Why utilizing both deterministic and probabilistic data can provide added context about who your prospective buyers are and the best ways to engage them.
As a modern marketer, you operate in a world brimming with technology and advanced analytics. You’re expected to be able to accurately target your customers, knowing exactly who they are and what they need. But when it comes to navigating through the seemingly depthless oceans of customer data, there are two schools of thought on which type of data set will be most effective: deterministic or probabilistic.
Until now, most digital marketers have been forced to rely on probabilistic data to fuel their online strategies. That is, they use proxy models to define targetable prospects. Unfortunately, probabilistic data can be inexact if proxies are based on incorrect assumptions.
Anchored in Truth
According to a recent article by Connexity, “Deterministic data tracking has long been considered the gold standard of identifying consumers; the term ‘deterministic’ refers only to data that is verified and true.” It is arguably the more accurate of the two data types because it matches specifically to an individual, building on inputted information to empower marketers to deliver finely tuned targeted advertisements or offers.
Deterministic data is highly valuable because of its accuracy. It can be pulled from contact request forms, content download forms, social media channels and e-commerce purchases. While end-users don’t always realize they are providing such data for these purposes, savvy individuals can be resistant to providing the information required to enable the collection of deterministic data. The data that is obtained from direct behaviors and unique identifiers is far more useful than an estimate – which basically is what probabilistic data is rooted within.
A Vast Ocean of Data
Probabilistic data is pulled from a much larger group of data sets to create a buyer persona that is likely to provide relevant, targeted marketing – but not for certain. While probabilistic data is constructed in more generalized terms, it enables marketers to build out a larger, broader campaign more efficiently. The potential downfall is that there will be a greater likelihood of missing intended audiences, as well as including those who are not the right audience for a specific offer.
Basing your efforts solely on probabilistic data also puts you in a position where assumptions must be made, and that is always tricky to navigate. For example, a man is shopping on his computer for a pair of boots to give to his wife for her birthday. Once he’s ordered the boots, he’ll begin to receive advertisements for more boots similar to the ones he’s just purchased, in addition to related items that he is unlikely to buy.
Rather than serving ads to him based on factual information obtained from him directly, brands are making guesses based on one purchase and a potential likelihood to buy more, as opposed to a known fact. That means those ads are missing the opportunity to position a product he would be more likely to purchase. Spending a significant amount of your budget on ads that may not be reaching the right audience at the right time can be a waste of marketing dollars, and will frustrate the user as well.
Choosing a Course
You may find yourself at a crossroads in this vein. Should you work with deterministic data or probabilistic data? Probabilistic data can be unreliable, but deterministic can be much harder to scale. The correct answer is – you guessed it – both.
According to Allison Schiff of AdExchanger, “There is also a growing trend around data companies like Oracle adopting a blended approach in certain cases, using a combination of probabilistic to complement their deterministic matching capabilities in an attempt to reach the scale of players like Facebook and Google. Although LiveRamp, for example, relies on deterministic data to power its Customer Link product, it leverages probabilistic matches for clients who want additional reach, hence its use of Drawbridge’s Connected Consumer Graph.”
As you can see, both deterministic and probabilistic data are necessary to fuel these modern marketing initiatives. Leveraging only one or the other will likely leave you at a distinct disadvantage. Utilizing both models – with reasonable expectations for each – can provide added context about who your prospective buyers are and the best ways to engage them. As Franklin D. Roosevelt once said, “A smooth sea never made a skilled sailor.” You have to test new methods and not be afraid to fail.
Whichever way your marketing organization intends to initiate a campaign, deterministic data can provide innumerable benefits. Through the collection and aggregation of information, you can ensure that the data you’re using is qualified, thereby increasing your potential for success. That means you’ll have verified addresses for email campaigns, accurate purchasing habits for targeted ads and appropriate phone numbers to reach business decision makers. This approach makes for smooth sailing and an increase in your data management and programmatic advertising ROI.
At Dun & Bradstreet, we leverage deterministic data that is anchored in and built for B2B marketing. We gather data on companies and contacts from verified sources that have been vetted for quality. It’s real business data sourced from real business people – collected, aggregated, edited and verified daily. Meaningful indicators are extracted and harmonized from over 30,000 global sources into a single, comprehensive set of business profiles. This data can be used in conjunction with first-party data you’ve collected, as well as behavioral data you’ve tracked, to create online segments and audiences that balance these two approaches.
To learn more about how Dun & Bradstreet can help you obtain and manage deterministic data for your organization, click here.