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Strengthen Mobile App Advertising with More Accurate Data Sets

June 5, 2018

The average smart phone user has upward of 41 apps installed. And, time spent “in-app” continues to increase. The unique set of installed apps creates a wealth of data that provides a digital fingerprint―giving insight into who mobile consumers are along with their interests. With billions of unique smartphone user profiles around the world, advertisers often struggle to leverage all of this data to effectively target mobile ads.

However, Sift is one company that is successfully operating at the intersection of mobile and big data. Sift focuses on analyzing the connections and links between apps. The company leverages all of this data with advanced artificial intelligence (AI) and machine-learning algorithms to sift through more than 50 billion mobile ad requests daily in order to hyper-target app install ads for its clients. Sift works with some of the industry’s largest agencies and demand-side platforms. It has also run thousands of advertising campaigns and delivered more than 10 million app installs for some of the largest brands in mobile.

“Competition in the mobile app marketplace is fierce, so it’s imperative that we provide the highest quality of traffic for our advertising clients to help position them to secure the best possible conversion rates,” said Slawek Pruchnik, co-founder and chief technology officer, Sift. “Our platform processes more than 600,000 bid requests per second from our 11 real-time bidding exchange partners across 180 countries. We found that our previous geolocation provider was simply not returning the level of accurate IP results we required.”

Sift is using a full geolocation data arsenal along with proxy information to improve efficiency and performance of clients’ advertising―helping them avoid wasted impressions and fight click fraud as well as enhance attribution and analytics.

Read the full press release to learn more about how to strengthen mobile app advertising with more accurate data sets.

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