Combine prospects' behavioral and social scores for accurate sales predictions.
But wait, there's a caveat. The data is unstructured.
This is where a new breed of big data analytic tools comes into play. Advanced technologies use natural language processing, Web mining, and machine learning to structure the unstructured (i.e., turn the mountains of information in the social Web into data that is clean and actionable). With these new tools, companies can take lead scoring to the next level by fusing demographic, firmographic, and behavioral scores with a new type of score—the social score.
Social score is best used against an ideal buyer profile that takes into account all possible digital footprints. For example, a company might find that the ideal buyer for one of its products is a person who takes an interest in cloud computing, attends a Gartner or Forrester IT conference, follows certain industry analysts, and has knowledge about VMware and Amazon cloud. Matching new leads with this ideal buyer profile will provide a strong indicator about the buyer's relevancy, regardless of the information he or she provides.
Combining the behavioral score, which indicates a person's level of awareness and interest with the social score, which indicates a person's true relevancy to the business, produces an extremely accurate prediction of one's likelihood of making a purchase.