According to a recent paper by Chris Miglino, co-founder and CEO of SRAX, an advertising tech company, as online users bounce from desktop to mobile, cross-device tracking is now more than ever a crucial component for any digital marketing strategy.
But to accurately pinpoint where your audience is, says the report, deterministic and probabilistic data is often used as the foundation for cross-device tracking.
Deterministic data is personal information shared by filling out a form, taking an online survey, or making another purposeful action. It’s considered high-quality data because it is verified and true, coming directly from consumers. This data can be used as the base for content personalization and product recommendations, a tactic most ecommerce sites use, as well as a key role in cross-device targeting.
For example, if you log into Facebook on desktop, and later log into your account via mobile device, marketers can use your login data to link your devices. This means marketers can deliver personalized ads to you no matter what device you’re on.
The downside, says the report, is that deterministic data can be tough to scale. And the two most scalable deterministic data sets are trapped within Facebook and Google’s walled gardens and not available for external use.
Probabilistic data, also known as inferred or modeled data, makes assumptions about users based on online activities and behavior. Meaning they are probable matches, as marketers can assign individuals to specific categories depending on what they searched, read, watched or bought. This method is far more complex because it requires algorithmic calculations.
For example, a marketer serves an ad for a family vacation package to a desktop connected to a certain WiFi residential address. If later a mobile device connects to the same WiFi, it becomes highly probable that the device is part of the household. And the same ad gets served to that device to reach another household member who may influence the buying decision.
The downside to using probabilistic data is that there is a greater likelihood of missing intended audiences. For instance, if a husband buys a pair of earrings online for his wife’s birthday, probabilistic data will tell you that he’s probably interested in other styles of earrings. And marketers will use this data to serve him ads displaying other earrings and even matching necklaces, all items that he’s unlikely to buy.
Which one is better?
Deterministic data may seem like the best choice because it’s more accurate and you want to get as close to your audience as possible. However, accuracy without scale won’t get you very far. It’s difficult to obtain deterministic data sets large enough to target audiences at scale, and probabilistic data provides that element of scale.
The key is to align your data sets with your objectives, says Miglino. If your goal is to target actual buyers of your product, then a deterministic data set would be the option of choice. But, if your goal is to target people who might be interested in your product, then probabilistic data will give you greater scale and potentially better conversions.
For instance, if you’re a CPG brand, you might lean towards deterministic data because your consumers follow predictable purchase patterns. But, if you’re a car brand, your customers will not follow predictable purchase patterns, says the report. In this case, use data that reveals a highly probable path to purchase based on other customers in the same position.
Marketers are also taking to a hybrid approach. A report by market research firm The Relevancy Group, found that 30% of surveyed marketers planned to use a combination of deterministic and probabilistic datasets in 2016. Layering factual data with inferred data provides the best of both worlds, accuracy and scale, concludes Miglino.
For additional information on this subject, please follow Mr. Miglino on Twitter@chrismiglino