The broad principles of site selection tend to remain static. You’ll always want to prioritize locations with the greatest access to demand from the highest concentrations of your target demographics. But the process retailers use to analyze and compare these opportunities is constantly advancing. The best location strategies adapt. Retail site selection continually reacts to new information, trends in consumer behavior, and increasingly refined best practices.
As you consider where to open, close, relocate, or consolidate stores in 2023, it helps to understand and anticipate where the industry is heading. What are other retailers doing? What are the experts seeing?
Tango has helped thousands of retailers analyze and prioritize hundreds of thousands of store locations by combining their unique business goals and insights with our highly advanced site selection software, Tango Predictive Analytics.
Here’s how we’re predicting the site selection process will continue to change throughout 2023.
Retailers have been talking about omnichannel retail for years. And while several companies had already been experimenting with alternative fulfillment options like buy online, pickup in store (BOPIS) and curbside pickup, COVID-19 turned these services into industry staples, making it impossible to ignore the connection between online sales and a retailer’s physical presence.
Today, this has brought increased attention to ecommerce interaction models. As Tango helps retailers create site models that accurately represent their businesses (so they can correctly forecast sales), many want to see how opening, closing, or relocating stores affects their ecommerce sales. What’s the total value of adding a point of presence to a trade area?
You wouldn’t necessarily want to close a store for poor performance if it also serves as a key fulfillment center for online orders. And if one location has greater potential to positively impact ecommerce sales, your prioritized list of sites should probably reflect that.
Many retailers are beginning to restructure or decommission COVID-era fulfillment options that require significant dedicated staff. These services were essential when the regulations and consumer mindsets made in-person shopping difficult, risky, or outright impossible. A short time ago, not offering curbside pickup or click and collect options had devastating consequences. But for some, the cost of facilitating these services now outweighs the benefits. Plenty of consumers still find alternative fulfillment options more convenient and accessible, but enough of them are comfortable setting foot in stores that adoption is slowing and, in some cases, even decreasing.
Kohl’s has ended their curbside pickup. Barnes & Noble de-emphasized their curbside pickup and only accommodates it by special request. Sam’s Club now charges $4 for the service. While alternative fulfillment represents part of the omnichannel opportunity, demand is decreasing, so it will likely become less important (and less common) in site models.
3. Standardized approaches to site selection and real estate transactions will grow increasingly popular
Taking a generalized approach to a multimillion-dollar investment may seem counterintuitive, but standardizing site selection isn’t actually about treating all sites, trade areas, and markets the same—it’s about using the same process for evaluating the opportunities they present. This is particularly important for retailers exploring international opportunities.
Lately, we’ve been seeing more retailers forego the time and money it takes to analyze the nuances of local markets (like the differences in demographic spreads between countries) and instead opt to focus on known predictors of success.
In practical terms, this means treating US and Canadian customers as a shared customer base, unifying your segmentation system across borders, rather than segmenting by country. It means using standardized expenditure categories, points of interest, and demographics, while ignoring less impactful sub-variables.
A streamlined, consistent approach makes data far more affordable to collect and configure. The tradeoff is that you lose unique variables like local demographic segments, but many retailers are starting to find that these aren’t always necessary for predicting success in a trade area.
Uber Eats and Grubhub have been around for years. But the COVID-19 pandemic brought a surge of demand for these third-party services and came with a slew of younger competitors. For restaurants, this has created a sometimes-frustrating layer of separation between each location and the customers it serves, but it’s also decreased the need for their own delivery services.
You lose visibility into who the end buyer is, but the opportunity delivery service represents is becoming less about how far your restaurant is willing to go and more about the prevalence of these third-party services.
While COVID-19 isn’t driving demand for third-party delivery anymore, it vastly increased the popularity and market penetration of these companies, and restaurants are now projecting third-party delivery sales in their site models. If you’re not incorporating this variable, your models might be missing this important contributor to today’s successful restaurants.
Where you source your demographic data can completely change the results of your market-level and site-level analysis. Outdated information or simply poor data collection could lead you to act on “opportunities” that aren’t there or miss the ones that are. And in some countries, census data is unavailable, limited, or not reflective of the actual demographics.
That’s why a lot of retailers are leaning more heavily on mobile movement data. This real-time dataset is a highly versatile tool for zeroing in on demand and refining your analysis. It’s critical for sales forecasting and identifying whether a site is actually in position to access the demand in a trade area. If you want to see which intersection is busiest, tracking mobile movement is the best way to see that (especially if you want to focus on specific demographics).
