Zainab Hussain joins us to discuss the seismic shift currently reshaping the brick-and-mortar landscape. As an e-commerce strategist with deep roots in operations management and customer engagement, she has watched the industry move away from the “bigger is better” mantra toward a model defined by precision and agility. With retail formats shrinking and the competition for prime real estate intensifying, the data used to select these sites has never been more critical. In this conversation, we explore how real-world mobility data is replacing traditional demographics as the cornerstone of retail strategy, ensuring that even the smallest storefronts can maximize their impact by being in exactly the right place at exactly the right time.
Many retailers are shifting from full-size locations to smaller formats ranging from 12,000 to 15,000 square feet. How does this footprint reduction change the margin for error in site selection, and what specific mobility metrics should leaders prioritize to ensure these condensed stores remain accessible?
When you move from a traditional full-size store, which might sprawl up to 25,000 square feet, down to a tighter 12,000 to 15,000 square foot footprint, you are essentially losing your status as a “destination” store. In those larger formats, shoppers might be willing to navigate a confusing intersection or drive an extra mile because they know you have everything they need under one roof. With a smaller format, like what we are seeing with Best Buy’s recent expansion into regions where full-size stores simply don’t fit, convenience becomes the primary product you are selling. Leaders have to prioritize vehicle activity and specific travel directions because a small-format store lives or dies by how easily a customer can pivot into the parking lot during their daily routine. You need to look beyond a simple dot on a map and analyze the route and directionality of traffic to ensure your front door is on the path of least resistance. If a customer has to cross three lanes of heavy traffic to reach a 12,000-square-foot shop, they will simply keep driving to a competitor who is easier to access.
While many businesses rely on annual average traffic counts, these figures often fail to capture morning versus evening commute patterns. How can analyzing “hour of the day” mobility data prevent costly real estate blunders, and what does this reveal about the “catchment area” of a potential site?
Relying on an annual average traffic count is like trying to dress for the weather based on a yearly average temperature; you’ll be completely unprepared for the actual conditions on the ground. For a retailer, especially those in the quick-service restaurant or coffee sectors, the “hour of the day” data is the only metric that truly reflects profitability. I have seen instances where a business made the catastrophic mistake of placing an outlet on the wrong side of a divided road, failing to realize that their peak volume was actually the dinner-time rush on the opposite side of the highway. That specific business ended up having to tear down that location and move it half a mile away just to capture the evening traffic flow. By using mobility data to power 15-minute drive-time polygons, we can see exactly who can realistically reach the store during those high-value windows. This allows us to overlay census data onto a specific time-of-day “catchment area” to see if our target demographic is actually passing the site when they are in the mood to shop, rather than just seeing a blur of cars on an annual report.
As retailers increasingly focus on “forecourt” locations and gas-adjacent sites, how can they use trip origin data to ensure they are capturing the right demographic rather than just a high volume of random passersby?
The shift toward gas and forecourt locations, a strategy we see a large Midwest supermarket chain aggressively pursuing right now, requires a much more granular understanding of where drivers are coming from and where they are going. It isn’t enough to know that 50,000 cars pass a pump every day; you need to know if those drivers are coming from the high-income residential neighborhoods your grocery brand serves. Trip data allows us to trace the “why” behind the movement, helping us determine if a potential site sits on a primary artery for our target demographic’s commute. When you can see the starting point of a journey, you can match that movement to specific census tracts, ensuring that the people passing by your new small-format store have the purchasing power and brand affinity you need. This turns a high-traffic road from a generic statistic into a specialized pipeline of potential customers who are already primed to engage with your brand. By understanding these physical-world behaviors, a retailer can confidently build a smaller store in a high-cost area, knowing the “catchment area” is filled with the exact people they want to reach.
What is your forecast for the evolution of retail site selection as mobility data becomes more integrated into real estate strategy?
I expect that by the end of this decade, the very concept of a “static” site selection process will be obsolete, replaced entirely by dynamic mobility modeling that treats a store as a living part of a city’s ecosystem. We are already seeing a move toward integrating both pedestrian and vehicular data to create a 360-degree view of how a location breathes throughout the day. Retailers will no longer sign a lease based on who lives within a five-mile radius, but rather on the flow of the 15-minute commute “polygon” that shifts in shape from Tuesday morning to Saturday afternoon. This precision will allow small-format stores to thrive in “gap” markets—those regions where a 25,000-square-foot store would fail due to overhead, but a 12,000-square-foot store thrives because it is perfectly positioned on the right side of the street. Ultimately, the winners in this space will be the companies that stop viewing real estate as a fixed asset and start viewing it as a strategic intercept point in a customer’s daily journey. The data is telling us exactly where the shoppers are moving; the retailers who listen will be the ones waiting for them with open doors.
