DISCOVER PROJECT
DISCOVER PROJECT
DISCOVER PROJECT
004/e-commerce
PEDLL
Why is finding the right bike so damn hard?
How I reimagined e-commerce for how cyclists actually want to shop
July 2025 (4 weeks)
You've finally decided to buy that dream bike. You've got $3,000 burning a hole in your pocket and months of research ahead of you.
You're comparing "aggressive endurance geometry" with "race-inspired compliance" across twelve different sites. One lists stack height in millimeters, another doesn't mention it at all. You've got seventeen browser tabs open, a spreadsheet tracking chainstay lengths, and you're googling "what does bottom bracket drop actually mean" for the third time this week.
This was the reality I discovered when I started researching cycling e-commerce. Despite cyclists being some of the most research-obsessed shoppers on the planet, some platforms treated bike buying like you were purchasing a pair of socks.
You've finally decided to buy that dream bike. You've got $3,000 burning a hole in your pocket and months of research ahead of you.
You're comparing "aggressive endurance geometry" with "race-inspired compliance" across twelve different sites. One lists stack height in millimeters, another doesn't mention it at all. You've got seventeen browser tabs open, a spreadsheet tracking chainstay lengths, and you're googling "what does bottom bracket drop actually mean" for the third time this week.
This was the reality I discovered when I started researching cycling e-commerce. Despite cyclists being some of the most research-obsessed shoppers on the planet, some platforms treated bike buying like you were purchasing a pair of socks.
SERVICES
springboard capstone PROJECT, UX DESIGN, INTERACTION DESIGN, UI/VISUAL DESIGN, DESIGN RESEARCH, DISCOVERY, IDENTITY BRANDING, web DESIGN
springboard capstone PROJECT, UX DESIGN, INTERACTION DESIGN, UI/VISUAL DESIGN, DESIGN RESEARCH, DISCOVERY, IDENTITY BRANDING, web DESIGN
Project Overview
The Core Problem
Cyclists spend hours researching technical specs across multiple sites, abandoning carts to cross-reference data, and often feeling like platforms don't understand the complexity of their purchase decisions.
The Core Problem
Cyclists spend hours researching technical specs across multiple sites, abandoning carts to cross-reference data, and often feeling like platforms don't understand the complexity of their purchase decisions.
Challenge
Design an e-commerce platform that serves both casual cyclists and technical expertsSolution
Adaptive interface with centralized specs, intelligent comparison tools, and persistent research features
Key Innovation
AI-powered persona testing methodology for rapid user validation
Challenge
Design an e-commerce platform that serves both casual cyclists and technical expertsSolution
Adaptive interface with centralized specs, intelligent comparison tools, and persistent research features
Key Innovation
AI-powered persona testing methodology for rapid user validation
Approach - personas
persona images
Understanding the User Spectrum
I needed to design for an incredibly diverse user base—from weekend riders intimidated by technical jargon to performance cyclists who can spot geometry errors across multiple websites.
The Cast of Characters:
Ethan the Analyzer spends weeks comparing frame geometry, building spreadsheets that track everything from head tube angles to tire clearances. Current sites make him hunt across PDFs and buried spec sheets.
Liam the Weekend Warrior just wants a reliable bike for Saturday rides but gets overwhelmed when sites throw "chainstay stiffness" and "bottom bracket drop" at him without explanation.
Ava the Gift-Giver is trying to buy her partner the perfect bike but can't tell if "endurance geometry" means comfortable or fast. She needs guidance, not intimidation.
Noah the Urban Explorer needs practical city commuting that also looks good and performs well, balancing style with functionality for daily use.
Oliver the Return Browser expects platforms to remember his preferences and research from previous visits—starting from scratch every time drives him crazy.
Understanding the User Spectrum
I needed to design for an incredibly diverse user base—from weekend riders intimidated by technical jargon to performance cyclists who can spot geometry errors across multiple websites.
The Cast of Characters:
Ethan the Analyzer spends weeks comparing frame geometry, building spreadsheets that track everything from head tube angles to tire clearances. Current sites make him hunt across PDFs and buried spec sheets.
Liam the Weekend Warrior just wants a reliable bike for Saturday rides but gets overwhelmed when sites throw "chainstay stiffness" and "bottom bracket drop" at him without explanation.
