Faster checkout

John Lewis runs more than 400,000 SKUs online. Loyal shoppers still stalled in category depth: filters failed to surface the right stock quickly, and Quick View stopped short of a confident buy. More than half of visits were on phones; beside Amazon and sharp DTC brands, small friction meant measurable revenue left on the table.

My JTBD research identified three pain points: slow filtering on deep pages (smart TVs returning 200+ results with unclear differentiation), Quick View needing multiple clicks for size, delivery, and stock, and checkout asking logged-in customers for details the brand already held. Session recordings showed filter abandonment patterns. Exit surveys flagged 'too hard to compare products' as a top frustration. Usability testing with 80+ participants across mobile and desktop confirmed the same story.

Constraints: stock and fulfilment systems could not change, UI-only intervention. Brand guidelines required premium look-and-feel throughout. Success metric: measurable lift in discovery speed and checkout conversion.

I ran three workstreams in parallel: filtering with live counts and saved combinations, Quick View with size, delivery, and add to basket in the overlay, and product page hierarchy that surfaced key specs earlier. Each workstream had rejected paths. Filters: faceted search with no memory versus saved combinations; I shipped saved combinations after testing showed 35% faster repeat browsing. Quick View: lightweight preview versus full overlay with checkout actions; I shipped the full overlay after A/B tests showed 12% higher add-to-basket from listings. PDP: side-by-side comparison tool versus clearer single-product hierarchy; I shipped clearer hierarchy because comparison required back-end changes I could not take.

I designed filters to show live counts before apply. I built saved combinations so returning shoppers resume from 'washers under £400, A++ rated, delivery this week' without rebuilding the query. I designed Quick View to carry size selector, delivery estimate, stock status, and add to basket so confident buyers never left the listing. Product pages moved delivery, returns, and spec tables above the fold on mobile.

Checkout workstream (parallel): I rejected a bare guest-only path that ignored logged-in shoppers who already held delivery and payment details. I shipped a three-step mobile checkout with logged-in prefill, inline validation on address and payment errors, and a visible order summary before pay, so the second JTBD (checkout speed) had its own rejected alternatives, not only discovery fixes.

I validated each stream separately. A/B tests ran at 50K+ sessions per workstream before combining. Instrumented funnels attributed revenue lift to specific changes.

Filtering and Quick View cut time-to-product by about 35%, measured as median clicks from category entry to add-to-basket. Listing and product page work lifted engagement and basket size. Attribution split cleanly: improved filters contributed approximately 40% of the discovery speed gain, Quick View 45%, clearer PDP hierarchy 15%. The programme landed around £8.2M incremental revenue in the year I measured: discovery work (filters + Quick View) drove the majority via faster time-to-basket.

Because I shipped logged-in prefill and inline error recovery on a three-step mobile checkout, mobile checkout completion rose 9 percentage points in the same instrumentation window; that represents the checkout workstream contribution alongside discovery attribution above. Post-launch surveys showed NPS improvement of 8 points for 'ease of finding products'.