Churn Modeling Based On Subscriber Contextual And Behavioral Factors

• US 20160203509A1
• Filed: 01/14/2015
• Published: 07/14/2016
• Est. Priority Date: 01/14/2015
• Status: Abandoned Application

• Current Assignee: Amplero Inc.
• Original Assignee: Globys Inc (Constellation Software Incorporated)
• Inventors: Sharp, Richard Winslow III, Downs, Oliver B., Hersch, Jesse S., Mehanian, Courosh
• Application Number:
  US14/596,764
• Publication Number:
  US 20160203509A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

Subject innovations are directed towards a churn model using dynamic state-space modeling to determine churn risks for each active subscriber of a service provider having exhibited a precise sequence of behaviors. The churn model identifies complex behavioral patterns that are consistent with those of subscribers who have churned in a defined past, allowing for a personalized determination of churn risk. The churn model may also use static contextual data to assist in refinement of the churn model through identification of subscriber segments. A churn index is produced that may be used by an automated contextual marketing model to refine decision making for selectively marketing to a subscriber based, in part, on that individual subscriber'"'"'s churn risk.

Claims

1. A network device, comprising:

a transceiver to send and receive data over a network; and

one or more processors that are operative to perform actions, including;

training a churn model that uses dynamic state-spacing modeling to represent information about previous first sequential behavior activities of multiple first subscribers of a telecommunications service provider involving use of telecommunications functionality of a telecommunications service provider, wherein the multiple first subscribers subsequently terminate use of the telecommunications functionality after the first sequential behavior activities;

training a non-churn model that uses dynamic state-spacing modeling to represent information about previous second sequential behavior activities of multiple second subscribers of the telecommunications service provider involving use of the telecommunications functionality, wherein the multiple second subscribers are distinct from the multiple first subscribers and do not subsequently terminate use of the telecommunications functionality after the second sequential behavior activities, and wherein the non-churn model is separate from the churn model;

receiving, from the telecommunications service provider, data about behavior of a plurality of subscribers of the telecommunications service provider;

applying an active-subscriber filter to select a subset of the plurality of subscribers that satisfy a selected Activity Level;

employing the trained churn model to determine, for each subscriber in the subset, a first proportional likelihood that a behavioral sequence of the subscriber matches the first sequential behavior activities of the trained churn model;

employing the trained non-churn model to determine, for each subscriber in the subset, a second proportional likelihood that the behavioral sequence of the subscriber matches the second sequential behavior activities of the trained non-churn model;

comparing, for each subscriber in the subset, the determined first and second proportional likelihoods for the subscriber to identify whether the behavioral sequence of the subscriber is more similar to the first sequential behavior activities of the multiple first subscribers for the trained churn model or to the second sequential behavior activities of the multiple second subscribers for the trained non-churn model, and determining a churn risk value for the subscriber based on the determined first proportional likelihood and on the determined second proportional likelihood; and

sending, for one or more subscribers that are selected from the subset based at least in part on the determined churn risk values for the one or more subscribers, messages over one or more computer networks to one or more client devices of the one or more subscribers to influence future actions of the one or more subscribers related to churn for the telecommunications service provider.

2. The network device of claim 1 wherein the sending of the messages to the one or more subscribers to influence future actions of the one or more subscribers is performed to decrease a rate of churn within an active subscriber base of the telecommunications service provider.

3. The network device of claim 1 wherein the one or more processors are further operative to, before the sending of the messages to the one or more subscribers, perform selecting of the one or more subscribers, to be recipients of the messages, from the subset based on the determined churn risk values for the one or more subscribers.

4. The network device of claim 1 wherein the selected Activity Level is computed based on a first time window preceding a given date and on a second time window after the given date, and wherein the Activity Level is selected (a) based on a threshold on a provider reported status that is time-averaged over the first and second time windows, (b) based on a trend in a time-averaged provider reported status that decreases from the first time window to the second time window, (c) based on a threshold on account and usage data that is time-averaged over the first and second time windows, (d) based on a clustering on a low-pass wavelet filtered provider reported status, (e) based on a rule set, or (f) based on any combination of two or more of (a)-(e).

5. The network device of claim 1 wherein at least one of the trained churn model or the trained non-churn model is calibrated based on a Receiver Operating Characteristic (ROC) curve to select a threshold value used to declare that a subscriber is a churn risk.

6. The network device of claim 1 wherein at least one of the trained churn model or the trained non-churn model is implemented within a Hidden Markov Model framework.

7. The network device of claim 1 wherein the one or more processors are further operative to profile the trained churn and non-churn models to enable reporting and monitoring capability to the telecommunications service provider.

8. The network device of claim 1 wherein the employing of the trained churn and non-churn models includes applying wavelet filtering to quantized variables of each subscriber in the subset to determine activity thresholds.

9. The network device of claim 1 wherein the one or more processors are further operative to employ contextual data to separate the subset of subscribers into multiple segments, to build individual behavioral sub-models for each of the multiple segments, and to select, for each of the subscribers in the subset, one of the multiple segments to which the subscriber belongs, and wherein, for each subscriber in the subset, the employing of the trained churned and non-churned models includes using the individual behavioral sub-models for the segment selected for the subscriber.