Ava the Gift-Giver is trying to buy her partner the perfect bike but can't tell if "endurance geometry" means comfortable or fast. She needs guidance, not intimidation.
Noah the Urban Explorer needs practical city commuting that also looks good and performs well, balancing style with functionality for daily use.
Oliver the Return Browser expects platforms to remember his preferences and research from previous visits—starting from scratch every time drives him crazy.
research & discovery
secondary research
What I Discovered
Current cycling sites excel at gorgeous photography but often struggle when users need to evaluate and compare products:
Technical specs scattered across tabs, buried in PDFs, or missing entirely
No guided discovery for casual buyers overwhelmed by jargon
Platforms forget preferences and make users restart research every visit
No meaningful comparison tools for side-by-side technical analysis
The Core Insight
Cycling purchases aren't impulse buys—they're extended research projects requiring platforms that support deep evaluation, not just pretty product pages.
The Opportunity
Create adaptive experiences that serve both casual browsers and technical experts without compromising either journey.
What I Discovered
Current cycling sites excel at gorgeous photography but often struggle when users need to evaluate and compare products:
Technical specs scattered across tabs, buried in PDFs, or missing entirely
No guided discovery for casual buyers overwhelmed by jargon
Platforms forget preferences and make users restart research every visit
No meaningful comparison tools for side-by-side technical analysis
The Core Insight
Cycling purchases aren't impulse buys—they're extended research projects requiring platforms that support deep evaluation, not just pretty product pages.
The Opportunity
Create adaptive experiences that serve both casual browsers and technical experts without compromising either journey.
research & discovery
user flow example
My Research Innovation:
AI-Powered User Testing
As a student without access to dozens of cyclists for traditional user testing, I needed to get creative about validation. While I knew a couple casual cycling acquaintances, they weren't available for the kind of comprehensive interviews and testing sessions a project like this would normally require. This constraint forced me to develop an approach that ended up becoming one of the most interesting parts of the project.
Here's how I made it work: I started by mapping out the key user flows—product discovery, comparison, purchase decisions—then built detailed behavioral profiles for each persona. Not just demographics, but how they actually shop: Ethan's spreadsheet obsession, Liam's anxiety around technical terms, Ava's need for confidence-building guidance.
The breakthrough came when I realized I could have AI actually walk through my designs as each persona. I'd set up a scenario: "You're Ethan trying to compare three road bikes. Navigate this interface and tell me exactly where you get stuck."
My Research Innovation:
AI-Powered User Testing
As a student without access to dozens of cyclists for traditional user testing, I needed to get creative about validation. While I knew a couple casual cycling acquaintances, they weren't available for the kind of comprehensive interviews and testing sessions a project like this would normally require. This constraint forced me to develop an approach that ended up becoming one of the most interesting parts of the project.
Here's how I made it work: I started by mapping out the key user flows—product discovery, comparison, purchase decisions—then built detailed behavioral profiles for each persona. Not just demographics, but how they actually shop: Ethan's spreadsheet obsession, Liam's anxiety around technical terms, Ava's need for confidence-building guidance.
The breakthrough came when I realized I could have AI actually walk through my designs as each persona. I'd set up a scenario: "You're Ethan trying to compare three road bikes. Navigate this interface and tell me exactly where you get stuck."
The feedback was surprisingly specific
The feedback was surprisingly specific
The feedback was surprisingly specific
"Ethan abandoned the comparison after 3 minutes because geometry specs were scattered across different tabs—he had to screenshot data just to compare it side by side." Or: "Liam bounced from the filter page in 30 seconds. All those technical terms without explanations made him feel stupid." This let me rapidly test the same design across all five personas and catch usability issues I never would have spotted on my own.
To make sure I wasn't just getting clever AI responses, I cross-checked the feedback against UX principles I knew and casually tested key insights with cycling acquaintances. When the AI said something would frustrate users, real cyclists confirmed it—that's when I knew this approach was actually working.
"Ethan abandoned the comparison after 3 minutes because geometry specs were scattered across different tabs—he had to screenshot data just to compare it side by side." Or: "Liam bounced from the filter page in 30 seconds. All those technical terms without explanations made him feel stupid." This let me rapidly test the same design across all five personas and catch usability issues I never would have spotted on my own.
