What Targeting Approach Should I Use For My B2B Campaign?

The Stark Difference Between B2C and B2B Advertising Blog Series

Business-to-business (B2B) programmatic advertising has evolved exponentially over the past few years. No doubt, many changes are happening at this very moment.

As a marketer or agency looking to launch a campaign, you can’t wait for the dust to settle, however. With a B2B sales cycle that is likely lengthy and involves multiple decision makers, you need to make decisions in the here-and-now, like deciding on your targeting approach. Audience definition starts with your first-party data – and sometimes that’s enough to point you in the right direction. But if you need to add third-party data for greater insights and to fill in gaps, you have some different options to choose from. 

As we discuss in The Stark Difference Between B2C and B2B Advertising, written in conjunction with Oracle Data Cloud, there are positives and negatives to any one method of activating a B2B marketing campaign. 

Simply put, deterministic data is confirmed information, and probabilistic data is inferred information.


Starting a B2B Marketing Campaign

The first step is to clearly understand your goals and audience targets. Is your campaign aligned to an industry or should you be targeting fast growing companies? Do you need to build awareness or drive leads? Are you targeting a specific person at a company or many people? Answering these types of questions can help you evaluate your options.



While working through your decision tree, the first distinction is the difference between deterministic and probabilistic data. Simply put, deterministic data is confirmed information, and probabilistic data is inferred information. Dun & Bradstreet puts equal effort into making sure both are high quality; and analytics take that data and create models that predict future statuses based on current facts and activities.

Both of these data types can be broken down further:

  • Deterministic into firmographic (company) and demographic (contact) data
  • Probabilistic into intent-based targeting (past activity) and predictive targeting (similarity to others)

The advantages of having Probabilistic & Deterministic data are:

  • Firmographic targeting can help you reach the right businesses based on an ideal customer profile. Industry, location, and size are all potential factors for you to use when narrowing down your targets.
  • Demographic targeting can get messages in front of the right decision makers based on their role, seniority, and other characteristics. By targeting specific roles, you can create more resonant messaging.
  • Intent-based targeting can help reach audiences that are interested in particular subjects, topics, or products in near real-time. For example, leverage information about who might be in-market for your products based on past online activity.
  • Predictive targeting can help identify audiences based on how they may act in the future, getting messages across earlier in the buyer journey, before there is an explicit need. Dun & Bradstreet uses deep knowledge of offline information and adds a layer of analytics, offering targets based on company growth, financials, and spending power.

Beware of these B2B insights issues:

  • Firmographic targeting can be narrow in scale and impressions which can produce limited results depending on the goal of the campaign. Understanding your customers and past campaign results will help in creating the right target set.
  • Demographic targeting can exclude key influencers involved in a B2B purchase decision. Be careful not to leave out lower-level influencers.
  • Intent-based targeting’s foundation consists of behaviors that can change rapidly, and messages can be irrelevant if data is not leveraged near real-time. It can also provide false positives such as students or competitors doing research, which can impact campaign ROI.
  • Predictive targeting can be based on inputs that are irrelevant to campaign objectives. Always check how models are built to better understand these data sets and how best to message.

By understanding the goals of your campaign, the messaging, and the tactics, you should be able to back into the targeting approach that best meets your needs. The next step is investigating the segments that are available to you, their reach and their cost. If you have questions, the Oracle Data Hotline is a resource that can help you navigate your options.

Now that you’ve gone through this yes/no flowchart, let me end by saying that there is not always an either-or. You can choose more than one option – predictive + demographic, for example. If possible, find a way to measure the results based on these differences. 

Read The Stark Difference Between B2C and B2B Advertising to learn more about planning, executing, and reporting for your next B2B programmatic campaign.