Optimising shopping cart experience

• Problem Discovery - choosing between two designs
• Key Decisions - the choices that shaped the research
• Insights & Outcomes - what the research revealed

Previous qualitative research identified problems with the existing shopping-cart experience and the design team developed two potential redesign directions.

Design A focused on simplicity by reducing visible information and distributing tasks across more screens. Design B increased information density and introduced comparison, filtering and control features. Both approaches aligned with recognised design principles, but those principles appeared to point in different directions.

The team needed to answer a practical product question: which direction was most likely to improve customer experience before committing development effort?

My role

I designed the research approach, balanced participant recruitment, shaped the experiment, selected the analysis method, interpreted findings and presented recommendations to stakeholders. The goal was not to prove a design was perfect - it was to reduce uncertainty and improve confidence in an upcoming product decision.

Use behavioural testing instead of preference polling

Rather than showing static screenshots and asking for opinions, I designed the study around interactive mobile prototypes. Participants completed realistic tasks before answering questions about their experience. This meant they evaluated the experience through use rather than speculation.

Balance the research cells before comparison

The study recruited 300 participants and split them into two groups of 150. The cells were balanced using behavioural and demographic characteristics so the comparison focused on differences between the designs rather than differences in participant composition. Confidence in the comparison depended on the groups being comparable.

Combine quantitative and qualitative evidence

Participants provided Likert-scale ratings alongside open-text responses explaining their reasoning. Statistical analysis identified whether differences were significant, while sentiment analysis and thematic review explained why those differences existed. Stakeholders received both numerical confidence and practical design insight.

Prioritise a fast, defensible decision

The work emerged from an urgent design question. A remote unmoderated study provided a practical way to gather evidence quickly without disrupting the broader research program. The team could make a development decision using customer evidence before implementation began.

What the research revealed

• Both redesign concepts were preferred over the current live experience
• Design B was preferred to a greater extent than Design A
• Design B scored higher for ease of use
• Statistical analysis found significant differences between the concepts
• Participants valued information visibility, comparison capability and filtering controls
• Participants reacted less positively to lower information density and additional navigation steps

What changed

• Provided a research-backed recommendation before development commenced
• Identified Design B as the stronger candidate for implementation
• Confirmed that both concepts improved on the existing experience
• Created evidence that aligned product, design and stakeholder decision-making
• Preserved useful elements from Design A for future consideration
• Validated findings using production outcomes after launch

Reflection

This project reinforced that qualitative and quantitative methods answer different questions. Qualitative research helped identify the design opportunities. Quantitative research helped estimate which option customers were more likely to prefer.

The most valuable outcome was not the statistical result itself. It was giving stakeholders enough confidence to move forward with a decision instead of continuing debate between two reasonable alternatives.