System and method for predicting and reducing subscriber churn

A method for predicting and reducing subscriber churn on a computer network, the method comprising:

• retrieving traffic flow data for a subscriber of the computer network;
• determining at least one subscriber metric from the traffic flow data;
• analyzing the at least one subscriber metric with a model for predicting subscriber churn;
• determining whether the at least one subscriber metric includes data points to be condensed;
• determining at least one systemic feature associated with the at least one subscriber, wherein the at least one systemic feature is based on an access network used by the computer network;
• predicting whether the subscriber will churn during a predetermined churn period, based on the at least one subscriber metric and the at least one systemic feature and a relationship between the at least one subscriber metric, the subscriber quality of experience and the at least one systemic feature;
• and
• if the subscriber is predicted to churn, providing a traffic action on the traffic flow for the subscriber;
• otherwise allowing the subscriber's traffic flow to continue without action.

A system for predicting and reducing subscriber churn on a computer network, the system comprising:

• at least one processor connected to at least one memory storing instructions executable by the at least one processor to implement a plurality of modules comprising:
• a data collection module configured to:
• retrieve traffic flow data per subscriber for a plurality of subscribers in the computer network;
• and
• determine at least one systemic feature associated with the plurality of subscribers, wherein the at least one systemic feature is based on an access network used by the computer network;
• a feature extraction module configured to determine at least one metric per subscriber from the traffic flow data and determine whether the at least one subscriber metric includes data points to be condensed;
• a machine learning module configured to:
• analyze the at least one subscriber metric and the at least one systemic feature for the predetermined time period and determine a relationship between the at least one subscriber metric, the subscriber quality of experience and the at least one systemic feature;
• create a model for predicting and reducing subscriber churn based on the analysis;
• and
• predict per subscriber, whether the subscriber is going to churn within a churn period in the future based on the analysis;
• an evaluation module configured to validate the prediction by determining whether the subscriber actually churned during the churn period;
• and
• a reporting module configured to perform a traffic action based on the prediction.