Founded in San Jose, California, in 1993 by the Mersho family, Shoe Palace has grown from a single storefront into one of the country's leading sneaker and streetwear retailers. Today it operates more than 250 stores across the western and southern United States, with deep ties to sneaker culture and longstanding partnerships with brands such as Nike, Jordan, and Adidas. That growth meant scaling a store fleet across markedly different environments — from dense mall locations to standalone street stores — each with its own traffic patterns and staffing needs.
The Challenge
For years, Shoe Palace ran its stores without a reliable way to measure performance relative to demand. Sales numbers told the team what had happened, but not why — and not whether a slow day was actually slow. Without traffic data, conversations about store performance ran on instinct. Assumptions about peak hours, staffing levels, and store-to-store comparisons had no baseline to check against.
"It's hard to imagine doing this business without having traffic. But the reality is there are a lot of retailers that don't have it. At one time, we were that retailer."
— David Lizak, Platform Manager, Shoe Palace
As Lizak puts it, running a store without traffic data is like driving a car with no speedometer: you can feel like you're going fast, but you have no way to confirm it.
The Solution
Shoe Palace began rolling out RetailNext traffic solutions in 2018. By the time David Lizak joined in 2019, the challenge had shifted from adoption to education, and the team built its program on three principles.
Keep it simple. Rather than overwhelm store teams with complex metrics, Shoe Palace translated traffic data into plain, actionable terms. Instead of asking a part-time associate to calculate conversion percentages, the team reframed targets around something staff already understood: serve one more customer than yesterday.
Build a shared language. Shoe Palace developed a staff-to-traffic ratio it nicknamed TPLH — traffic per labor hour — that gives every store manager an intuitive read on whether a shift is appropriately staffed. The ratio turns an abstract number into a question any associate can answer: can you and I handle this many customers in an hour?
Normalize across a diverse fleet. With stores spread across California, Texas, Arizona, Nevada, and Florida, no two locations look alike. TPLH lets Shoe Palace compare performance fairly, adjusting for mall versus street formats rather than holding every store to the same standard.
Every number behind those staffing decisions starts with one device. Aurora®, RetailNext's all-in-one IoT sensor, delivers 95 to 99% accurate people counting — manually audited at every installation — from a single ceiling-mounted unit. For a fleet spanning multiple states and store formats, that accuracy is what makes labor planning trustworthy at scale.
The Results
By its second year of tracking and training to the data, Shoe Palace had achieved a 10% average lift in conversion year over year. One store moved from 15% to nearly 17% conversion in that window.
Smarter labor planning. TPLH gave the operations team a healthy staffing benchmark that flexes by store type. Showing teams precisely where their peaks fell — and how much those peaks varied store to store — gave managers the specificity to coach to it, and gave the business aggregate data for fleet-wide planning.
Fair store comparisons. Two stores a few miles apart, like Shoe Palace's original San Jose location and a nearby mall store, can look nothing alike in traffic pattern or demographic. RetailNext data lets the team compare similar store types to each other rather than forcing an apples-to-oranges read on performance.
Better cross-functional decisions. Marketing and product teams no longer rely solely on sales-based assumptions about where demand is highest. Traffic and demographic data now inform inventory allocation and regional strategy.
Assumptions challenged with historical data. Where managers assumed they knew their store's rhythm, peak hours, and staffing needs, Shoe Palace started checking those assumptions against historical traffic. The result: missed opportunities during high-traffic windows that had gone unnoticed for years.
Shoe Palace now combines RetailNext traffic data with workforce-planning platforms such as StoreForce, bringing TPLH, sales per hour, and square footage into a single view. That integration helps the team pinpoint power hours and peak periods store by store — something that was invisible before traffic data existed.
