How to Take the Headache Out of B2B Digital Targeting

Seventy-four percent of firms say they want to be data-driven. More than 90 percent of companies say they are using (or trying to use) customer data to segment audiences for better targeting. But very, very few are getting the outcomes they want. A mere two percent of senior executives responding to a recent Economist Intelligence Unit (EIU) survey believe they have achieved “broad positive results” from their digital analytics investments. Whoa. There’s a pretty serious disconnect happening somewhere in there. Despite investing heavily in data analytics and striving to implement more sophisticated digital targeting models, they’re falling woefully short.

The Core Problem: Disconnected, “Dirty” Data

We asked Eric Duerr, CMO of Rocket Fuel, makers of a powerful programmatic marketing platform, what’s going on. His observation has been that “dirty” data—outdated, inaccurate, poorly mapped and disconnected data scattered among systems—is holding many enterprises back in their efforts to implement more advanced targeting methods. They simply aren’t ready to make effective use of sophisticated predictive or anticipatory targeting models because outcomes can only be as good as the data fed into the tools. Conceivably, then, a marketing team could be practicing advanced analytics and still miss the mark with their targeting.

Generally, B2B marketers master digital targeting only when they reach full maturity in the way they use data. Are they using it to turn data into information, information into knowledge or knowledge into understanding? Another indicator of marketers’ targeting effectiveness is the type of (clean) customer data being used: Are they using behavioral, predictive and/or anticipatory data?

The answers to these questions help determine whether a marketing team’s targeting efforts are standing, walking, jogging or sprinting.

 

For context, behavioral data provides a snapshot of what has already happened or is happening right now, answering the question “What happened?” Behavioral analytics give you valuable information about customers’ actions and preferences and tell you where you’re at today.

Predictive targeting goes a step beyond that by taking large amounts of customer firmographic information, behavioral data and other inputs and using it to make educated guesses about what customers are likely to do and want next based on their past behaviors and the decisions of other buyers similar to them.

B2B companies that master a targeted set of digital capabilities generate eight percent more shareholder returns and a revenue compound annual growth rate (CAGR) that is five times greater than the rest of the field.
McKinsey & Co.
 

The next level of sophistication—the “Holy Grail” according to Alex Schwarm, Senior Director of Marketing Analytics Product Strategy at Dun & Bradstreet—is anticipatory modeling, which may be used in combination with behavioral and predictive methods. Schwarm says anticipatory modeling  synthesizes a very broad swath of internal and external inputs and actually “learns” from trends to identify instrinsic state changes in companies. This is how you can know what  B2B customers will need, even before they do. With an anticipatory model, you can proactively target your efforts toward a future state as well as react to previous states.

As an example of anticipatory targeting, Schwarm cites Dun & Bradstreet’s Material Change™ Segmentation predictor, which can, among other things, indicate if a company is moving into growth mode, stable mode or decay mode. That information in turn can help to select the most promising account to build relationships with, thereby maximizing the return on marketing campaigns.

But he cautions that this is highly nuanced information and requires a high level of sophistication to apply it effectively. For instance, a company that is downsizing or shrinking seems like a poor target for B2B sales, but it may in fact be the perfect target when marketing a product that helps make up for lost personnel by automating some employee processes.

Use this maturity model to not only assess where you are today but decide where you want to be in the next few years. Then you can fill in the steps between.

You Have to Walk Before You Can Run: Overcoming Inaction

In terms of digital targeting maturity, some B2B enterprises are standing still due to dirty data or organizational passivity. But most marketing teams are at least shambling along, using behavioral customer data analytics to some degree to inform business decisions, target audiences and evaluate the success of their initiatives after the fact. But very few are sprinting—or even jogging—into predictive or anticipatory targeting. Why not?

Aside from the obvious issue of organizational silos that every large business faces, there’s the subtler issue of risk exposure causing inaction. Seventy percent of global executives believe that data and analytics will expose them to reputation risk. This explains why so many firms fail to take steps to achieve a more sophisticated digital targeting maturity level, despite the fact that 79 percent of top-performing marketing teams currently use predictive intelligence, and 49 percent report extensive use. Executive leadership needs to be on board, and you’re going to need more than just the CMO to get things moving.

Interconnectedness—sharing customer data, content and brand assets across marketing, commerce and service functions to enable real-time, personalized and relevant interactions—is critical to being able to build predictive and anticipatory targeting models. So even though CMOs increasingly “own” customer data and analytics in many organizations, along with the predictive and anticipatory technologies that connect analytics to action, they must work closely with other executives to build and maintain an organization-wide data strategy that captures, maintains and activates customer data from every channel and touchpoint throughout the buyer's journey.

With a strong data strategy in place, collaboration among executive leaders and a clear plan for activating data with predictive and/or anticipatory modeling, you will eventually be able to take off running and get out ahead of your customers’ needs and expectations.

 

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