Over the past 5 years, using mobile data for retail site selection has become table stakes. The problem is that many retailers today are using this data incorrectly which can lead to multi-million dollar mistakes. At Locate, we have implemented a successful mobile-data driven site selection strategy for over 100 retail brands, and we want to share the 3 common mistakes retailers make that can make or break your site selection. This is Part 1 of a 3-part series. Mistake #1: Not customizing the mobile data analysis for your brand This is by far the most common and fundamental mistake. Most retailers analyze the number of visits that a co-tenant attracts to estimate the number of customers they can realistically attract themselves: “The Chipotle across the street attracts XYZ visitors a month, this could be a great site!” Have you heard something like that before? One restaurant brand we recently consulted found an availability in a similar situation: the co-tenant next door was indeed Chipotle, which attracts 17,700 monthly visitors, and ranks 7th out of 107 locations in the state, per Locate Data: A great location at first glance but further investigation reveals otherwise. This client specifically targets a higher end demographic, and more specifically, the Uptown Individuals psychographic segment. However, here’s the consumer breakdown of the visitors of this Chipotle (and nearby retailers), per Locate: This client’s target customer segment (Uptown Individuals) merely makes up a whopping 14% of the visitors at the Chipotle and nearby retail shops. That means of the 17,700 monthly visitors at the Chipotle, less than 2,500 of them are likely to be customers of this client. This leads us to our first principle when using mobile data for site selection: When analyzing mobile data, retailers need to filter down the visitation values based on their target customer profile. Click Here to Contact Us Secondly, the peak hours of the client’s existing locations are from 6:00pm – 9:00pm. However, here’s the day-part breakdown of when consumers are frequenting this Chipotle (and nearby retailers), per Locate: Here, we see that the 6:00pm – 9:00pm time frame accounts for less than 1/3 of total visitors of nearby retailers. If we combine the number of visitors that fall within the target customer profile AND this specific time frame, we need to reframe the initial analysis to instead say: “Of the 17,700 monthly visitors to Chipotle, less than 10% of them are likely to be our customers!” This insight leads us to our second principle: When analyzing mobile data, retailers need to filter down the visitation values based on the specific time frame they typically attract their customers. Mobile data analysis is of course one of many factors that go into site selection, but this example shows how a site analysis that starts out very promising can be extremely misleading and potentially lead to a multi-million dollar mistake if the analysis is rudimentary. Click Here to Contact Us About Locate Locate is a national retail brokerage that provides a unique data-driven leasing solution for over 100 growing retail brands. Our team of experienced brokers use mobile data and A.I. to help you find the best location for your brand that can increase your store sales by up to 15%. If you’re interested in learning more about how we can grow your top line sales, we’d love to hear from you. – Team Locate firstname.lastname@example.org www.locate.ai
3 Mistakes Retailers Make With Mobile Data (Part 1)
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