How Is Predictive Intelligence Driving Change in the World of B2B?

One of the biggest trends that has recently emerged in the B2B world over the past year or two is predictive intelligence. A practice that was still in its infancy as recently as 2014, predictive intelligence is now increasingly regarded as essential for B2B marketers, and a growing number of companies are making or planning strong investments in predictive analytics and related technologies. Driven by more affordable and readily available technologies, an ever-increasing volume of data, and the decreasing efficacy and ROI of traditional techniques, predictive intelligence is on the verge of bringing massive change to B2B marketing and operations.

What is predictive intelligence?


Often used interchangeably with similar terms like predictive analytics, predictive marketing, or predictive recommendation, predictive intelligence is broadly defined by organizations such as McKinsey as the application of mathematical models to accurately predict the probability of a given outcome. In the context of B2B marketing, this can be broken down into a three-step process: data on consumers’ behavior and actions is collected from a variety of sources; algorithms distill and interpret the data and create a set of predictions; and finally, rules are developed based on the predictions to guide the delivery of relevant communications and offers to consumers with the goal of achieving specific business outcomes.

Why is predictive intelligence becoming so widely used?

Alongside the significant growth of inbound and content marketing efforts in recent years—which focus on the creation and distribution of quality, relevant, value-added content to strategically attract and retain a defined target audience—a surprising parallel development has unfolded: it’s getting harder to tell what type of consumer behavior truly indicates an intent to buy.

We can understand this more clearly by jumping back to the B2B marketing world of a decade ago, when marketing qualified leads (MQLs), or leads most likely to become customers as determined by lead intelligence, were the standard for assessing B2B marketing performance. Around this time, if a prospect went through the process of filling out a multi-field form in order to download a white paper or watch a webinar, that behavior alone was enough to transform them almost automatically into a sales qualified lead (SQL), or a lead that has displayed the intent of becoming a buyer.

However, consumers today are more educated and have a greater appetite for content than ever before, but this can no longer be taken for granted as a clear indicator of buying intent. For example, before company managers today decide to make a purchase from a technology vendor, they are able to review industry reports and analyst briefings on the vendor, study technical and product reviews, get product questions answered on community boards, and even get a free trial of the product they are interested in. Despite this high level of B2B content consumption, it’s no longer possible to assume that this manager has as good as made the purchase. In recent years, B2B companies have increasingly had to correct their long-held assumption that higher levels of content engagement automatically drive similar levels of growth in marketing-driven revenue.

This is where predictive intelligence enters the picture. Due to its machine learning and data mining capabilities, predictive analytics do a much better job of analyzing and identifying the indicators that in turn help B2B companies make more informed decisions about how to target particular categories of leads and turn them into customers. Whereas traditional techniques simply helped companies build a target account list based on prospects’ similarities to existing customers, predictive intelligence goes beyond that into examining real activity data and pinpointing what actually signals that leads will buy in the future.

Specific use case examples

Some of the most common scenarios where predictive intelligence is being deployed in B2B marketing today include:

  • Prioritizing SDR follow-up—Sales development teams have struggled with the question of how a limited workforce can sort through and prioritize the thousands of net-new MQLs that increased content consumption on any given day can yield. In the traditional approach, teams are often reduced to working with extremely basic criteria like a company size’s or installed technology. Predictive intelligence helps companies to find accounts which are a better fit through more targeted criteria and can even support the continuous improvement of a company’s market-defining filters by using signals from other digital ecosystems.
  • Incorporating intent into the scoring model—Firmographic data filters can help B2B companies identify prospects that are a good profile fit, but provide no insight into where these prospects may be at in terms of timing or buying stages. Predictive intelligence solutions can leverage third-party data from B2B publisher networks to make sense of the true intent behind particular online behaviors, thus helping companies drive conversion rates by not only approaching the right prospect, but by approaching them at the right time.
  • Doing more with less—High-budget B2B companies have been discovering that simply throwing more money at low conversion rates is not usually the way to solve the problem. Predictive intelligence helps these firms to operate more cost effectively by removing unworkable prospects from the equation, prioritizing leads for follow-up, and routing high intent prospects on the cusp of an active buying cycle to the firm’s strongest closers.