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Checkout Research: Why Customers Abandon Cart, and What It Takes to Keep Them
Officeworks' desktop checkout converted above industry standard, but mobile conversion lagged and checkout took over 4x longer than the 1-minute benchmark customers expect. I led a mixed-methods research programme, an audit spanning 6 methods, 613 NPS responses, and 10 user interviews, to find out why.
The research surfaced three connected stories: customers need to feel confident before they can be efficient, cart abandonment has specific and fixable causes (hidden charges, security doubts, and slow load times ranked highest), and Officeworks' mobile checkout was underperforming its own desktop experience.
I translated these findings into 6 prioritized recommendations, including surfacing total cost upfront, making the mini cart editable, and redesigning for adaptive mobile checkout, directly shaping the roadmap for Officeworks' checkout redesign.
Not every fix moved at the same speed. Availability and delivery info couldn't be resolved inside checkout itself, the system was too rigid, so I surfaced it earlier, in the PDP and mini cart, with delivery cost and free shipping threshold next in line. Express checkout was...
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How do you keep a brand's promise of calm intact once real users start clicking through checkout?
Mudita builds minimalist devices around one idea: technology that harmonizes with your life instead of competing for your attention. The brandbook lays that promise out in detail - tone, restraint, what the brand should never feel like. My job was carrying that same discipline into the product itself.
I worked across the checkout flow, the storefront and category browsing, and the product page above the fold - three surfaces where small frictions had quietly built up against the brand's own case for calm. The brandbook gave the rules. The UI work was making sure those rules survived contact with a real cart and a real user.
For the case study visuals, I used AI for the first time to produce product footage 🤖 - wind moving through a scene, an object drifting in place. Higgsfield was genuinely a good way to learn that workflow fast.
Full case study - actual before/after breakdowns - is up on Behance 🔗, linked below.
Genuinely curious where people land on this: generating production assets with AI is fast, and honestly kind of fun as a workflow - but there's real pushback that it's taking work away from people who used to shoot this stuff. Where do you fall on that?
The checkout flow detail is what stands out to me, that's usually where a brand's calm promise turns into three different button behaviors fighting each other. On your AI question, I think it matters less whether it's fast and more who's checking the output against the brand's...
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