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.
Predictive vs. Fact-based (Intent Signal) Models
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. One advantage of the fact-based intent signals model is that, because there is no “look-alike” modeling, there is no need for the large-scale data and modeling initiative that is inherent in predictive analytics.
One way to view the two approaches is that the fact-based intent signals model identifies for a marketing team the companies that are currently showing more interest in their product, service, or solution than usual, while the predictive analytics model adds a layer of interpretation, using look-alike modeling to indicate which of these prospects most closely resembles current customers and thus should be most likely to purchase – hence the “predictive” aspect.
There is value to each of these approaches, and it is up to each organization to decide which is the best fit for its objectives.
Prioritizing High-quality Leads
How important is the prioritization of leads using the fact-based intent signals approach versus predictive analytics?
The fact-based intent signaling approach does not require traditional lead scoring because all of the prospects are in the early stages of the buying cycle. Marketers can skip this process, saving time-to-market and money. The content records can be used to automatically trigger tailored content to these individuals through integrations with marketing automation solutions such Eloqua or Marketo. Marketers using this approach send, on a “set it and forget it” basis, tailored campaigns designed to appeal to prospects who are in the online research stage. This approach offers great breadth and timeliness, as campaigns are triggered immediately and on an ongoing basis.
The predictive analytics model will require the prioritization of leads. This model adds a layer of prioritization but does not indicate which prospects are in the buying cycle, as the fact-based intent signals model does.
Calculating Actual Marketing ROI
Historically, it has been a challenge for marketers to measure the ROI of their efforts, and key performance indicators simply aren’t cutting it anymore. When a company is able to apply layered data to marketing decisions, it makes more informed choices. These choices are also backed by intelligence, which comes from a strong understanding and application of big data.
Making well-informed, thoughtful decisions that can be tied to specific metrics with financial implications can be the difference between spending money where there is little or none to be made and targeted, lower cost/higher return hotspots. Many companies overlook the importance of big data in calculating marketing ROI, but that is changing.
As the old saying goes, “If you are not going to lead, get out of the way.” Early adopters still have a significant advantage, but mid-adopters retain a chance to catch up and compete successfully.
Identifying Viable Opportunities
B2B marketers are beginning to use big data to see what’s happening in near real-time. By weeding out the less probable conversion opportunities in their email and homing in on prospects with a stronger interest in what they are offering, they are meeting their objectives faster than ever before.
We can take that principle one step further by simplifying opportunity identification, something many B2B marketers struggle with on a daily basis. This can be accomplished by applying granular filters to large data volumes and engaging with prospects before they present themselves as a potential customer. When you add in behind-the-scenes analytics and account monitoring, moving marketing to the next level is fast, effective, and far less costly.
A recent study by Dun & Bradstreet found that over 66 percent of companies have “a lack of revenue and industry data for existing customers.” This is a staggering percentage, especially considering the weight this data carries on prospects’ likelihood to convert.
Marketers benefit from having as many data points at their disposal as possible, so the commonality of incomplete data is testament to the difficulty in finding and maintaining reliable, meaningful data. Access to exceptional B2B marketing data is clearly a competitive edge.
Reducing Barriers to Entry
Barriers to entry have been a major struggle for B2Bs, but big data is changing that. New approaches are providing access to some benefits of big data without requiring the complex algorithms and extensive data mining that typically have been required with older approaches. Mid-sized companies may now avail themselves of the best information, for example, sorting through billions of online behaviors to determine the best targets across every industry. This newfound ability to break into new markets more easily is what Big-Data-as-a-Service (BDaaS) companies are really delivering. We see beyond what data typically reveals. Instead of a patchwork of probabilities, we see a completely objective, real-time 360-degree-view of individuals, based solely on factual, descriptive actions. This knowledge, paired with automated triggers and instant engagement, is where real value lies.
These are only some of the remarkable developments in big data that we have witnessed in recent years. The more we use big data to drive our prospecting efforts and move conversion tactics forward, the more we will begin to understand and apply that knowledge to future campaigns and strategic initiatives.
I think the most exciting aspect of this big data evolution is how it changes with us. As our depth of knowledge and understanding improves, we are taking advantage of entirely new types of data to improve not only our own capabilities, but also the experiences of our prospects and customers – a true win/win proposition.
Brian Giese is a founder of True Influence, the leader in Data-as-a-Service (DaaS) business intelligence and demand generation services for marketers. True Influence is an innovator in combining content, data, and technology to allow Fortune 500 companies and SMBs a competitive edge by enabling them with the tools to pre-empt competitors in the marketplace.
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