Traditional segmentation often relies on broad, predefined categories like new visitors, high-value customers, or cart abandoners. While these segments are logical, they’re far too generalized to fully leverage the depth of modern customer data. This workflow uses AI-driven pattern recognition to analyze thousands of data points and create hyper-specific microsegments that reveal hidden customer behaviors.

Instead of manually defining segments, this workflow continuously refines and updates segments based on real-time behavior, purchase patterns, and engagement triggers. For example:

• Mobile Browsers, Desktop Converters: Customers who explore products on mobile but only complete purchases on desktop—especially when a discount is applied.

• Seasonal Big Spenders: Shoppers who spend large amounts exclusively during major sales events like Black Friday or Christmas, with a preference for bundle deals.

• Second-Purchase Premium Upsellers: Customers who are more receptive to premium upgrades on their second purchase after a positive first experience.

By uncovering these specific segments, your marketing efforts can become hyper-personalized, increasing relevance, conversion rates, and long-term customer value.