross-functional collaboration & alignment
Cross-functional collaboration & alignment
Aligning stakeholders required navigating competing priorities - PMs wanted fast delivery to improve metrics, Engineering worried about sizing logic complexity, and Customer Support pushed for clearer terminology.
Aligning stakeholders required navigating competing priorities - PMs wanted fast delivery to improve metrics, Engineering worried about sizing logic complexity, and Customer Support pushed for clearer terminology.
They key conflict
They key conflict
Product Managers proposed removing visual placement elements entirely to accelerate launch, arguing text-based options would be simpler to implement.
Product Managers proposed removing visual placement elements entirely to accelerate launch, arguing text-based options would be simpler to implement.
My compromise strategy
My compromise strategy
I proposed a phased visual approach using Phase 1 user testing data. Since participants had specifically requested visual context ("I'd like to see the design on a t-shirt") and 60% of support tickets involved placement confusion, I argued for starting with simple t-shirt outline illustrations, then upgrading to full mockups with color options in a fast follow.
I proposed a phased visual approach using Phase 1 user testing data. Since participants had specifically requested visual context ("I'd like to see the design on a t-shirt") and 60% of support tickets involved placement confusion, I argued for starting with simple t-shirt outline illustrations, then upgrading to full mockups with color options in a fast follow.
The resolution
The resolution
The team agreed to launch with basic t-shirt silhouettes showing placement areas, then iterate to realistic mockups based on initial user feedback and usage data. This phased approach satisfied engineering's timeline concerns while preserving the visual guidance users needed.The realistic mockups became crucial to user confidence. Validation confirmed that visual context was essential for sizing decisions.
The team agreed to launch with basic t-shirt silhouettes showing placement areas, then iterate to realistic mockups based on initial user feedback and usage data. This phased approach satisfied engineering's timeline concerns while preserving the visual guidance users needed.The realistic mockups became crucial to user confidence. Validation confirmed that visual context was essential for sizing decisions.
Solution & Design
Solution & Design
Following usability testing and A/B validation with 10% of users, we launched the placement-driven interface to all customers.
Following usability testing and A/B validation with 10% of users, we launched the placement-driven interface to all customers.
Validation results
Validation results
Testing the placement-based sizing approach with both new users and existing Jiffy customers confirmed the solution addressed the confidence gap and workflow efficiency problems while delivering significant time savings across user segments.
Testing the placement-based sizing approach with both new users and existing Jiffy customers confirmed the solution addressed the confidence gap and workflow efficiency problems while delivering significant time savings across user segments.
Testing the placement-based sizing approach with both new users and existing Jiffy customers confirmed the solution addressed the confidence gap and workflow efficiency problems while delivering significant time savings across user segments.
96%
96%
Task Completion Rate
Task Completion Rate
20+ users: new & existing
customers
20+ users: new & existing customers
High
High
User Satisfaction
User Satisfaction
Strong preference over
current flow
Strong preference over current flow
"Oh my God I love this! I like that it gives you guidelines for sizes!
This will save tremendous amount of time for me."
"Oh my God I love this! I like that it gives you guidelines for sizes!
This will save tremendous amount of time for me."
Existing Jiffy customer
The is top of mind, very straightforward, and simple.
Super easy to select the sizes."
The is top of mind, very straightforward, and simple.
Super easy to select the sizes."
New user testing participant
The impact
The impact
The redesign delivered measurable improvements across user confidence, order speed, and overall conversion directly supporting business growth.
The redesign delivered measurable improvements across user confidence, order speed, and overall conversion directly supporting business growth.
↓ 62%
↓ 62%
↓ 62%
Drop-off Rate
Drop-off Rate
Drop-off Rate
56% → 21%
56% → 21%
56% → 21%
↓ 45%
↓ 45%
↓ 45%
New users
New users
New users
Conversation rate
Conversation rate
Conversation rate
↓ 34%
↓ 34%
↓ 34%
Support tickets
Support tickets
Support tickets
Sizing-related issues
Sizing-related
issues
Sizing-related issues
↓ 70%
↓ 70%
↓ 70%
Time to Cart
Time to Cart
Time to Cart
10min → 3min
10min → 3min
10min → 3min
Setting a new standard in the industry
Setting a new standard in the industry
One of our long-time users, a creator with over 1M subscribers on YouTube, featured the new experience in her video. Watch Youtube Video
One of our long-time users, a creator with over 1M subscribers on YouTube, featured the new experience in her video. Watch Youtube Video
Even beginners can now size with confidence!
Even beginners can now size with confidence!
Jennifer Maker
Within months, competitors began adopting similar flows, reinforcing that our solution had set a new UX benchmark in the space.
Within months, competitors began adopting similar flows, reinforcing that our solution had set a new UX benchmark in the space.
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© 2025 Anna Vasyukova
© 2025 Anna Vasyukova
© 2025 Anna Vasyukova