BrandIntel’s unique methodology for collecting, processing and analyzing spontaneous online consumer content is backed by proprietary technology, iterative human analysis and proven best practices.

While online research may return millions of data points, only a small percentage is usable and contextually relevant. BrandIntel’s standard-setting methodology brings order to unstructured data by first filtering content a minimum of five times: twice through BrandIntel’s proprietary technology and an additional three times by fully trained data taggers.

BrandIntel then applies quantitative analytics and relevance scoring to ensure data and recommendations provided accurately reflect consumer sentiment, with zero margin for error.

BrandIntel easily identifies this relevant data by first validating the audience and then filtering out data that:

• Cannot be authenticated
• Is misinterpreted
• Lacks emotional content
• Is targeted at the wrong demographic
• Is from irrelevant sources