Stores and The City: One Size Doesn’t Fit All
New York has long been heralded as the Mecca for consumers and avid shoppers. From its role as proving ground for the 1837 inception of a small ‘fancy goods’ store by a 25 year old Charles Lewis Tiffany, to the multi-floor cathedrals of consumerism that populate midtown today, the city’s reputation for retail is famous around the world.
It makes sense then, that if you want to know what’s happening in the world of retail or to take a look at how shopping habits are changing, you would look to the city’s shoppers as a microcosm or a bellwether for buyers everywhere. And nowhere do patterns and behaviors come to the forefront more prominently than during the all-important January sales period.
The great thing about New York (apart from the skyline, the speakeasies, the stores and the inimitable sense of humor…. We could go on) is that walking is still one of the primary ways to get around. This means that foot traffic patterns provide incredibly strong indicators of consumer shopping habits (and what influences them) — but foot traffic only tells us part of the story. Combine the foot traffic data with the smartphone devices being carried around and all of a sudden you get a very clear understanding of who shops where, what times attract different groups and, all importantly, how you can better engage and converse with them.
Throughout January of this year we did just that. We ran our own study looking at shopper patterns in some of the world’s most well-known stores, looking specifically at foot traffic post-Holiday season at Macy’s, Bloomingdales, Saks, and Lord & Taylor to see what we could learn from the foot traffic around and into each to provide learnings for marketers. What we found was that high foot traffic alone wasn’t the best reason to heavy up media delivery to a specific retailer (despite an advertiser’s popular belief). Instead of a one-size fits strategy, each brand should target and engage the micro-tribes that make up their store audiences.
For example, Macy’s, a store placed in prime foot traffic location – between myriad subway and mainline train stations, and also likely the most marketed to international tourists as a destination itself. It’s hardly a surprise that it enjoyed the most foot traffic of the four stores we looked at. Conversely, Lord & Taylor and Bloomingdales saw less foot traffic during the observation period. At first glance, a marketer could conclude that the quieter stores offered less opportunity and, based on the higher foot traffic they might increase spend on targeted mobile ads in Macy’s and Saks. This approach would treat the entire in-store audience as the same tribe, sending them the same creative ad message.
But by using location technology, marketers are able to further break down, qualify and target audiences so they don’t have to treat in-store shoppers as the same demographic group. By monitoring device IDs after in-store visits to determine if they visit local schools during school hours or pick up and drop off times, “parents” versus “students” can be identified and messaged accordingly. Taking a closer look at the audience breakdown of the store traffic in this way, we saw that Lord & Taylor actually indexed with the highest audience of parents followed by Saks Fifth Avenue. With the higher price point of Saks Fifth Avenue and the older brand persona of Lord & Taylor, this data rings true with that of the retailer profiles and is an important insight for marketers to have in their arsenal when looking to reach certain demographic audiences.
One tribe (ie students vs parents) is not necessarily more valuable than the other but, as a retailer, this level of insight into the wants, needs and capabilities of each is invaluable as you plan advertising campaigns, sales strategies and in-store offers to target and engage each. Trends may start here in the city but what national and local stores can learn from the device and foot traffic data available today, will be key to making those trends work in every location, and for every buyer.