US 10,762,517 B2Granted

Subscription churn prediction

AssigneeeBay Inc.

InventorsRama Krishna Vadakattu, Bibek Panda, Swarnim Narayan, Harshal Godhia

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Abstract

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.

Claims18

Dependent claims

A churn prediction system comprising:

at least one hardware processor;
an electronic memory storing instructions that when executed configure the at least one hardware processor to perform operations comprising:
identifying a historical sample set of subscriber data from a pool of historical samples stored in a database;
segmenting the historical sample set of subscriber data based at least in part on one or more of account age, subscription age, and gross merchandise value (GMV);
automatically selecting, using machine learning, a subset of attributes from a set of attributes of the segmented historical sample set of subscriber data based at least in part on an information gain value of each attribute of the set of attributes;
ranking each attribute in the subset of attributes using the machine learning;
generating a decision tree by assigning non-leaf nodes of the decision tree to corresponding attributes in the selected subset of attributes, each non-leaf node arranged in the decision tree according to the rank of the attribute assigned to the non-leaf node using the machine learning;
generating, using a programming language for analysis, a plurality of churn predictions for a plurality of target subscribers based at least in part on the decision tree and target data for the plurality of target subscribers stored in the database, each churn prediction of the plurality of churn predictions indicating a churn likelihood of a corresponding target subscriber of the plurality of target subscribers;
generating a ranked list of the plurality of target subscribers based at least in part on the plurality of churn predictions;
and
causing presentation of the ranked list via a graphical user interface.

8 more elements

A computer-implemented method for predicting churn comprising:

identifying a historical sample set of subscriber data from a pool of historical samples stored in a database;
segmenting the historical sample set of subscriber data based at least in part on one or more of account age, subscription age, and gross merchandise value (GMV);
automatically selecting, using machine learning, a subset of attributes from a set of attributes of the segmented historical sample set of subscriber data based at least in part on an information gain value of each attribute of the set of attributes;
ranking each attribute in the subset of attributes using the machine learning;
generating a decision tree by assigning non-leaf nodes of the decision tree to corresponding attributes in the selected subset of attributes, each non-leaf node arranged in the decision tree according to the rank of the attribute assigned to the non-leaf node using the machine learning;
generating, using a programming language for analysis, a plurality of churn predictions for a plurality of target subscribers based at least in part on the decision tree and target data for the plurality of target subscribers stored in the database, each churn prediction of the plurality of churn predictions indicating a churn likelihood of a corresponding target subscriber of the plurality of target subscribers;
generating a ranked list of the plurality of target subscribers based at least in part on the plurality of churn predictions;
and
causing presentation of the ranked list via a graphical user interface.

6 more elements

A non-transitory machine-readable medium storing processor-executable instructions which, when executed by a hardware processor, cause the hardware processor to perform operations comprising:

identifying a historical sample set of subscriber data from a pool of historical samples stored in a database;
segmenting the historical sample set of subscriber data based at least in part on one or more of account age, subscription age, and gross merchandise value (GMV);
automatically selecting, using machine learning, a subset of attributes from a set of attributes of the segmented historical sample set of subscriber data based at least in part on an information gain value of each attribute of the set of attributes;
ranking each attribute in the subset of attributes using the machine learning;
generating a decision tree by assigning non-leaf nodes of the decision tree to corresponding attributes in the selected subset of attributes, each non-leaf node arranged in the decision tree according to the rank of the attribute assigned to the non-leaf node using the machine learning;
generating, using a programming language for analysis, a plurality of churn predictions for a plurality of target subscribers based at least in part on the decision tree and target data for the plurality of target subscribers stored in the database, each churn prediction of the plurality of churn predictions indicating a churn likelihood of a corresponding target subscriber of the plurality of target subscribers;
generating a ranked list of the plurality of target subscribers based at least in part on the plurality of churn predictions;
and
causing presentation of the ranked list via a graphical user interface.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 14/986,476Filed 31 Dec 2015
Published: US 2017/0004513 A15 Jan 2017
Granted: 1 Sep 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G06Q 30/0202
Market modelling; Market analysis; Collecting market data: market predictions or forecasting for commercial activities
G06F 16/24578
With adaptation to user needs: using ranking
G06N 5/01
Computing arrangements using knowledge-based models: dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
G06N 20/00
Machine learning