Mountain Warehouse PDP Research
Turning the tables on the product display page
Following a redesign of a high-traffic product page, performance declined but the causes were unclear. I led a mixed-method research programme to understand user decision-making, identify barriers to confidence, and align stakeholders around evidence-based improvements. While rooted in e-commerce, this work focused on reducing user uncertainty, improving information clarity, and supporting better decisions challenges directly transferable to complex public service journeys.
Company
Mountain Warehouse
Platform
Mobile and Desktop Website
My role
UX Research
Timeline
2 months
Why this matters
Users navigating transport systems, like customers choosing products, must make decisions with incomplete information.
This project demonstrates my ability to:
Reduce decision friction in high-stakes journeys
Design for clarity, accessibility, and confidence
Align multiple stakeholders around user needs
Turn research into measurable service improvements
Problem
Users lacked confidence when making purchase decisions on the redesigned PDP. The experience did not effectively support users in, finding key information, interpreting product details and trusting their purchase decisions. This resulted in increased friction and potential loss in conversion.
My Role
Led end-to-end research strategy across multiple phases
Conducted qualitative and quantitative research
Defined success metrics across user and business needs
Influenced product direction through insight synthesis
Facilitated stakeholder alignment across teams
Embedded a test-and-learn culture through experimentation
Approach
Phase 1: Discovery & Alignment
Stakeholder Interviews & Assumption Unpacking (8 Participants)
Literature review
Competitive analysis
Remote card sorting (78 responses)
Data Analytics
Goal: Align on assumptions, understand business needs, and define how users expect information to be structured.
Phase 2: Behaviour & Usability
1-to-1 semi structured user interviews (5 participants)
Think-out-loud usability testing (7 participants)
Goal: Understand decision-making behaviour and identify friction in the current PDP experience.
Sample of affinity map
Three Key Insights
Across these phases, the research generated 20 distinct insights. From these, three key themes emerged that had the greatest impact on user decision-making and overall PDP performance. These insights highlight the primary barriers to purchase and informed the direction of design improvements.
Size & Fit Confidence is the Primary Barrier to Purchase
What we saw
Insight
Users lacked confidence in selecting the correct size. Especially new customers and in categories like women’s, kids, and footwear.
Impact
Hesitation to purchase or incorrect purchases → increased returns
Opportunity
Provide clearer, contextually relevant size guidance at the point of decision
Model Imagery is Critical for Assessing Fit and Driving Purchase Confidence
What we saw
Insight
Users depend on model imagery to assess fit, making it a critical factor in purchase confidence. Representation matters users are more confident when they can see products on models similar to themselves.
Impact
Lack of relatable imagery reduces confidence and conversion
Opportunity
Prioritise diverse, fit-focused imagery to help users visualise products
Lack Comparison Tools Forces Users into Workarounds
What we saw
Insight
The PDP does not adequately support product comparison, leading users to create their own workarounds. This indicates a clear unmet need for a more efficient and structured way to compare products.
Impact
Higher cognitive load, slower decisions, drop-offs
Opportunity
Introduce better comparison tools or streamline cross-product evaluation
Previous size guide design
Deep Dive: Improving Size & Fit Confidence
To address the lack of confidence in size selection, I tested iterative improvements to the size guide and selection experience using remote Figma prototype usability testing (46 participants).
Benchmark (current experience)
Task completion: 63%
Error rate: 3.4 per user
SEQ: 4.6/7
Iteration 1 - Improving clarity and relevance
What changed
Repositioned size guide CTA before the point of decision
Introduced gender and category-specific size guidance
Simplified table structure to improve readability (reducing need to scan across large matrices)
Outcome
Task completion: 84% (+21%)
Error rate: 2.3 per user (-1.1)
SEQ: 5.5/7 (+0.9)
Iteration 2 - Reducing complexity and cognitive load
What changed
Redesigned size guidance into a single, simplified one fold view
Introduced cm/inch toggle to support generational and geographical user preferences
Outcome
Task completion: 88% (+25%)
Error rate: 0.6 per user (-2.8)
SEQ: 6.1/7 (+1.5)
Through iterative testing, the size selection experience evolved from a complex, table-heavy interaction into a clearer, more accessible decision-making tool—directly improving user confidence and reducing friction at a critical point in the purchase journey
Improved size guide design
Impact
Business Impact (A/B Testing – 88,000 users)
+4.3% Add-to-Basket rate
+4.6% Revenue per Visitor
+9.22% engagement with size guidance
User Impact
Increased confidence in size selection
Reduced errors and backtracking
Faster, more confident decisions
Organisational Impact
Shift from assumption-led to insight-driven decisions
Alignment across product, design, and stakeholders
Established a test-and-learn experimentation culture
Outcomes
This research generated 20 validated insights that informed a structured experimentation pipeline.
This led to:
24 positive and 8 inconclusive A/B tests
Consistent improvements in conversion and engagement
A clearer understanding of key purchase drivers
What I Learned
This project shifted my focus from usability to confidence. Users often had the information they needed, but lacked the confidence to act on it.
I learned that small, well-timed improvements at key decision points can have a significant impact on both user experience and performance. Rather than aiming for a single “perfect” solution, iterative testing and validation proved far more effective in reducing uncertainty and improving outcomes.
It also reinforced the importance of early stakeholder alignment—ensuring insights are understood and actionable is key to turning research into real impact.
What I’d Do in a Transport Context
I’d apply the same research-led, iterative approach to help make journey planning and decision-making clearer, more accessible, and more reliable for a wide range of users.
In practice, that means:
Improving confidence in journey planning and ticket selection by ensuring users have the right information, at the right time, to make informed decisions without hesitation
Reducing friction across complex, multi-modal journeys by simplifying how users compare routes, modes, and options
Supporting users during disruption and uncertainty, where clear, timely, and trustworthy information becomes critical
Designing for accessibility from the outset, aligning with WCAG standards to ensure services are inclusive for all users
Using data and research to continuously measure and improve outcomes, focusing on real user behaviour and service performance
More broadly, I’m interested in how UX can help shape behaviour making sustainable choices like public transport, walking, and cycling feel easier, more intuitive, and more appealing through well-designed digital experiences.