To make sure I wasn't just getting clever AI responses, I cross-checked the feedback against UX principles I knew and casually tested key insights with cycling acquaintances. When the AI said something would frustrate users, real cyclists confirmed it—that's when I knew this approach was actually working.
my solution
Pedll—a conceptual e-commerce platform built specifically for how cyclists actually research and buy gear, balancing technical depth with intuitive guidance. Pedll treats bike buying like the complex research project it actually is, serving both weekend riders and gear obsessives without compromising either experience.
Pedll—a conceptual e-commerce platform built specifically for how cyclists actually research and buy gear, balancing technical depth with intuitive guidance. Pedll treats bike buying like the complex research project it actually is, serving both weekend riders and gear obsessives without compromising either experience.
Core Features
Centralized Technical Hub
All frame geometry charts and component specs in one standardized, searchable database. No more hunting across seventeen tabs to find tire clearance measurements.
Intelligent Comparison Engine
Side-by-side analysis with explanations: "This bike's longer wheelbase provides stability on long rides but less agility in tight corners."
Adaptive Interface
Progressive disclosure that starts simple and reveals complexity based on user behavior. Casual browsers see riding-style categories while technical users access detailed specifications immediately.
Persistent Research Environment
Saved comparisons and notes that survive across sessions, because nobody makes a $3,000 decision in one sitting.
Core Features
Centralized Technical Hub
All frame geometry charts and component specs in one standardized, searchable database. No more hunting across seventeen tabs to find tire clearance measurements.
Intelligent Comparison Engine
Side-by-side analysis with explanations: "This bike's longer wheelbase provides stability on long rides but less agility in tight corners."
Adaptive Interface
Progressive disclosure that starts simple and reveals complexity based on user behavior. Casual browsers see riding-style categories while technical users access detailed specifications immediately.
Persistent Research Environment
Saved comparisons and notes that survive across sessions, because nobody makes a $3,000 decision in one sitting.
The Result:
Ethan gets comprehensive data tools, Liam gets guided discovery, Ava gets confidence-building education, and Oliver gets seamless research continuation—all in one cohesive experience.
The Result:
Ethan gets comprehensive data tools, Liam gets guided discovery, Ava gets confidence-building education, and Oliver gets seamless research continuation—all in one cohesive experience.
design process
4-Week Process Overview
4-Week Process Overview
4-Week Process Overview
Week 1: User research and persona development
Week 1: User research and persona development
Week 1: User research and persona development
Week 2: Competitive analysis and initial concept development
Week 2: Competitive analysis and initial concept development
Week 2: Competitive analysis and initial concept development
Week 3: Prototyping and AI testing methodology implementation
Week 3: Prototyping and AI testing methodology implementation
Week 3: Prototyping and AI testing methodology implementation
Week 4: Design iteration and refinement based on testing insights
Week 4: Design iteration and refinement based on testing insights
Week 4: Design iteration and refinement based on testing insights
comparisons problem
The Challenge
My AI testing revealed a fundamental tension. Ethan needed comprehensive technical data to make decisions, while Liam got overwhelmed by the same information. How do you serve both without compromising either experience?
I was wrestling with this problem when I realized I was approaching it wrong. Instead of hiding or revealing technical specs, what if I could translate them into something universally understandable?
The Challenge
My AI testing revealed a fundamental tension. Ethan needed comprehensive technical data to make decisions, while Liam got overwhelmed by the same information. How do you serve both without compromising either experience?
I was wrestling with this problem when I realized I was approaching it wrong. Instead of hiding or revealing technical specs, what if I could translate them into something universally understandable?
The Breakthrough
The inspiration came from an unexpected place—racing video games. Every time you select a car in games like Gran Turismo or Forza, you see those familiar stat bars: Speed, Acceleration, Handling, Braking. Players instantly understand what a car optimized for speed versus handling looks like, even without knowing the technical specs behind those ratings.
The Solution
I designed a series of graphic bars showing how each bike compared across key dimensions: Performance, Comfort, Weight, Durability, Tech, and Value. Casual users like Liam could immediately grasp that one bike prioritized comfort over performance. Meanwhile, technical users like Ethan could still drill down into the detailed specs that informed those ratings.
The Breakthrough
The inspiration came from an unexpected place—racing video games. Every time you select a car in games like Gran Turismo or Forza, you see those familiar stat bars: Speed, Acceleration, Handling, Braking. Players instantly understand what a car optimized for speed versus handling looks like, even without knowing the technical specs behind those ratings.
