The Data Difference: Deterministic vs. Probabilistic Methods
Which data is better for digital ad campaigns: deterministic or probabilistic? The savvy marketer will tell you both, and for good reason. Both types of data play a role in helping marketers hone in on their ideal customers with personalized messages at scale.
How do you know when to use each? It depends on your campaign objectives, data availability, privacy compliance, and budget. Let’s dig in.
First, a Refresher
Deterministic data is any data we know is true and accurate because it was supplied by the consumer directly through unique identifiers such as browsing behavior or location data stemming from a mobile device tied to a user.
Probabilistic data is based on probabilities. For instance, marketers may assume that visitors who browse their baby products section of a website are moms (as well as women of child-bearing age). These data points allow for a profile to be built on who a user likely is.
How Deterministic Data is Used
Deterministic data has multiple use cases in advertising. To begin, it can be tied to user-specific attributes and behaviors–like keyword searches, places visited, and much more–ensuring your ads resonate with your target audience by personalizing them based on those attributes. This will drive campaign performance.
You can also use deterministic data to ensure ad relevance through strategies such as geo-fencing. This tactic leverages location data from the consumer’s device, such as a smartphone. Geo-fencing lets you create a virtual boundary around a certain area, such as a retail outlet or an event location. When a user’s device enters or exits this boundary, you can target them with an ad. In other words, deterministic data leverages accurate location information to precisely target ads to people in a specific place.
Deterministic data is vital to identity resolutions. Unifying deterministic customer data across channels provides you with a holistic view of your customers and prospects, as well as gain insight into their customer journey. Identity graphs help provide a clearer picture of a target audience. Simpli.fi’s identity graph adds even more value, serving as a metagraph, or a graph of graphs, uniquely connecting important attributes, interests, and behaviors to help create user profiles that can be targeted with precision for utmost performance.
Privacy is another consideration. First-party data — i.e. data you collect directly from your customers with their permission — is a form of deterministic data. While deterministic, first-party data can be used probabilistically, such as making assumptions about the users who provided you with their data, as well as how they provided it. For instance, a consumer may have provided their contact details to a financial services company, which may assume the consumer has interest in a particular product based on the context of a page that led to the lead form.
While deterministic data is accurate, it’s based on an identifier which means it can be a costly data strategy to implement. In these cases, probabilistic data can help you fill these gaps.
How Probabilistic Data is Used
We use probabilistic data to identify patterns and make inferences about user behavior and preferences. For instance: marketers may infer that people who buy baby toys and clothes are parents. During the holiday season, however, they may infer that such shoppers are grandparents looking for holiday gifts.
Probabilistic methods are vital when you need to reach a wider audience and maximize your audience reach. You can use your deterministic data to create profiles of your customers, and then use probabilistic data to find consumers who are similar to your customers at scale.
An example of a probabilistic data use case is ZTV, Simpli.fi ZIP-code based targeting, which focuses on specific targeting factors (i.e., demographics, household income provided by U.S. Census data) by zip code rather than an individual user’s data. In this scenario, the ZIP-code and U.S. Census data has been stripped of all PII data and is inherently privacy compliant.
The downside to probabilistic data is that you will inevitably reach some portion of users who don’t meet your campaign criteria. That said, the lower cost for the data means you may still be able to achieve strong ROI.
So what’s the right approach? Both can provide value to your campaign. Deterministic data can ensure you reach the right person, but probabilistic data can help you understand more about who that customer is. Deterministic data is all about precision, while probabilistic data is about scale.
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