METHOD AND SYSTEM FOR GENERATING A TARGETED CHURN REDUCTION CAMPAIGN
A system to generate a targeted churn reduction campaign in an on-line social networking system may be implemented as a churn reduction campaign generator. In one embodiment, a churn reduction campaign generator utilizes a subscriber retention model and a churn probability model. When there is an indication, within an on-line social networking system, that a member, who is a subscriber to a paid service in the on-line social networking system, is likely to fail to renew their subscription (or “churn”), the churn reduction campaign generator executes the subscriber retention model to trigger a targeted subscriber retention campaign.
1 . A computer-implemented method comprising:
based on utilization, by a member, of one or more features provided by an on-line social networking system, determining churn probability for the member, the member being a subscriber to a service provided by the on-line social networking system, the churn probability indicating probability of the member failing to renew a subscription to the service;
determining that the churn probability for the member is greater than a low threshold value;
determining, using at least one processor, that an increase in utilization by the member of a target feature from the one or more features is to result in decreasing the churn probability for the member; and
provide the member with a recommendation with respect to the target feature.
2 . The method of claim 1 , comprising determining that a cost of the increase in utilization by the member of the target feature is less than respective costs of increasing utilization, by the member, of other features from the one or more features.
3 . The method of claim 1 , wherein the determining of the churn probability for the member comprises utilizing behavior information of the member, the behavior information monitored and stored in the on-line social networking system.
4 . The method of claim 1 , wherein the determining of the churn probability for the member comprises determining respective values assigned to the one or more features, a value assigned to a feature from the one or more features indicative of a frequency of utilization of the feature by the member.
5 . The method of claim 1 , wherein the determining of the churn probability for the member comprises determining respective values assigned to the one or more features, a value assigned to a feature from the one or more features indicative of an intensity of utilization of the feature by the member.
6 . The method of claim 1 , comprising determining that the churn probability for the member is less than a high threshold value.
7 . The method of claim 1 , wherein the providing of the recommendation to the member is via a news feed of the member in the on-line networking system, via a banner ad in the on-line networking system, or via a home page of the member in the on-line networking system.
8 . The method of claim 1 , wherein the providing of the recommendation to the member is via an e-mail message to the member.
9 . The method of claim 1 , comprising determining whether the member renewed the subscription to the service subsequent to the recommendation with respect to the target feature and storing a result of the determining in a storage system associated with the on-line social networking system.
10 . The method of claim 1 , comprising monitoring activities of the member in the on-line networking system subsequent to the recommendation with respect to the target feature.
11 . A computer-implemented system comprising:
at least one processor coupled to a memory;
a churn probability detector to determine, using the at least one processor, churn probability for a member based on utilization, by the member, of one or more features provided by an on-line social networking system, the member being a subscriber to a service provided by the on-line social networking system, the churn probability indicating probability of the member failing to renew a subscription to the service;
a threshold module to determine, using the at least one processor, that the churn probability for the member is greater than a low threshold value;
a target feature detector to determine, using the at least one processor, that an increase in utilization by the member of a target feature from the one or more features is to result in decreasing the churn probability for the member; and
a recommendation module to provide the member with a recommendation with respect to the target feature, using the at least one processor.
12 . The system of claim 11 , comprising a cost evaluator to determine that a cost of the increase in utilization by the member of the target feature is less than respective costs of increasing utilization, by the member, of other features from the one or more features.
13 . The system of claim 11 , wherein the churn probability detector is to determine the churn probability for the member utilizing behavior information of the member, the behavior information monitored and stored in the on-line social networking system.
14 . The system of claim 1 , wherein the churn probability detector is to determine respective values assigned to the one or more features, a value assigned to a feature from the one or more features indicative of a frequency of utilization of the feature by the member.
15 . The system of claim 1 , wherein the churn probability detector is to determine respective values assigned to the one or more features, a value assigned to a feature from the one or more features indicative of an intensity of utilization of the feature by the member.
16 . The system of claim 1 , wherein the threshold module is to determine that the churn probability for the member is less than a high threshold value.
17 . The system of claim 1 , wherein the recommendation module is to provide the recommendation to the member via a news feed of the member in the on-line networking system, via a banner ad in the on-line networking system, or via a home page of the member in the on-line networking system.
18 . The system of claim 1 , wherein the recommendation module is to provide the recommendation to the member via an e-mail message to the member.
19 . The system of claim 1 , comprising a campaign outcome monitor to determine whether the member renewed the subscription to the service subsequent to the recommendation with respect to the target feature and storing a result of the determining in a storage system associated with the on-line social networking system.
20 . A machine-readable non-transitory storage medium having instruction data to cause a machine to:
determine churn probability for a member based on utilization, by the member, of one or more features provided by an on-line social networking system, the member being a subscriber to a service provided by the on-line social networking system, the churn probability indicating probability of the member failing to renew a subscription to the service;
determine that the churn probability for the member is greater than a low threshold value;
determine that an increase in utilization by the member of a target feature from the one or more features is to result in decreasing the churn probability for the member; and
provide the member with a recommendation with respect to the target feature.