Locate in Store: bridging online browsing with in-store stock

H&M was betting that online and offline shopping needed to merge, but customers couldn't trust that items shown online were actually in stock in their local store. Leading research across the US, China, and Sweden, I traced the "can't find products" complaint back to its real root cause: the in-store inventory system only updated once or twice a day. Rather than shipping a map on top of stale data, I paired an interactive in-store map with store bots that scanned shelves in real time, treating accurate data as a precondition for the map being trustworthy.

• 15% increase in inventory location accuracy within two months.
• About 12% increase in cart conversion on located products, and a 25% reduction in product search time.
• 40% cut in pilot-to-deployment time via the experimentation frameworks built alongside it.

H&M was proactively experimenting with a way to bridge online and offline shopping, betting that the boundary between the two channels was about to matter a lot more. 'Locate in Store' started as that bet: could customers move seamlessly between their preferred H&M store and the online platform, and easily find what they wanted once they got there.

The bet was that the boundary between channels was about to matter far more. Locate in Store started there: could customers move seamlessly between their preferred H&M store and the online platform, and easily find what they wanted once they arrived?

Research across the US, China, and Sweden confirmed the bet was right. In Sweden, 15% of customers who mentioned the store described it as "messy," and 6% said products were hard to find. In China, feedback was harsher: stores were described as feeling more like a warehouse than a fashion store. But the deeper issue, surfaced through staff interviews, was that the in-store inventory system itself only updated once or twice a day. Any navigation feature built on top of stale data would confidently point customers toward stock that might already be gone.

I led customer research across the US, China, and Sweden, interviewing both customers and store staff. That research is what surfaced the real root cause. Staff didn't describe the map as the missing piece; they described the inventory system itself as outdated. That reframing, from "customers need a map" to "the underlying data needs to be real-time first," is what shaped the final solution.

Three approaches were piloted in real stores, each addressing a piece of the problem, with limited time to ship something that fixed the root cause rather than adding a nicer interface on top of the same outdated system:

• RFID tags for product tracking.
• Smart cameras for shelf monitoring.
• Autonomous store bots that could scan aisles and assist customers.

The obvious framing was "help people find a product in the store." I reframed it: the real problem was a broken handoff, where customers built intent online, then lost that context at the door. So rather than start at the map (the visible symptom), I worked the journey in order, surfacing availability upstream, making intent explicit, making the assortment store-specific, and only then handing off to in-store wayfinding.

Before committing to one model, I prototyped three MVP concepts, each testing a different way a customer might start the "find it in store" journey: searching for it, saving it, or being routed to it fastest. Testing the entry point as competing concepts is how I de-risked the navigation model before build.

• Store bots scanning shelves in real time, which drove the 15% inventory accuracy increase within the first two months, directly solving the staleness problem staff had flagged.
• An interactive map built on that now-accurate data, which drove the roughly 12% increase in cart conversion on located products and a 25% reduction in overall product search time.
• A/B testing and painted-door tests post-launch, which validated real usage patterns and surfaced further refinements.
• Experimentation metrics and decision frameworks built alongside it, which cut pilot-to-deployment time by 40%, letting the team assess and scale high-potential retail concepts faster.

The headline numbers were only trustworthy because success was defined before launch and measured across several dimensions, not a single vanity metric.

Shipping the pilot was the start, not the finish. Three threads carried the work forward.

Locate in Store is evidence of what a deliberately planned UX strategy can do: pairing customer insight with the right technology to reshape the retail experience around personalisation, efficiency, and ease. As the UX Strategist, my role was the connective tissue, tying the technology to what customers actually needed so every part of the project held together as one coherent experience rather than a set of features.