There’s been a lot of buzz recently over location-based technologies, which leverage location tracking technology that is built into smartphones and GPS devices. These range from location-based mobile advertising and messaging, to drive time and traffic analytics used for site selection and competitive analysis. A world of opportunities is presented by these technologies: you can send a targeted message or offer to a prospect as they walk by your store or by a competitor’s store. You can track how many customers drive by each of your fast food locations at lunch time in comparison to the competition. You can count the number of cars that are in your competitor’s parking lot when they do a sale. There are many more such examples.
Similar to many big data plays, the opportunity is large and undefined. What is the best use case for your industry? Your company? Over the past few months, Topline Strategy has engaged in several projects that involved location-based technologies. These technologies include i) a mobile advertising and messaging platform, ii) traffic analytics for competitive intelligence, and iii) drive-time analytics for site selection. In each case our clients saw tremendous potential for the technologies across many customer segments. In all cases Topline narrowed down the opportunity to a few segments that are heavily affected by ‘drive by’ traffic, i.e., where customers are driving by a location and can be swayed to make a purchase decision right there and then.
For example, one client has an incredibly unique and advanced technology to message customers through notifications after customers downloaded an app. The client believed this approach would be attractive to ‘big box retailers’ in competitive industries such as Home Depot vs. Lowe’s. The use case was that a customer who had already downloaded a Lowe’s app for example, would be sent a message or coupon when they were parking/entering the Home Depot location. However, our research showed several problems with this approach. The first is the issue of frequency. For this approach to work there would need to be a large number of app downloads by customers who happen to also shop frequently at competitors. The second problem is the degree of hassle. If a customer is in a parking lot with the intent to purchase, would they drive to a different location to use a coupon? How big would that coupon need to be? The third problem is conversion – not all shoppers are actually buyers. You put all these together and the ROI is just not that attractive.
In contrast, our research showed tremendous appeal in ‘drive by’ industries: these are industries where customers make impulse purchases based on location. Examples are convenience stores, quick service/fast food, gas stations, motels and others. In these industries one can easily imagine swaying a customer to pull into a fast food restaurant if they are driving by and get a coupon. The main difference is that purchases in these industries are usually unplanned, the hassle factor is small, and typically the number of app downloads is large.
Although we have conducted research for different companies in this industry, the data has aligned incredibly well. Traffic based site selection technologies also appeal most to drive by industries. And we saw again, for big box retailers, in this case those located in anchor malls, mall demographics are a better indicator than traffic. But convenience stores and fast food chains love the idea of traffic-based site selection.
Other examples exist as well. The general learning from these projects is that vastly different companies and technologies can share the same underlying customer needs. And that diving down to detailed customer needs by talking to customers, wins, losses and the market as a whole can uncover previously unknown trends.