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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: collecting, by a computing system, past user information and churn data for a plurality of users; training, by the computing system, a churn prediction model with the past user information and churn data; and calculating, by the computing system, a churn propensity score for a present user based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn. 2. The computer-implemented method of claim 1, wherein the past user information comprises a plurality of user-connection entity affiliations, each user-connection entity affiliation indicative of an affiliation between a user and a connection entity. 3. The computer-implemented method of claim 2, further comprising filtering the past user information and churn data based on filtering criteria, wherein the training the churn prediction model with the past user information and churn data comprises training the churn prediction model with past user information and churn data that satisfy the filtering criteria. 4. The computer-implemented method of claim 3, wherein the filtering criteria comprises a user-specific usage time threshold. 5. The computer-implemented method of claim 4, wherein the usage-specific usage time threshold removes any user-connection entity affiliations that account for less than a percentage threshold of a total usage time for a particular user. 6. The computer-implemented method of claim 5, wherein the percentage threshold is 10%. 7. The computer-implemented method of claim 4, wherein the filtering criteria further comprises a population-specific usage time threshold. 8. The computer-implemented method of claim 1, further comprising calculating churn propensity scores for a plurality of present users; and ranking the plurality of present users based on the churn propensity scores. 9. The computer-implemented method of claim 8, further comprising contacting a subset of the plurality of present users based on the churn propensity scores. 10. The computer-implemented method of claim 9, wherein contacting a subset of the plurality of present users based on the churn propensity scores comprises contacting a subset of the plurality of present users based on a churn propensity score threshold. 11. 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; training a churn prediction model with the past user information and churn data; and calculating a churn propensity score for a present user based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn. 12. The system of claim 11, wherein the past user information comprises a plurality of user-connection entity affiliations, each user-connection entity affiliation indicative of an affiliation between a user and a connection entity. 13. The system of claim 12, wherein the method further comprises filtering the past user information and churn data based on filtering criteria, and further wherein the training the churn prediction model with the past user information and churn data comprises training the churn prediction model with past user information and churn data that satisfy the filtering criteria. 14. The system of claim 13, wherein the filtering criteria comprises a user-specific usage time threshold. 15. The system of claim 14, wherein the usage-specific usage time threshold removes any user-connection entity affiliations that account for less than a percentage threshold of a total usage time for a particular user. 16. 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; training a churn prediction model with the past user information and churn data; and calculating a churn propensity score for a present user based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn. 17. The non-transitory computer-readable storage medium of claim 16, wherein the past user information comprises a plurality of user-connection entity affiliations, each user-connection entity affiliation indicative of an affiliation between a user and a connection entity. 18. The non-transitory computer-readable storage medium of claim 17, wherein the method further comprises filtering the past user information and churn data based on filtering criteria, and further wherein the training the churn prediction model with the past user information and churn data comprises training the churn prediction model with past user information and churn data that satisfy the filtering criteria. 19. The non-transitory computer-readable storage medium of claim 18, wherein the filtering criteria comprises a user-specific usage time threshold. 20. The non-transitory computer-readable storage medium of claim 19, wherein the usage-specific usage time threshold removes any user-connection entity affiliations that account for less than a percentage threshold of a total usage time for a particular user.