There are patterns yet to be discovered around designing your classification strategy. The following is an early take of how one might design a strategy.
Uniform Context lets you create a classification taxonomy based on dimensions: audiences, intents, signals, and enrichments. These let you classify visitors based on a specificity spectrum: signals and enrichments are very small visitor actions, and those combine into the aggregates: intents and audiences.
When designing dimensions, consider what top level personas you wish to segment visitors into them. These will be your audiences. Then consider reasons that visitors would visit that cause them to belong to these audiences: these are your intents, and their scores roll up into the audiences. Finally consider the specific signals that visitors have the intent for visiting: these are your signals or enrichments.
In some cases it may be beneficial to also design more and less-specific signals, for example for geo-targeting you might wish to have both a signal for "Country: Canada AND Region: Ontario" as well as a more general "Country: Canada" signal.
Third-party classification tools (CDPs, GeoIP)
Many businesses are already using third-party or in-house classification systems. These can integrate easily into Uniform's classification system by using enrichments (if the external data is some sort of numeric vector like a predicted segmentation from an ML model with confidence values), or quirks (if the external data is a constant value).
This third party data can then participate directly in your intent and audience-based classification taxonomy, enabling seamless personalization with data from all sources.
Uniform Context natively supports quirks data from CDN providers that expose GeoIP data to target visitors by their location.