Mobile movement data also helps retailers learn more about their current customer base or perform competitive analysis through geofencing. If you know very little about which demographics actually visit your stores, for example, geofencing lets you track anonymized demographic data from the mobile devices that enter your locations. And you can use the same functionality to see generalized data about the people who visit your competitors.
Increasingly, mobile movement data is proving to be one of the most valuable datasets for site selection, and throughout 2023, we can expect more retailers to start relying on it.
Retailers use processes like retail void analysis and white space analysis to vet and quantify market-level opportunities. Lately, we’ve been seeing a lot of retailers, particularly restaurant chains, topping out in their regional markets and focusing on international expansion.
Evaluating sites in foreign markets used to be extremely difficult and required extensive local expertise and consulting. But as more retailers adopt a standardized approach and use tools with international datasets (like Tango Predictive Analytics), there are fewer barriers to cross-border opportunities.
Shopping malls used to be prime locations for a wide range of retail categories. Under the right conditions, they’re still effective for plenty of brands, but there are fewer and fewer of them every year, and many of those that remain are in a state of decline. In fact, some retail consultants estimate that there could be as few as 150 shopping malls remaining just a decade from now (less than one-quarter of the malls we have today).
While shopping malls carry a number of potential benefits, including valuable co-tenants and a steady stream of foot traffic, modern retailers have the tools and insights they need to identify locations that are in a better position to access the most relevant demand. Instead of planting yourself in a mall, you can position your store next to the points of interest that are most appealing to your best customers, or even in the sites with the highest concentrations of and greatest exposure to your target demographics.
Shopping malls may generally be on the decline, but between strip malls, the remaining shopping malls, and other business centers, retailers still have a variety of opportunities to lease locations with co-tenants. In the past, raw sales figures and brand awareness were the main considerations that made a co-tenant desirable. These are still certainly factors, but retailers have access to far more granular (and useful) information about their potential co-tenants.
For example, you may know that your most successful locations have a particular type of business or brand nearby. Using cross-shop analysis or mobile movement data, you may even know exactly which complementary brands your customers spend their time and money with. Using affinity co-tenant scores, retailers are prioritizing their co-tenants based on business alignment and overlap in target demographics.
Co-tenants represent a special circumstance, but it’s worth noting that this analysis is relevant to sites without them as well. Having complementary businesses across the street or within a short radius can significantly impact your success as well.
Historically, train stations, airports, universities, and other non-traditional locations have been difficult to forecast—you’re often estimating the potential of a completely transient population, or one which fluctuates frequently. But these are important opportunities, particularly for restaurants that are topping out in traditional locations.
This is another area where access to mobile movement data is empowering retailers to better evaluate opportunities and confidently make unconventional decisions. Using a 100-meter grid size, retailers can see the actual foot traffic around these nontraditional locations and forecast sales based on the demographics in the immediate area. They can also see how the opportunity changes throughout the day, week, month, and year, learning about the site’s unique peak hours and seasonal demand.
Any location is an opportunity, and modern retailers can equip themselves to evaluate the size of that opportunity, giving them a wider range of viable growth options.
Some brands are more dependent on seasonal demand than others. And if your business only operates on short-term leases, site selection and market-level analysis becomes a more frequent part of your operations. Traditionally, this model has come with a high degree of risk.
What if your best-performing locations are no longer available when you need them?
Can you expect a pop-up store to perform the same from year to year?
But with advances in site selection software, pop-up stores can be increasingly viable and agile. A seasonal store can easily analyze the impact of returning to a market in a new location versus leasing the old one again.
Even brands with long-term locations may find the feasibility of pop-up locations more appealing. Local events and festivals, coupled with high commercial vacancy rates and better analytical capabilities, could open the door to more short-term stores.
Whether you’re getting ready for market planning or digging into site selection, you need advanced tools, capabilities, and processes to create accurate site models, forecast sales, and pursue the best opportunities.
For that, you’ll need a robust predictive analytics solution like Tango Predictive Analytics. Tango combines GIS mapping capabilities with mobile movement data and comprehensive site selection criteria, empowering you to make real estate decisions you can trust.
Want to see what Tango Predictive Analytics can do for you?