Microsoft Invests in Predictive Analytics Vendor
As venture capital investment appears to be zeroing in on specific applications and platforms, modest funding rounds are being reported by software startups focused on areas such as predictive analytics.
Prevedere, a cloud-based enterprise predictive analytics startup, said last week it has closed a $10 million funding round that included new investor Microsoft Ventures. The Series B round pushed Prevedere’s total venture funding to $19.55 million in four early rounds, according to the web site Crunchbase.
The latest was led by Norwest Venture Partners, which was joined by Microsoft and existing investors PointGuard Ventures and Rev1 Ventures, a seed stage venture fund based in Columbus, Ohio. Prevedere, which has offices in Columbus and Sunnyvale, Calif., said it would use the new funds to expand the reach of its platform in enterprise markets following robust year-on-year growth.
Microsoft (NASDAQ: MSFT) is among the startup’s roster of new partners. “Our investment builds on our existing relationship as a partner to help expand Prevedere’s work with enterprise companies,” Leo de Luna, managing director of Microsoft Ventures, noted in a statement. He cited the startup’s cloud-based business intelligence platform as a reason for the investment.
Founded in January 2012, Prevedere bills its “external data” predictive analytics software as filling the gap between costly data providers and internal business intelligence projects by delivering “external leading indicators” such as economic and consumer behavior. The artificial intelligence-based platform provides advanced statistics and pattern matching along with real-time predictive models based on internal data and external predictive analysis.
The advertised result is a reduction in errors when developing financial forecasts, claims the startup, which targets manufacturing, retail, insurance and financial services companies.
The company touts its platform as leveraging cloud computing as a way of flowing large sets of external data on customer behavior and other global data into its correlation engine to come up with more accurate forecasts about product demand or operations expenses.