Versium raises $2.5M to help marketers crunch data that predicts customer behavior

fanscoreversiumBusinesses today want to know as much as they can about their customers in order to better serve them. Often times, though, they are working only with information they’ve collected on their own.

But when marketers combine their work with other, more general “real world” information — things like household income, social media activity, demographics — that data as a whole suddenly becomes much richer.

That’s exactly what ‘big data’ startup Versium Analytics is helping their clients accomplish. The Redmond company just closed a $2.5 million round from angels in Silicon Valley and Seattle, including an investment from the TiE Angels Group Seattle, to take the phrase “know your customer” to a whole new level.

versiumLed by former InfoSpace executives Chris Matty and Kevin Marcus, the 18-month old company crunches data from publicly available online and offline information about “real-life attributes” of people, which is then combined with an organization’s existing data to provide companies with a more intelligent profile of each customer.

Matty is pleased with the 100 billion data attributes his company has already collected, but he’s even more excited about linking Versium’s information with enterprise data to really help companies make better choices.

By combining both sets of data, the 12-employee team at Versium has built a suite of predictive scores — think credit scores, but for ROI-driven categories like a fraud score or churn score — that can be integrated into already-existing enterprise applications and help companies answer questions like, ‘Who is more likely to cancel a membership?’ or, ‘Who is more likely to scam?’

CEO Chris Matty.

CEO Chris Matty.

“Our technology precludes the need for deploying a complex analytics platform,” Matty said. “The output is a simple score that addresses a business need.”

Matty learned that the brands and businesses Versium have worked with didn’t want another complex “big data” platform, but rather just wanted an answer.

“Instead of hiring a data scientist or training people how to use a system, we just give them a score that can be acted upon,” Matty said.

The predictive scores are very similar to credit scores in that you can understand what a number means without knowing what goes into the calculations. The video below shows this example for a professional sports team, giving fans certain scores depending on their attributes which can help the team target their marketing efforts more efficiently.