By Heather Clancy
March 24, 2015

Analytics company Localytics thinks it has the answer to building and retaining mobile app audiences, and it just got another $35 million vote of confidence for its vision.

Its Series D funding round, led by Sapphire Ventures, also includes existing investors Foundation Capital and Polaris Partners. The new infusion almost doubles the 200-person Boston company’s backing, bringing total backing to $60 million.

“App marketing is seeing tremendous growth, and Localytics has the strongest customers base and the unique combination of app marketing and analytics needed to dominate this category,” said Sapphire managing director Doug Higgins, who has joined Localytics’ board.

The company’s technology supports more than 32,000 apps across 2.3 billion devices, including software for Nordstrom, Rue La La, eBay, The Weather Channel, and MyFitness Pal. Many of the founders actually were mobile app developers before founding Localytics. CEO Raj Aggarwal, a former consultant with Bain & Co., was involved with the strategy behind the iPhone App Store concept. CTO Henry Cipolla built the analytics technology for Microsoft’s desktop applications.

“We set off to build a mobile analytics company, that could tell mobile apps developers what they could do to create a better experience,” Aggarwal said. “What we started to discover in working with customers is that they were struggling with monetization.”

The funds will be used to build out Localytics’ vision for “predictive app marketing” that goes beyond the in-app messages and push marketing used to encourage certain behaviors.

According to Localytics research, 20% of all mobile apps are used just once. If someone doesn’t return within the first week, there’s a 60% probability that he or she will never come back. Predictive app marketing will help marketers decide which users are at risk and which ones are worth pursuing.

“Predicting a user’s next move really brings personalization to life,” Aggarwal wrote in the company’s blog. “Our platform now takes the robust user and behavioral insights we collect for customers and users data science and machine learning to identify correlations and therefore a customer’s intent.”

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