Quantifind Debuts DIY Chat Influencer Analytics

In the US, brand insights and analytics specialist Quantifind has launched a DIY machine learning solution called SIGNUM Analysis, to enable marketers to identify those consumer conversations that influence purchasing decisions.

Founded in 2009 by two physicists from Stanford University, Quantifind's flagship product suite Signum uses patented technology and predictive models to help marketers analyze historical financial data alongside unstructured conversations, and link brand strategies and sales. The resulting data is used for targeting social media campaigns, and developing ads. In February, the firm raised $30m in a round of growth funding.

The new SIGNUM Analysis solution claims to remove the 80% of consumer data that is 'noise' with no correlation to revenue, allowing marketers to focus on the relevant 20% which the firm says ties consumer data to buying behavior and revenue. The application draws data from billions of Twitter posts, Tumblr blogs, 'voice-of-customer' logs and forum posts, correlating it with point-of-sales records, new product development sales records, and revenue figures. It also offers drill-downs with filters to explore trends down to individual consumer conversations.

CEO David Karnstedt (pictured) comments: 'SIGNUM Analysis embeds intelligence into daily operations by using data science to automate the work of cleaning and filtering irrelevant data. This allows marketers to tap into millions of insights from consumer conversations as part of their everyday workflow'.

Web site: www.quantifind.com .