Customer segmentation divides your customers into groups that behave similarly, such as frequent low-spend visitors, big seasonal buyers or lapsed customers. Each group can then be treated differently in marketing and service.
Traditional segments use simple rules like recency, frequency and spend (RFM). AI clustering goes further, finding groups in the data you did not define in advance by looking at products bought, channels, timing, discount use and location. Segments update automatically as behaviour changes.
Retailers use segments to send targeted emails, design loyalty tiers, pick audiences for paid ads and decide who to win back. Hospitality venues use them to reward regulars, invite local groups to events or promote quiet-night offers to the right guests.
Segments are only useful if you can act on them, so avoid creating dozens of groups that nobody uses. Data must be linked to the same customer across the POS, web and app. Check that you have consent for the marketing you plan and that segments do not unfairly exclude or target vulnerable people.
A handful of well-chosen segments usually delivers more value than complex modelling nobody uses. Revisit segments every few months, since customers move between groups and offers that worked last season may need updating.