A churn prediction model is presented that uses both behavioral data as well as user characteristics to predict whether a given user will churn (i.e., stop using) an application. Initially a training set of user interactions can be correlated to a churn probability value for various sequences of user activity. Then, as regards a real time user, user actions in navigating through the app may be recorded, and this information can be used, in addition to user characteristics, to predict the probability that this user will churn, thus implementing in a “nip churn in the bud” approach (or, the inverse, remain loyal and continue to use the app). In some embodiments, a partial set of user actions can be identified as subsequences of known churn sequences. To users performing those subsequences of activity, a real time message, offer or promotion may be sent so as to influence them not to churn. In exemplary embodiments of the present invention, user data may be uploaded from a user's device to proprietary or cloud servers. Churn analysis, or a more detailed churn analysis, using up to the minute collective data for the given app, may, for example, be performed on those servers.
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