We’ve all heard the buzz about how marketers are finally starting to apply “big data” in earnest to guide their outreach strategies. Some even predicted that 2014 would be the year that this shift to true big-data integration would happen.
However, Razorfish recently surveyed 685 C-suite executives and published these findings: A whopping 76 percent of marketers are not using behavioral data for segmentation analysis or targeting. The reality is that marketers are largely missing out on high-frequency, real-time insights which have the potential to radically change the B2B marketing landscape.
Differentiating between types of data
In the world of predictive intelligence, we talk about two distinctive types of big data: behavioral and descriptive. To transform B2B companies into true data-driven enterprises, business leaders must first and foremost understand that not all data is created equal. Some definitions:
- Descriptive data is typically structured data which is compiled (via a survey, for example), provoked, created, and transacted. Descriptive data determines the demographic attributes of a person or company, such as position, age, title, company size, etc.
- Behavioral data is typically unstructured, user-generated and must be captured. It’s good to think about behavioral data as “digital breadcrumbs” that reveal behavioral patterns of target consumers and predict those consumers’ next purchase, among other insights.
The value of behavioral data
While behavioral data is harder to compile and interpret due to its irregular and unstructured (“dirty”) form, it’s worth the effort, in my view. Traditionally, marketers have used data to understand the bottom of their sales funnel. But this part of the funnel represents fewer than 10 percent of market opportunities. Used correctly, behavioral data can provide insights on the full funnel, including at the top, where new prospects haven’t even surfaced yet.
By itself, descriptive data primarily lends itself to “spray-and-pray” marketing tactics. For example: A marketer may assume that all CMOs in companies with 5,000+ employees want to be touched with messages about Product X. However, this may or may not be true. The marketer would be better served by understanding and leveraging digital footprints to find buyers who are actually in the market to buy now. This insight is crucial for marketers aiming to shift away from spray-and-pray methods toward more-refined, targeted marketing.
By aggregating the digital footprints of a potential buyer (behavioral data)—what material that buyer is perusing on a website, what his past purchases have been, etc.–marketers can accurately target messages according to where the buyer is in the purchase journey, what s/he will likely purchase, and even the potential value of the purchase. More targeted marketing is more effective marketing, which means higher MQL-to-SQL conversions and, ultimately, increased revenue.
A significant barrier to this approach is determining how to aggregate behavioral data with existing descriptive data, and put the resulting insights into action. Because behavioral data is so widespread and hidden, it requires highly flexible, big-data solutions to make sense of it. With a static “black box” solution, marketers risk not receiving the right insights from the data to intelligently inform their strategies and decisions. In fact, according to Razorfish, of the 24 percent of its survey respondents who are using behavioral data, less than 20 percent have the capabilities — technology, creative execution/processes and integrated data— to deliver a targeted experience.
The good news is that the B2B predictive-intelligence landscape is rapidly evolving, and with that comes a wealth of opportunities for marketers to use customized technology and predictive analytics to improve process efficiencies and predict sales.