Archive for the ‘Map Data’ Category

Shopping Boundaries Help Target Serial Shoppers

by Gloria Dufield, Marketing Specialist on April 26, 2012 at 2:51PM

Location based marketers have a tough job designing campaigns around users because you don’t want to offer a discount to someone who was already about to purchase your product.  For example, targeting anyone who comes within 500 feet of your big-box store is potentially redundant – they were already there to shop at your store.  You might nudge them toward a particular product, but such targeting isn’t getting them there in the first place. Random emails to them when they are not even close to your store have the inertia problem:  unless your offer is truly amazing, they probably won’t get off the couch to drive to your store.

There is a better way to target:  shopping boundaries.  We all know shopping districts or malls where there are many stores, restaurants and other consumer services all in one tight geography.  People in these areas are likely there either to work or to shop.  And if they are shopping, they are likely to go to multiple venues.  In these cases, it’s easier to nudge them into your store, even if that’s not why they originally went out.  Essentially, you are battling for share of wallet at that point because the inertia of leaving the house has already been overcome.

We offer over three thousand of the highest-trafficked shopping boundaries in the United States and Canada with expansion every quarter.  These predefined geofences are easy for marketers to incorporate to improve mobile targeting and promotions, without requiring custom infrastructure for each store.  Once a user crosses into one of these geofences, their propensity to respond to your marketing increases.

Interested in Shopping Boundaries?  Contact us to learn about these and other geofences to assist you in your geotargeted marketing initiatives.

Foursquare Hack Day Project Using Maponics Neighborhoods

by Johanna Hunter, Product Manger on April 17, 2012 at 9:07AM

We wanted to point you to an interesting internal project from engineers at Foursquare using Maponics Neighborhood Boundary data.  Engineers matched 1,500,000,000 check-ins globally to the neighborhoods in which they took place.  Using the resulting data, they determined the top categories based on location check-ins in each neighborhood and created a profile that reflects how people work and play in that neighborhood.

Algorithmically, they were then able to compare neighborhoods across different cities. For example, they determined which three neighborhoods in San Francisco were most similar to New York City’s East Village based on top check-in categories.

Want to know the three neighborhoods in San Francisco most similar to the East Village? Get the results and learn more at Foursquare’s Neighborhood Experiment.

Visit our Customer Use page for more interesting uses of neighborhood boundaries.

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