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Four Strategic Ways Retailers Can Use Location Data Beyond Advertising

  • Written by  Jeff White, Gravy Analytics

0aaJeff White GravyAnalyticsIt’s widely assumed that location data is collected primarily for the purposes of targeting and advertising — like the time American Eagle Outfitters geofenced its outlet stores to deliver nearby customers timely promotions and notifications in the malls’ parking lots — to boost not only foot traffic but sales. While location data does enable a variety of successful advertising initiatives, what’s often overlooked is its broader, strategic business power.

Location intelligence — generated from opt-in data that is thoroughly cleansed and, most importantly, aggregated and anonymized — can provide retailers with invaluable insights about their customers (both current and prospective), telling the stories of where they go and what they do there. Armed with these insights, retailers can better understand the markets in which they operate, the behaviors and motivations of their customers, and even benchmark against their competition.

Here are four ways retailers can — and should — be thinking about leveraging location data:

  1. Research: If your business decisions are solely based on historical transactions, you could be missing out. Insights from location data can reveal blind spots and untapped opportunities — such as new merchandise, services and engagement experiences — that will appeal to your target audience. Take Domino's for example: their recent delivery service to outdoor "hotspot" locations is a prime example of using location data to expand your business model. Though location data reflects real-world movements, it can also be strategic for e-Commerce businesses — use it to learn more about your customers, and find more just like them. These important nuances about people, their lifestyles and purchasing habits can help you understand your customers on a deeper level.
  1. Operations: Tech-savvy retailers can use location data to help understand where they can hone and optimize operations so that their business runs smoothly and the customer experience is flawless. H&M — in addition to many others — is already using big data to better inform how to stock shelves regionally and reduce unsold inventory. But location data can take this approach a step further, revealing when and where there are lines or unused spaces, for example, to help retailers determine where, when and if they need to add or displace stock, staff or amenities, and also identify opportunities for promotions and signage.
  1. Competitive intelligence: Want to know how frequently your customers (and prospects) shop at or visit your competitors’ businesses? How far do they travel to get there — and where else do they go en route? This gold mine of real-world insight about where people go and how they interact with your competition is invaluable intelligence for retailers. Better competitive understanding can help inform decisions about everything from pricing and inventory to in-store promotions and hours of operation.
  1. New Locations: Location data should most definitely be part of your due diligence when considering expanding into new locations. Important things to consider are how far people generally travel to those kinds of locations, and how foot traffic compares at businesses and amenities nearby. For example, if you’re thinking of opening a sporting goods store, it’d be wise to know first if the folks spending the most time in that area are golf enthusiasts or avid campers. Layer on top of that behavioral insights about your target customers and you’ll see whether the location makes business sense, before you make any final decisions.

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With a strategic approach, anonymized location data can help retailers continue to compete and evolve, enhance merchandise and services, address challenges, plan for the future and, ultimately, impact their bottom line.


 

Jeff White is the founder and chief executive officer of Gravy Analytics. He is passionate about building disruptive technologies that have large applicability to change industries. Prior to founding Gravy Analytics, he founded several companies and led them to successful exits.

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