Social Lead Scoring: Uncovering the Real Value of Big Data for B2B Marketing

A new breed of Big Data analytics tools is bringing companies' digital footprints into the fold, analyzing unstructured and structured data to deliver richer, more detailed insights.

The concept of Big Data has become almost a clich?n the business world, with hundreds of articles and expert opinions championing the potential value of this unprecedented wealth of information for marketers. And, few would argue with the potential. The real-life application, however, is a different story.

The problem is that the real value of Big Data remains locked away, albeit not in the conventional, vault-like manner. It's actually the opposite: Harnessing Big Data for real marketing and sales advantage in the B2B space is like roping a cloud-it's there and you can almost define it, but containing it and actually controlling or wielding it to your benefit is nearly impossible. The sheer volume of information, coupled with the fact that it is unstructured and doesn't fit neatly into rows and columns, makes it very difficult to pin down any real, usable insights. In this case, more isn't exactly better.

Because of these issues, Big Data has never been able to live up to its potential when it comes to lead scoring, or the process of ranking leads in terms of their sales-readiness and potential value. Most lead scoring techniques gather and analyze three types of data-demographic, firmographic, and behavioral-all mostly found in nice, neat databases. However, these basic techniques leave a large number of stones unturned. First, conventional databases are often unreliable and frequently out of date, as key personnel change positions, job descriptions, or even move between employers. The popular use of web forms to pre-screen and gather the necessary lead scoring data is error-prone as people often enter false information to avoid being bothered with "unsolicited" future contact-or they simply bail out and leave the form unfilled altogether.

As a result, marketers are forced to make educated guesses and assumptions about who they should be targeting and how to find them. This frequently puts marketing and sales at odds, unable to agree on exactly who are the right targets, causing friction and a lack of focus in what should be a tightly concerted effort. It also puts tremendous pressure on marketing to funnel high-value prospects to the sales team, while essentially working with one hand tied behind their backs.

To resolve these challenges, savvy marketers are beginning to realize the potential of tapping into the social sphere to gain the relevant, timely, and more accurate insights they need to clearly identify and target the most valuable prospects. Why? Social networks, the blogosphere, content sharing sites, and other user-generated content sources are incredibly rich repositories of valuable prospect data. Even better, the owners of this data - the prospects themselves - take great pains to cultivate this content, continuously updating, refining, and adjusting their online presence to showcase their most timely and relevant needs, desires, wants, and interests.

If only we could somehow mine this data efficiently, at scale, to unlock the valuable insights this "cloud" holds.

Actually, we can. A new breed of Big Data analytics tools is bringing these digital footprints into the fold, analyzing this unstructured Big Data alongside conventional structured databases to deliver much richer, more detailed insights into who the ideal buyer really is, and where and how to reach them. Using technologies like natural language processing, web mining, and machine learning to turn the mountains of unstructured data into clean and actionable information, these new tools fuse the conventional methods of demographic, firmographic, and behavioral profiling with this rich social data to generate a social score - the ultimate mash up of third-party and primary data.

Using this method, sales and marketing teams can identify an ideal buyer profile, based on hard data and user-generated content, and then continuously refine this profile in real time based on changes in the market, business strategy, and other influences. Once the ideal buyer profile is established, marketing and sales teams can actively target and identify new prospects who match the profile based on similar characteristics in their social score.

For example, the ideal buyer for a cloud provider's services may be someone who's expressed interest in cloud computing, visits a Gartner or Forrester IT conference, follows cloud-focused industry analysts, and is knowledgeable about VMware and Amazon cloud. Matching new leads with this ideal buyer profile provides a strong indication about their relevancy and potential value as a prospective buyer.

As a result, harnessing Big Data through the power of social lead scoring tools enables highly refined prospect targeting that significantly improves marketing and sales efficiency, optimizes the time and money spent on prospecting efforts, and eliminates the friction between marketing and sales teams that can now be certain that they're all working toward the same goal. This precision targeting system has proven to deliver higher-than-average conversions by ensuring the message is getting through to the right people at the right time, from the very beginning.