Abstract: A system and method for creating a model for predicting and reducing subscriber churn in a computer network. The method including: for a predetermined time period: retrieving traffic flow data per subscriber for a plurality of subscribers in the computer network; determining at least one metric per subscriber from the traffic flow data; determining at least one systemic feature associated with the plurality of subscribers; and storing the at least one amalgamated metric and feature; on reaching the predetermined time period create the model by: analyzing at least one metric and at least one feature for the predetermined time period; predicting, per subscriber, whether the subscriber is going to churn within a churn period in the future based on the analysis; validating the prediction by determining whether the subscriber actually churned during the churn period; and creating the model based on the validated predictions.

Abstract: A system and method for creating a model for predicting and reducing subscriber churn in a computer network. The method including: for a predetermined time period: retrieving traffic flow data per subscriber for a plurality of subscribers in the computer network; determining at least one metric per subscriber from the traffic flow data; determining at least one systemic feature associated with the plurality of subscribers; and storing the at least one amalgamated metric and feature; on reaching the predetermined time period create the model by: analyzing at least one metric and at least one feature for the predetermined time period; predicting, per subscriber, whether the subscriber is going to churn within a churn period in the future based on the analysis; validating the prediction by determining whether the subscriber actually churned during the churn period; and creating the model based on the validated predictions.

Abstract: A system and method for creating a model for predicting and reducing subscriber churn in a computer network. The method including: for a predetermined time period: retrieving traffic flow data per subscriber for a plurality of subscribers in the computer network; determining at least one metric per subscriber from the traffic flow data; determining at least one systemic feature associated with the plurality of subscribers; and storing the at least one amalgamated metric and feature; on reaching the predetermined time period create the model by: analyzing at least one metric and at least one feature for the predetermined time period; predicting, per subscriber, whether the subscriber is going to churn within a churn period in the future based on the analysis; validating the prediction by determining whether the subscriber actually churned during the churn period; and creating the model based on the validated predictions.

Abstract: A method for determining subscriber experience in a computer network including: defining a plurality of features influencing subscriber experience; initializing a weight for each of the features of the plurality of features; determining a feature value for at least one subscriber for each of the plurality of features; determining a subscriber score for the at least one subscriber based on the feature values and the weight of the feature; determining at least one subscriber with a subscriber score outside a predetermined threshold; and performing a subscriber response action for each subscriber with the subscriber score outside of the predetermined threshold.

Abstract: A system and method for creating a model for predicting and reducing subscriber churn in a computer network. The method including: for a predetermined time period: retrieving traffic flow data per subscriber for a plurality of subscribers in the computer network; determining at least one metric per subscriber from the traffic flow data; determining at least one systemic feature associated with the plurality of subscribers; and storing the at least one amalgamated metric and feature; on reaching the predetermined time period create the model by: analyzing at least one metric and at least one feature for the predetermined time period; predicting, per subscriber, whether the subscriber is going to churn within a churn period in the future based on the analysis; validating the prediction by determining whether the subscriber actually churned during the churn period; and creating the model based on the validated predictions.