Today’s marketers are expected to accurately target customers while simultaneously knowing vast details about their backgrounds as well as their needs and desires. However, 23% of marketers admit personalization is a challenge due to poor data quality. We can solve this issue by fully understanding data; how it is collected, and what it indicates. A basic differentiator between data types is whether it is probabilistic or deterministic. 

Until recently, digital marketers have been forced to rely on probabilistic data to build their customer retention strategies. Marketers used proxy models to define potential target customers. Conversely, deterministic consumer data relies on first person accounts and has the ability to be verified. It can, however, be limited in scale.

In order to determine which data type would be best to use for your business’ marketing efforts, it’s important to analyze and compare the two prior to making a decision.

Probabilistic data is implied or inferred from past customer behavior. It is the result of applying predictive analytics. These methods attempt to project values or attitudes, or predict future behaviors based on historical information. With this method, we create device relationships through a knowledge base of linkage data and predictive algorithms for an identity graph. 

Devices are also grouped together via device fingerprinting, IP matching, location, Wi-Fi networks, and behavioral and browsing data—using statistical modeling at a given confidence level. These groups can be linked to identities based on predictive algorithms.

Deterministic data, on the other hand, is tied to real-world behaviors. This is valuable when creating personalized marketing messages targeted to an individual customer. Deterministic data is ideal when looking to reach audiences that are interested in particular subjects, topics, or products in real-time. For example, a phone carrier extending upgrade offers would only want to reach customers who own the previous phone model. This type of data can be pulled from contact request forms, download forms, social media, and online purchase orders. The data that is obtained from direct behaviors and unique identifiers is far more useful than an estimate. This is because facts are used to back the data up.

You may find yourself at a crossroads at this stage. Probabilistic data can predict how customers may act in the future, helping to get messages across earlier within the buyer journey. However, customer behaviors can change quickly. Present patterns may not be a solid indicator of future actions. Conversely, deterministic data can help zero-in on the right businesses to target based on an ideal customer profile, but it can be limited in impressions.

While both types of data have their uses, deterministic data is the type of first-person, accurate data that is generally considered the gold standard for analytics. It identifies who a customer is, and what real-life interactions they have had with a brand. Or, in the case of competitive intelligence, with competitors. Probabilistic data is merely a possibility. A common example is found within weather reporting, where a common value is used to predict future weather patterns. This is useful information to have, but hardly accurate enough to be the sole source of decision-making data.

As a location intelligence company, Gravy Analytics processes quality pseudonymous location signals from mobile devices in order to get verifiable and high-quality location data. This provides deterministic data for a deeper understanding of customer behavior including where they go and what events they attend. Overall, this data can provide information about the consumer’s interests and values.

When you’re ready to take a deeper dive into proper audience targeting methods, take a look at Gravy’s thoughts on location-based marketing.