Inventory Data from Retailigence Helps Mobile Users Buy Locally

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sucks data about products, prices, and inventories into a vast, cloud-based database. It then provides an application programming interface (API) that app developers can use to grab the data, based on inputs such as a UPC barcode, a product name, or even just a product category, such as “blue jeans.”

AppNet returns product data filtered by the inquirer’s location. All of the product listings you’ll find in retail search apps like Town Square for the iPhone, ShopWithIt for Android phones, and ShopSavvy (on both operating systems) comes through AppNet. “We have data for 10 million products in 100,000 stores,” Geiger says.

More than 1,000 developers are signed up to use the API, and Geiger says retailers have been happy to contribute their inventory data to the system. “Things are changing in retail,” he says. “A year ago their primary focus was on creating an iPhone app. Nine months ago it was on creating an Android app. Six months ago it was a mobile website. Now they are starting to realize that they spent all this time and money on their own apps and sites, but it’s still less than 1 percent of the population that has ever downloaded any of those apps or used the sites. That’s the sweet spot for us to approach them. We say ‘Look, you have a million third-party apps that consumers are using, and you need to be visible there.’”

This year, the 25-employee startup has branched out in a new direction: providing data for display ads on the mobile Web, through a second service called AdPop. But the product wasn’t actually Geiger’s idea: it was Microsoft’s.

“They approached us and said they were going to be spending millions of dollars on this ad campaign to promote Office on mobile display ads, and they would like the ability to show a consumer who clicks on the ad which nearby stores are selling it,” Geiger says. “We had honestly never thought about that use case. But we did the campaign and Microsoft loved the results, and the retailers loved the fact that we were driving consumers into their stores. That woke us up to the opportunity.”

The North Face "Router Backpack" Mobile Ad, with location data from Retailigence

The North Face "Router Backpack" Mobile Ad, with location data from Retailigence

Through AdPop, Retailigence provides the same data available through the AppNet API to mobile advertising networks, which use it to customize mobile ads on the fly with maps and local product data. The North Face, for example, has used AdPop to alert mobile users in San Francisco about its “Router Backpack,” which has extra compartments for laptops and wireless gear. If you see the ad while you’re in downtown San Francisco, it will come with a map directing you to The North Face’s Union Square store.

AdPop users find that the click-through rates on their mobile ads are 20 to 50 percent higher when they include this kind of local data, Geiger says. And in the future, once mobile payment technology catches on, Retailigence might be able to help mobile advertisers in another way: by providing data on each ad’s payback.

“The reason why so little money is going into mobile advertising so far is because people don’t know the effectiveness of it,” Geiger says. “The way to measure the true effectiveness would be to track the behavior of somebody after they see an ad. That’s exactly the void we are filling. We will be able to see that Mr. X saw the ad using Retailigence data, clicked on it, went into the store, and used a mobile payment solution to buy the product.” This kind of tracking already works in Japan, where many more consumers have payment systems built into their smartphones; it should be up and running in the United States within 18 months, Geiger says.

But the company isn’t betting its whole future on mobile Web ads. As the Vitaminwater campaign illustrates, Retailigence’s data could be useful in any situation where product information is being delivered in digital form and location data is available.

“Think of it this way,” Geiger says. “Brands are spending billions of dollars making people aware of products, but then it stops; there is nothing beyond that. Worse than that, nobody knows the ROI on any billboard or any magazine ad. Imagine if, through Retailigence, we could add to any advertisement a bit of data that not only drives people to stores but lets us track who went and what they bought. That is powerful and that is what we can enable.”

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Wade Roush is the producer and host of the podcast Soonish and a contributing editor at Xconomy. Follow @soonishpodcast

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