Predictive segmentation is a marketing technique employed to discern and delineate customer segments based on the anticipated likelihood of specific behaviors, events, or conditions occurring in the future. Typically driven by artificial intelligence and machine learning technology, this approach is automated.

Prominent examples of predictive segmentation encompass segments such as “likelihood to purchase” and “likelihood to churn.” Whereas “likelihood to purchase” aims to categorize customers based on their anticipated propensity to buy your products in the future, “likelihood to churn” identifies segments of users who are more susceptible to discontinuing their engagement with your business.

Marketers equipped with predictive segments can:

Utilize intelligent predictive models to forecast each consumer’s probability of various actions, including purchasing, repeat purchasing, or churning.
Deploy personalized push notifications tailored to engage each customer effectively.
Schedule push notifications to reach the appropriate user at the opportune moment when they are likely to engage and when the business requires it.
Automate customer journeys for individual customers across predefined marketing funnels.
Deliver optimal product recommendations, content, or offers to each customer.
Predictive segmentation streamlines operations by automating the identification and analysis of valuable or high-potential audience segments that warrant targeting. However, while serving as a pivotal capability, it does not independently determine the ideal experience tailored for each identified audience segment. Essentially, while predictive segmentation expedites a marketer’s progress in the workflow by furnishing the right segments to work with, crafting the suitable experiences still entails trial and validation over time through data.