One of the retailer’s most valuable assets isn’t their inventory. It’s their location intelligence. Retailers live and die by their ability to open up profitable locations, so they invest huge amounts of money into making the right leasing decisions. These decisions draw from various data sources and include information like housing starts, traffic patterns, and local foot traffic.
But as digital and physical retail collide, these traditional metrics might not tell the whole story. As more shoppers use a mixture of online and in-store options (like buying online and going to the store to pick up the items), retailers need to know more than the local resident’s in-person shopping proclivities. They need to know about their online spending habits as well. “Retail is becoming a marketing and customer acquisition channel for many brands,” said Justin Abrams, CEO of retail analytics provider Flagship.
According to Abrams, retailers often don’t fully understand their clients’ omni-channel purchasing trends. “For as big of an industry as retail is, there is not much data science going on,” he said. Currently, in-store promotions are either made based on gut, or at best, an analyst combing through tens of spreadsheets to make an informed decision. Imagine if brands were able to leverage machine learning across infinite data sets? They could then start optimizing for sell-through, versus falling in the trap of pushing new lines and trendy products that would likely sell well anyways. “Data science would help retailers not just understand how promotions help increase sales but how they can improve the overall health of the company,” Abrams said. Rather than pushing a top item, they might find it more beneficial for margins and inventory to push “unsexy” products.
Better promotions are only the start of what better retail analysis could do. The rise of fast fashion and the growing popularity of pop-up stores has increased retailers’ reliance on a better understanding of customer purchasing habits. Some companies are experimenting with adjustable pricing, where items could instantly go on sale depending on their local supply and demand.
Interestingly, this shift to a more holistic understanding of consumers has brought the landlords into the role of providing analytics on both physical and digital spending in and around their locations. Abrams has been working with landlords to help them increase their knowledge of their market to help them advise their tenants. Many tenants are even willing to pay more for a space that comes with location analysis. “If landlords don’t know the online shopping habits of their local community, they are missing something too,” he said.
Landlords are also finding new ways to get data that can inform their location analysis. “License plate readers have been a great way to understand your clientele, but now you can layer mobile phone data onto it to get an even better understanding of not just where they live, but where they are coming from,” said Alex Zikakis of Capstone Advisors. But these efforts can only give retailers an idea of who is buying at stores, leaving out a large and growing swath of the population that prefers to shop only online.
As the retail landscape continues to blend online shopping and physical stores, how we think about things like location analysis, local markets, and tenant mixes becomes more complicated. Bringing local online shopping habits into the location intelligence calculation is an obvious step in the right direction. But, including online shopping in brick-and-mortar retail decision-making is only the beginning. Retailers looking to expand their real estate footprint need local knowledge, which is something that landlords are uniquely positioned to provide. Soon we will likely see retail landlords not only offering space in their shopping centers but bundling services that help their tenants make better retail decisions and better utilize their most valuable resource.