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SYSTEMS AND METHODS FOR PREDICTING SUBSCRIBER CHURN IN RENEWALS OF SUBSCRIPTION PRODUCTS AND FOR AUTOMATICALLY SUPPORTING SUBSCRIBER-SUBSCRIPTION PROVIDER RELATIONSHIP DEVELOPMENT TO AVOID SUBSCRIBER CHURN
SYSTEMS AND METHODS FOR PREDICTING SUBSCRIBER CHURN IN RENEWALS OF SUBSCRIPTION PRODUCTS AND FOR AUTOMATICALLY SUPPORTING SUBSCRIBER-SUBSCRIPTION PROVIDER RELATIONSHIP DEVELOPMENT TO AVOID SUBSCRIBER CHURN
System and method for predicting subscriber churn include machine learning algorithms for classifying the subscriber's decision to stay with the present subscription provider or to switch to a new provider. The machine learning algorithms may include a logistic regression/ neural network for modeling churn propensity in subscribers. The churn risk analysis systems and methods may identify a group of subscribers most likely to churn. Further, the churn risk analysis systems and methods may identify a group of subscribers least likely to churn. The identified subscribers may be presented to a representative of the subscription provider, for example through a user interface.
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