Four Strategic Ways Retailers Can Use Location Data Beyond Advertising

August 28, 2018 41 Comments

It’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. 

     

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