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US 10,949,771 B2Granted
Systems and methods for churn prediction
AssigneeFacebook, Inc.
InventorsVincent Gonguet, Aude Hofleitner, Sofus Attila Macskassy, Steven James Jarrett, Aruna Bharathi, Zhiliang Ma
Life of this patent
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Abstract
Systems, methods, and non-transitory computer-readable media can collect past user information and churn data for a plurality of users. A churn prediction model is trained using the past user information and churn data. A churn propensity score is calculated for a present user based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn.
Claims20
A computer-implemented method comprising:
- collecting, by a computing system, past user information and churn data for a plurality of users, wherein the past user information comprises a plurality of user-connection entity affiliations, each user-connection entity affiliation indicative of an affiliation between a first user and a connection entity, and further wherein the churn data comprises a change of at least one of a device model, an operating system, a device brand, or a service provider;
- filtering, by the computing system, the past user information based on a population-specific usage time threshold, wherein the filtering the past user information comprises:
- determining a plurality of populations of users based on the past user information comprising at least one of a shared geographic area or a shared gender;
- determining a population-specific usage time threshold for a first population of users of the plurality of populations of users, wherein the population-specific usage time threshold specifies a minimum usage time for a specific user-connection entity affiliation;
- receiving a usage time for the first user of the first population with the specific user-connection entity affiliation;
- and
- excluding past user information associated with the first user of the first population from the past user information for the plurality of users based on the past user information associated with the first user not satisfying the population-specific usage time threshold;
- training, by the computing system, a churn prediction model with the past user information and churn data for the plurality of users;
- and
- calculating, by the computing system, a churn propensity score for a present user for whom to predict churn propensity based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn.
7 more elements
A system comprising:
- at least one processor;
- and
- a memory storing instructions that, when executed by the at least one processor, cause the system to perform a method comprising:
- collecting past user information and churn data for a plurality of users, wherein the past user information comprises a plurality of user-connection entity affiliations, each user-connection entity affiliation indicative of an affiliation between a first user and a connection entity, and further wherein the churn data comprises a change of at least one of a device model, an operating system, a device brand, or a service provider;
- filtering the past user information based on a population-specific usage time threshold, wherein the filtering the past user information comprises:
- determining a plurality of populations of users based on the past user information comprising at least one of a shared geographic area or a shared gender;
- determining a population-specific usage time threshold for a first population of users of the plurality of populations of users, wherein the population-specific usage time threshold specifies a minimum usage time for a specific user-connection entity affiliation;
- receiving a usage time for the first user of the first population with the specific user-connection entity affiliation;
- and
- excluding past user information associated with the first user of the first population from the past user information for the plurality of users based on the past user information associated with the first user not satisfying the population-specific usage time threshold;
- training a churn prediction model with the past user information and churn data for the plurality of users;
- and
- calculating a churn propensity score for a present user for whom to predict churn propensity based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn.
10 more elements
A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:
- collecting past user information and churn data for a plurality of users, wherein the past user information comprises a plurality of user-connection entity affiliations, each user-connection entity affiliation indicative of an affiliation between a first user and a connection entity, and further wherein the churn data comprises a change of at least one of a device model, an operating system, a device brand, or a service provider;
- filtering the past user information based on a population-specific usage time threshold, wherein the filtering the past user information comprises:
- determining a plurality of populations of users based on the past user information comprising at least one of a shared geographic area or a shared gender;
- determining a population-specific usage time threshold for a first population of users of the plurality of populations of users, wherein the population-specific usage time threshold specifies a minimum usage time for a specific user-connection entity affiliation;
- receiving a usage time for the first user of the first population with the specific user-connection entity affiliation;
- and
- excluding past user information associated with the first user of the first population from the past user information for the plurality of users based on the past user information associated with the first user not satisfying the population-specific usage time threshold;
- training a churn prediction model with the past user information and churn data for the plurality of users;
- and
- calculating a churn propensity score for a present user for whom to predict churn propensity based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn.
7 more elements
Dependencies are inferred from the claim text.