This new intent signals-related information perfectly fits the digital body language model and has great appeal to B2B marketers. After all, it is the rare B2B marketer who would answer the question “Do you want to know when prospect companies are more interested in your product than they usually are?” with any response other than, “Of course. Of course I do.”

Two paths have emerged in terms of how this information is derived: predictive vs. fact-based intent signals.

Predictive analytics is an approach that bundles intent data with sophisticated statistical modeling to help companies prioritize their outreach to potential prospects. Marketers wishing to take advantage of predictive analytics will provide all possible relevant information to the predictive analytics vendor. That vendor will take all of this CRM, marketing automation, financial and other data to construct a custom statistical model. Over time, this model will become accurate enough that it can recognize the companies that are showing increased activity on the web that most closely resembles the marketer’s current customers.

The alternative is the fact-based intent signals approach – on a Data-as-a-Service basis. In this scenario, the focus is entirely on intent signals; there is no statistical-modeling component. The activity taken by an individual on the web is tracked, aggregated, and provided to marketing. The benefit of fact-based intent signals solutions is that they inform B2B marketers when a prospect organization is conducting the online research that marks the beginning of today’s buying journey. Additionally, the contact record is provided for the organization, eliminating the need for the large-scale data and modeling initiative that is inherent in predictive analytics.