Big Data is often dismissed as a buzzword, but the simple truth is that better B2B marketing data is continually becoming available – and “better” comes both in the form of increased volume of an existing type of data and in the form of entirely new types of data.
Perhaps the most interesting and impactful new form of B2B marketing data is what many refer to as intent signals – the ability to monitor the online activities of people who are browsing the web, downloading white papers, registering for webinars, and other such actions while at work. B2B marketers are now very much accustomed to monitoring visitors to their own websites. What’s new is the ability to collect data about visits by people at specific corporate IP addresses that you don’t know, plus visitors to everyone else’s website – to monitor activity across the internet.
The purpose of this, of course, is to identify when individuals at a given company are beginning online research that precedes a purchase, and a great deal of innovation is taking place identifying and using this information.
B2B marketers today appropriately place a great deal of emphasis on identifying and understanding what Steve Woods of Eloqua termed the “digital body language” of prospects. Any online activity that can be monitored should be, as every available data point is helpful in discerning the evaluation and purchase intentions of individuals and the organizations for which they work.
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.
There are clearly use cases justifying the scope of these undertakings – the data cleansing and augmentation, custom model tweaking and modification, and ongoing commitment to maintenance – that predictive analytics requires. The great benefit is that it provides tremendous guidance in terms of how prospect companies should be prioritized.
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.
Marketers using the fact-based intent signals model conduct outreach to all prospect companies identified. This outreach might consist of automatically triggered email campaigns, programmatic display, even direct contact from a sales team that has been notified with an alert.
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