The Solution
I designed a series of graphic bars showing how each bike compared across key dimensions: Performance, Comfort, Weight, Durability, Tech, and Value. Casual users like Liam could immediately grasp that one bike prioritized comfort over performance. Meanwhile, technical users like Ethan could still drill down into the detailed specs that informed those ratings.
Testing the Concept
When I ran this through my AI testing, the results were clear. "Liam immediately understood the trade-offs between bikes without getting lost in geometry charts." "Ethan used the visual comparison to quickly narrow his options, then dove into the technical details for his final three choices."
Testing the Concept
When I ran this through my AI testing, the results were clear. "Liam immediately understood the trade-offs between bikes without getting lost in geometry charts." "Ethan used the visual comparison to quickly narrow his options, then dove into the technical details for his final three choices."
Testing the Concept
When I ran this through my AI testing, the results were clear. "Liam immediately understood the trade-offs between bikes without getting lost in geometry charts." "Ethan used the visual comparison to quickly narrow his options, then dove into the technical details for his final three choices."
The gaming-inspired interface became a bridge between complexity and clarity—giving everyone the level of information they needed to make confident decisions.
The gaming-inspired interface became a bridge between complexity and clarity—giving everyone the level of information they needed to make confident decisions.
visual design decisions
Visual Identity
Deep blues and grays suggesting technical precision, with strategic red accents for key actions. Typography that works equally well for technical spec sheets and engaging product stories. Grid-based layouts that feel both credible and navigable.
The Result
An interface that appeals to gear nerds without intimidating newcomers—technical depth wrapped in genuine usability.
Visual Identity
Deep blues and grays suggesting technical precision, with strategic red accents for key actions. Typography that works equally well for technical spec sheets and engaging product stories. Grid-based layouts that feel both credible and navigable.
The Result
An interface that appeals to gear nerds without intimidating newcomers—technical depth wrapped in genuine usability.
Product Images
"Sometimes the most valuable part of a student project isn't the final design—it's the creative problem-solving that constraints force you to develop."
— Adam Winegar
what i learned
This project taught me that limitations often force better solutions than unlimited resources would. Without access to traditional user research, I developed a testing methodology that was actually more efficient and iterative than conventional approaches.
This project taught me that limitations often force better solutions than unlimited resources would. Without access to traditional user research, I developed a testing methodology that was actually more efficient and iterative than conventional approaches.
The bigger revelation
Designing for technical complexity isn't about dumbing things down—it's about making sophisticated information genuinely accessible. Expert users need comprehensive tools, but the path to that complexity should welcome rather than intimidate newcomers.
The Innovation Lesson
Sometimes the most valuable part of a student project isn't the final design—it's the creative problem-solving that constraints force you to develop. The AI testing methodology could benefit other designers facing similar research limitations.
The bigger revelation
Designing for technical complexity isn't about dumbing things down—it's about making sophisticated information genuinely accessible. Expert users need comprehensive tools, but the path to that complexity should welcome rather than intimidate newcomers.
The Innovation Lesson
Sometimes the most valuable part of a student project isn't the final design—it's the creative problem-solving that constraints force you to develop. The AI testing methodology could benefit other designers facing similar research limitations.
Looking Forward
Pedll demonstrates how e-commerce can better serve specialized communities through thoughtful information architecture and adaptive interfaces. The foundation is solid for exploring how technical platforms can balance depth with approachability.
This project proved that student work can drive genuine innovation in both methodology and design—sometimes the best solutions emerge when traditional approaches aren't available.
Looking Forward
Pedll demonstrates how e-commerce can better serve specialized communities through thoughtful information architecture and adaptive interfaces. The foundation is solid for exploring how technical platforms can balance depth with approachability.
This project proved that student work can drive genuine innovation in both methodology and design—sometimes the best solutions emerge when traditional approaches aren't available.
Looking Forward
Pedll demonstrates how e-commerce can better serve specialized communities through thoughtful information architecture and adaptive interfaces. The foundation is solid for exploring how technical platforms can balance depth with approachability.
This project proved that student work can drive genuine innovation in both methodology and design—sometimes the best solutions emerge when traditional approaches aren't available.
project achievements
Methodological Innovation
Methodological Innovation
Methodological Innovation
Design Success
Design Success
Design Success
User Experience Success
User Experience Success
User Experience Success
