“When Gartner first wrote about predictive lead scoring in August 2014, the practice was still in its early stages.” In their March Tech Go-to-Market report, Gartner notes that “predictive lead scoring is now a “must have” for B2B technology marketing leaders with high volumes of leads from inbound channels and events.” We believe this change in analysis is driven by widespread adoption and planned future investment in predictive intelligence. It also speaks to the live implementation of predictive tools and the tangible economic impact organizations are seeing.
The Birth of the Educated Buyer
Surprisingly, the appetite for predictive intelligence is fueled by the success of inbound and content marketing efforts, not their failure. Over the past decade content marketing has improved the buying experience of B2B decision makers. Today, before making an investment in new strategies, technologies and skill sets a manager can educate herself by looking across analyst briefings, industry reports, white papers, case studies and technical reviews. She can get a sense of her counterparts experiences at other companies by looking at product review sites and can even have her questions answered by peers on community boards. Finally, in many cases, she can even take a particular product or solution for a spin with the rise of free trials and freemium business models.
Content marketers have succeeded in creating a culture of content consumption among professionals in specialized fields. Industry magazines and continued professional education is no longer just for doctors and lawyers. The unforeseen result has been that with the rise of B2B content consumption it is now much harder to understand what type of behavior and engagement is truly indicative of buying intent.
The Death of the MQL
On the demand gen side, over the past decade marketing qualified leads or MQLs have become the standard for measuring B2B marketing performance. In 2006 getting a prospect to fill out a multi-field form to watch a webinar or download a piece of collateral might have been as good as taking them through a full sales qualification process. Today, even if an individual has engaged with multiple pieces of content and requested to be contacted, it is far from a certainty that they are in market to make a buying decision and not just self-educating themselves on a topic of personal interest.
This rise in content consumption has created the problem of the MQL trap: the assumption that higher levels of content engagement will reflect similar growth in marketing-driven revenue. Companies have discovered the fallacy of this approach and many have implemented new metrics like sales accepted leads (SAL) or have simply moved to focusing on revenue growth as an indicator of marketing success.
The Rise of Predictive Intelligence
Predictive intelligence addresses this dual problem by allowing B2B marketers to make sense of complex buyer behavior and allows B2B sales professionals to sift through unmanageable queues of prospects to find those in an active buying cycle. Here are three of the most common and effective use cases for predictive implementation today.
- Prioritizing SDR Follow-Up
The increase in content consumption has created a problem of “too many” for sales development teams. When any given day might mean thousands of net-new marketing qualified leads, how do you prioritize a limited workforce? Many organizations have worked on identifying account characteristics and trigger events that help triage SDR follow-up. For some company size and installed technology might be enough to qualify or disqualify a prospect.
At its most basic level, predictive intelligence helps organizations find and bubble up accounts that are a fit for their products and services. At more advanced stages, predictive intelligence also supports the continuous improvement of the filters that define a company’s market by incorporating signals from outside their digital ecosystem. This means finding prospects exhibiting interest in your products, even if they do not fit your current definition of a tier 1 target account.
- Adding Intent To Your Scoring Model
Improving your firmographic filters can bring appreciable gains in MQL to SQL conversion rates as well as deliver real impact on your pipeline. The problem with stopping at profile fit however, is that it gives your marketing and sales team no insight into the timing and buying stage of your prospects. In many cases you can be calling on (and alienating) decision makers who are not yet in an active buying cycle or conversely reaching prospects who are so far down the road with one of your competitors that your sales team will be playing catch up from day one.
Gartner puts their finger on this issue when they say that “traditional lead scoring essentially uses engagement as a proxy for intent, despite often providing only a loose correlation.” We feel the problem with this approach is that while downloading white papers and visiting web pages is indicative of something, there is no guarantee that it is a sign of buying intent.
Only predictive intelligence solutions that incorporate third party data from B2B publisher networks and analyze it through data science and predictive modeling can begin to get at the true intent behind certain online behaviors. Firmographic and engagement scores from your marketing automation coupled with intent information can help drive conversion rates up not by percentage points, but by orders of magnitude.
- Do More With Less
For some happy marketers, budget isn’t the problem. The rub is that it’s also not the solution. Throwing more money at low conversion rates won’t necessarily prove successful. That’s why, for large enterprises, predictive intelligence is of paramount importance. Being able to prioritize leads for follow up and immediately remove unworkable prospects can help save serious money in sales qualification costs. On the other hand, being able to uncover and route high intent prospects just entering an active buying cycle to your strongest closers can help shorten sales cycles and raise average deal sizes.
Regardless of how you choose to first implement your predictive intelligence solution, the experiences of marketing and sales organizations that have seen success make it is abundantly clear that having your sales team involved in a meaningful way early and often is key to success. Predictive intelligence will challenge you to rethink your lead scoring rubric, lead routing flows and engagement insight connect marketing to sales.
If you’re interested in learning more about how 6sense drives value for our customers, check out how Cisco implemented predictive intelligence in order to successfully grow pipeline, increase MQL-to-SQL conversions and find net-new lead.