SUBSCRIPTION CHURN PREDICTION

摘要

A churn prediction system includes at least one hardware processor, a memory including a historical sample set of subscriber data, and a churn prediction engine executing on the at least one hardware processor. The churn prediction engine is configured to identify the historical sample set, identify a set of attributes, automatically select a subset of attributes based on an information gain value, generate a decision tree by recursively generating nodes of the decision tree by computing an information gain value for each remaining attribute of the subset of attributes, identifying a highest attribute having the highest information gain value, and assigning the highest attribute to the node. The churn prediction engine is also configured to receive target data for a target subscriber, apply the target data to the decision tree, thereby generating a churn prediction for the target subscriber, and identify the target subscriber as a churn prediction.

主权项

1. A churn prediction system comprising:
at least one hardware processor; a memory including a historical sample set of subscriber data; a churn prediction engine, executing on the at least one hardware processor, configured to:
identify the historical sample set of subscriber data in the memory;identify a set of attributes within the historical sample set;automatically select a subset of attributes from the set of attributes based on an information gain value of each attribute of the set of attributes;generate a decision tree based on the selected subset of attributes and the historical sample set, wherein generating the decision tree further includes recursively generating nodes of the decision tree starting from a root node, each non-leaf node of the decision tree representing an attribute from the subset of attributes, generating a first non-leaf node of the decision tree includes:
computing an information gain value for each remaining attribute of the subset of attributes;identifying a highest attribute having the highest information gain value; andassigning the highest attribute to the first non-leaf node;receive target data for a target subscriber, the target data including each attribute in the subset of attributes;apply the target data to the decision tree, thereby generating a churn prediction for the target subscriber; andidentify the target subscriber as a churn prediction.