While location has been important in the retail value equation (think of the old saw said about the primary driver of store success, “location, location, location”), the confluence of COVID, climate-related challenges and customer desires for more targeted assortments have brought it into the forefront of retailer consciousness.
The current resurgence of brick and mortar stores as described in Business of Fashion (paywall), didn’t just start with the wind-down of the last COVID surge, it actually dates from the MIDDLE of the first surge.
As people left cities like New York, which were hotbeds of COVID cases and stores closed, entrepreneurial retailers found other places that could be profitable. From the article in Business of Fashion:
If the lockdowns [SIC] exposed the flaws of a brick-and-mortar approach to retail, designer Nili Lotan took away the opposite lesson.
At the height of the pandemic, her namesake label’s East Hampton store was so busy that it made up for the lost revenue from her two closed stores in Manhattan.
The experience led her to invest in more stores wherever her wealthy clientele congregates: one opened in Aspen in June, another is planned for Palm Beach later this year and three more locations are in the works for 2022.
“Every store that I don’t open, I’m leaving money on the floor,” Lotan said.
In other words, shoppers, especially wealthy ones, did still want to touch and feel the products they were going to buy…they simply didn’t want to be trapped in what they perceived to be Petri dishes with no access to the outdoors.
Certainly, it doesn’t take advanced analytics to predict that wealthy New Yorkers would move from Manhattan to South Florida or the Hamptons to wait out the medical storm, but these were not the only places they went, nor were the wealthy the only ones to “get out of town.”
But where did they go? And what do they want to buy? For this, we need Location Analytics. How else would a retailer know that its customers were moving to Boise, Idaho, or from Detroit to Cape Coral, Florida?
And how else would a retailer know which of those customers had made the move, how demographics had changed on both ends of the move, and which of those customers wanted the full in-store shopping experience, vs. the variety of Omnichannel delivery methods, from pure eCommerce to BOPIS, BOPAC or BOPAL (buy online, pick up at locker)?
As RSR partner Brian Kilcourse pointed out in a recent newsletter article on Location Analytics:
“Businesses need to see people, assets, and processes in real-time—and to be able to analyze what they’ve uncovered to improve both strategic and operational decision-making.
That’s where location data and the insights that can be derived from it become really important.
Whether you call the movement towards that kind of visibility “digital transformation”, “digital twins”, “location intelligence” or something else, the ability to capture dynamic location data from people, processes, and things, and the analytics needed to make sense of it, it has become a barrier to competition.”
To say that retailers’ understanding of the importance of location intelligence is rapidly evolving is an understatement.
In the four years that RSR has benchmarked retailer attitudes about the promise of location data and analytics, they have changed dramatically from enthusiastic naiveté about micro-marketing all the way to a belief that they are essential to achieving sustainable growth, greater operational effectiveness, and more resilience in a risky marketplace.
This more mature point-of-view was already observable by March 2020 among over-performers (“Retail Winners” in RSR’s parlance), but the pandemic acted as an accelerator to retailers’ efforts to address them.”
Look at the differences below, from RSR’s latest benchmark report on the subject, Creating Competitive Advantage With Location-Aware Processes:
| Perceived Value Of Analyses Enabled By Location Data (‘Very Valuable’) Source: RSR Research, July 2021 | ||
| 2020 | 2021 | |
| Combining geographic and demographic data for better business decisions | 71% | 82% |
| Site analysis and new location selection | 53% | 65% |
| Workforce sources and needs | 64% | 65% |
| Supply chain network design | 59% | 63% |
| Instore mapping | – | 61% |
| Target marketing based on CRM data | – | 61% |
| Re-segmentation on lifestyle and buying choices | 51% | 60% |
| Weather analysis | 35% | 60% |
| Delivery optimization | 59% | 59% |
| Assortment planning | 53% | 58% |
| Assortment localization | 55% | 53% |
| Cross channel market analysis, pre- and post-COVID | 53% | 52% |
While this year a majority of retailers generally place a lot of value on every analysis option we presented, the highest valued choice, “combining geographic and demographic data for better decision making” is really an aggregation of all the others.
And while combining geo- and demographic data in decision-making processes is by no means new, it is certainly gaining momentum as the nature of demographic data itself is changing from static to dynamic information.
To be successful in today’s fast-changing world, retailers need to be able to “see” what’s happening in and around their businesses in real-time.
Such vision enables them to react quickly to changes that affect their business outcomes, and for several years now, we’ve been researching the location intelligence tools that hold the key to bringing this vision to life.
Location Analytics is taking its place as a key tool in supporting what consumers want and need, regardless of their wealth or demographic status.
COVID has been called the great accelerator, but it also made completely unexpected changes in behavior. We expect more changes ahead. Watch this space!
