How does AI put to bed the debate over whether probabilistic or deterministic data works best? In this guest article, James Milne, SVP Business Development at data and tech company Epsilon argues that AI has rendered the question itself redundant, transforming two competing methods into a single, more powerful identity strategy.
For too long, brands have been forced to choose sides. When it comes to identifying and reaching audiences online, two approaches have dominated the debate: probabilistic and deterministic – and marketers have been divided ever since.
The probabilistic approach uses signals including device attributes, browser behaviour, location patterns, and network data to assign likelihood scores for inferring whether different interactions belong to the same person.
It has played an important role for decades, even though it’s ultimately based on guesswork, especially when it comes to scale. Modern models, for instance, can extrapolate lookalikes from relatively small seed sets while ingesting thousands of signals and updating in near real time, delivering targeting and measurement that excel in upper and mid funnel activity.
In the other camp, deterministic matching provides a tangible identity spine based on persistent, unique identifiers. Unlike probabilistic matching, it relies on pseudonymised identifiers that are known to be true, such as logins, hashed email addresses, customer IDs, or consented CRM data. Instead of likelihood, it offers certainty where the data exists.
Enter AI and it’s no longer about choosing sides.
Probabilistic Matching Looks More Compelling Than Ever
If machines can detect patterns across onsite behaviour, context, and first-party data at scale, the argument goes that performance can be maintained or even improved without the heavy lift of capturing and stitching together identity across the open web. This is why the debate feels freshly energised. Better pattern detection and real-time learning make probabilistic methods look more attractive to brands than ever.
On the probabilistic side, AI powers everything from lookalike modelling to propensity scoring and media mix analysis. These systems often train on deterministic data where it exists, then generalise into areas where identifiers are sparse. The result is that probabilistic matching becomes a learning system rather than a static set of rules.
Transparency no longer becomes a trade-off either. Until only recently, probabilistic systems dealt in likelihoods and cohorts, not people, so accuracy could vary by channel, device, and time. When something went wrong, it became difficult to explain why. With AI enabling stronger validation and experimentation, performance is easier to verify.
Certainty Still Wins (Only When Data is Verified)
On the deterministic side, AI helps clean and reconcile first-party data, detect bad identifiers, and infer relationships such as households. It makes identity graphs richer and more durable without sacrificing accuracy.
Deterministic matching, however, only works where strong first-party data exists. If someone is not logged in, or an email address is unavailable, many systems simply fail to match. When advertisers complain about low match rates, it’s usually because they’re trying to force individual channels to line up.
Brands therefore need to stop obsessing over how channels connect and start with the person instead. Create one identity, tie all signals back to it, and keep filling in the picture so recognising and using new signals becomes progressively easier.
In doing so, brands will deliver true one-to-one personalisation. Because the person is reliably known, they can decide if someone should get promotional vs aspirational messaging and avoid wasted impressions on the wrong people.
Where Value is Highest, Proof Matters Most
The person matters where journeys can be longer and more complex, as well as in high stakes use cases like loyalty programmes and lifecycle messaging, where tying past actions and preferences to the individual unlocks greater value.
The 2026 World Cup shows exactly why this matters. Some 34 million viewers in the UK are expected to follow the tournament, according to Epsilon’s recent research, but only half are regular football fans. That mixed audience will take fragmented paths to purchase across devices, screens, and moments – and only brands with the identity infrastructure to stitch that journey together will be able to prove what drove the result.
When powered by AI, deterministic identity brings stability to measurement and incrementality analysis when precision and accountability are non-negotiable. With the research also suggesting three-in-ten fans are more likely to choose a brand that activates during the tournament, the ability to connect exposure to outcome becomes an important difference between a campaign that brands can prove and one they can only hope works.
AI has clearly changed the game when it comes to identifying audiences. Most serious identity strategies now start with deterministic data as the backbone and layer probabilistic methods on top to extend reach and insight. Deterministic identity provides confidence and measurement integrity. Probabilistic models add scale and flexibility.
Used together, they turn small, fragmented datasets into dynamic, scalable audience profiles that can be activated across channels and measured against real outcomes. Brands can know when certainty matters, when inference is sufficient, and build an identity foundation strong enough to support both – and, in doing so, put to bed a debate that AI has finally resolved.
