How can AI improve segment definition?

AI improves segment definition by finding patterns humans miss. Machine learning models can analyze thousands of behavioral signals, identify correlations between actions, and surface clusters that would take humans weeks to discover manually.

Predictive modeling lets AI define segments based on future behavior rather than past actions alone. Instead of grouping everyone who purchased last month, AI can identify subscribers most likely to purchase next month, even if their past activity looks unremarkable.

Dynamic clustering creates segments that update automatically as behavior changes. Rather than static rules, AI groups subscribers by similarity scores that recalculate continuously. This means segments stay fresh without manual intervention.

AI also handles multivariate complexity. Humans struggle to hold more than a few variables in mind when defining rules. AI can weigh dozens of factors simultaneously, including engagement cadence, content preferences, session depth, and purchase timing, to create nuanced groupings.

Platforms like Braze, Klaviyo, and Salesforce Marketing Cloud offer AI powered segmentation features. Custom models can be built using Python libraries like scikit learn and integrated via API.

AI is the cartographer that maps territories invisible to the naked eye. It reveals subscriber landscapes that manual segmentation cannot see.