Summary

• Accurate identity resolution is a prerequisite in an increasingly cookie-free tech space.
• Deterministic and probabilistic matches prioritize aspects like precision vs. scaling.
• Making informed decisions based on the brand’s unique marketing goals is the ideal route.

The tech realm has undergone many upheavals, and the impending disappearance of cookies is among them. This imminent exit has led to an ocean of what-ifs. 

• What if accurate customer tracking is no longer possible?
• What if delighting customers with tailored CX becomes more challenging? 
• What if the most dreaded predictions come true? Tracking cookies—gone; local storage—removed; IP addresses—blocked.

It’s not all doom and gloom if marketers are prepared for these questions. They need to reformulate an audience identification strategy that works best for the new trends.

In this article, we discuss classic identity resolution methods, including deterministic and probabilistic matching, as well as their respective strengths and limitations. We also take a look at why brands should fuse the best of both approaches to identify audiences at the segment-of-one level across touch points.

Deterministic matching: Opting for accuracy

Deterministic matching maps customer identity-data relationships at very high accuracy levels (usually at least above 80%) using personally identifiable information (PII) given by customers such as mobile number, email address, and key demographic details like age.

Apart from basic personal information, deterministic data can take on other forms.

Customer data includes:

• lifetime value, purchase, and browsing patterns 
• sentiments towards the brand
• product or service affinity 

Company data includes industry, content consumed, company size in terms of revenue, and employees, among other attributes.

Probabilistic matching: Casting a wider net

Customer databases will have thousands (or even millions) of incomplete audience profiles. This is commonplace when online channels are visited without a unique identifier, resulting in unknown and duplicated audience records. Another probable cause can be misspellings during manual data entry.

Enter probabilistic matching.

Probabilistic matching uses statistical modeling to unify data into audience profiles, at a specific confidence level deemed satisfactory by the brand or the solution they employ.

Here’s an example.

Derrick is browsing sports on two separate devices that are connected to the same Wi-Fi network, a probabilistic algorithm ties that data to a single audience profile. 

If, however, Derrick uses a third device on the same Wi-Fi network and begins browsing for vacuum cleaners instead, this data may be tied to a separate audience profile. Alternatively, the algorithm may decide that “Derrick Gates” is the same person as its nickname, “Drick Gates,” given enough common attributes.

However, when a wild “Derek Gates” appears, the algorithm might wait until more data is available to decide whether the profiles should be merged or not.

A comparison: Precision vs scalability

Probabilistic matching accelerates the expansion of customer profiles in the database. This means the brand can expect a broader campaign reach and target a larger set of audiences who might purchase a product, for instance. 

The drawback of probabilistic matching, however, is that it falls short in data accuracy and one-to-one journey mapping, where deterministic matching performs better.

As it stands, deterministic matching occurs less frequently than one might think. It is more likely for anonymous users to interact with brands and then disappear without a trace, especially as cookies cease to be the compass for brands.

Our stance

It is not imperative that we proclaim one approach to be necessarily superior to the other. The golden mantra is:

It depends on your goals. 

Instead of upholding a false dichotomy, assess their relevance to your unique CX and marketing objectives.

Deterministic matching will be more useful when you aim to create bespoke content, offers, and interactions that add up to a segment-of-one customer journey. If your goal is to expand campaign reach or create industry-specific content that is more modular and automated, then probabilistic matching will come in handy.

Moving forward

Which approach is best for cross-device, omnichannel measurement? How can the brand prepare for the demise of traditional customer tracking methods? These are the questions marketers must ask themselves while searching for solutions that balance the strengths of both matching methods to deliver omnichannel audience identity resolution at scale.