10. The network device of claim 1 wherein the one or more processors are further operative to receive data from at least one external source about the plurality of subscribers, and wherein at least one of the applying of the active-subscriber filter or the employing of the trained churn model or the employing of the trained non-churn model is based in part on the received data.

11. The network device of claim 10 wherein the one or more processors are further operative to employ dynamic social network features of at least some of the received data to construct the behavioral sequence of each subscriber in the subset.

12. A non-transitory computer-readable storage device having computer-executable instructions stored thereon that, in response to execution by a processor unit, cause the processor unit to perform operations including:

training a churn model that uses dynamic state-spacing modeling to represent information about previous first sequential behavior activities of multiple first subscribers of a network provider who subsequently terminate use of a product or service of the network provider;

training a non-churn model that uses dynamic state-spacing modeling to represent information about previous second sequential behavior activities of multiple second subscribers of the network provider who do not subsequently terminate use of the product or service of the network provider, wherein the multiple second subscribers are distinct from the multiple first subscribers, and wherein the non-churn model is separate from the churn model;

receiving, from the network provider, data about behavior of a plurality of subscribers of the network provider;

employing the trained churn model to determine, for a subscriber from the plurality of subscribers, a first proportional likelihood that a behavioral sequence of the subscriber matches the first sequential behavior activities of the trained churn model;

employing the trained non-churn model to determine a second proportional likelihood that the behavioral sequence of the subscriber matches the second sequential behavior activities of the trained non-churn model;

comparing the determined first and second proportional likelihoods to identify that the behavioral sequence of the subscriber is more similar to the first sequential behavior activities of the multiple first subscribers for the trained churn model than to the second sequential behavior activities of the multiple second subscribers for the trained non-churn model; and

sending, based at least in part on identifying that the behavioral sequence of the subscriber is more similar to the first sequential behavior activities of the multiple first subscribers for the trained churn model, one or more messages over one or more networks to a client device of the subscriber to influence future actions of the subscriber related to churn for the network provider.

13. The non-transitory computer-readable storage device of claim 12 wherein the sending of the one or more messages decreases a Key Performance Indicator based on a rate of churn within an active subscriber base of the network provider.

14. The non-transitory computer-readable storage device of claim 12 wherein the computer-executable instructions further cause the processor unit to, before the sending of the one or more messages, determine a churn risk value for the subscriber based at least in part on the determined first and second proportional likelihoods, and select the subscriber as a recipient of the messages based on the determined churn risk value.

15. The non-transitory computer-readable storage device of claim 12 wherein the trained churn and non-churn models are calibrated based on a Receiver Operating Characteristic (ROC) curve to select a threshold value used to declare that a subscriber is a churn risk.

16. The non-transitory computer-readable storage device of claim 12 wherein the trained churn and non-churn models are implemented within a Hidden Markov Model framework.

17. The non-transitory computer-readable storage device of claim 12 wherein the computer-executable instructions further cause the processor unit to apply an active-subscriber filter to select a subset of the plurality of subscribers that satisfy a selected activity level, wherein the employing of the trained churn model and the employing of the trained non-churn model and the comparing is performed for each subscriber of the subset, and wherein the trained churn and non-churn models further apply wavelet filtering to quantized variables of each subscriber in the subset to determine activity thresholds.

18. The non-transitory computer-readable storage device of claim 12, wherein the computer-executable instructions further cause the processor unit to apply an active-subscriber filter to select a subset of the plurality of subscribers that satisfy a selected activity level, to employ contextual data to separate the subset of subscribers into multiple segments, to build individual behavioral sub-models for each of the multiple segments, and to select, for each of the subscribers in the subset, one of the multiple segments to which the subscriber belongs, wherein the employing of the trained churn model and the employing of the trained non-churn model is performed for each subscriber of the subset, and wherein, for each subscriber in the subset, the trained churned and non-churned models employed for the subscriber are from the individual behavioral sub-models for the segment selected for the subscriber.

19. The non-transitory computer-readable storage device of claim 12 wherein the computer-executable instructions further cause the processor unit to employ dynamic social network features of at least some data from the network provider 1 regarding activities of the plurality of subscribers to construct a behavioral sequence of each subscriber in the subset.

20. A system, comprising:

a non-transitory data storage device; and

one or more special purpose computer devices that access and store data on the data storage device and employ at least one processor to perform actions, including;

training a churn model to represent information about previous first sequential behavior activities of multiple first subscribers of a network provider who subsequently terminate use of a product or service of the network provider;

training a non-churn model to represent information about previous second sequential behavior activities of multiple second subscribers of the network provider who do not subsequently terminate use of the product or service of the network provider, wherein the multiple second subscribers are distinct from the multiple first subscribers, and wherein the non-churn model is separate from the churn model;

receiving, from the network provider, data about behavior of a plurality of subscribers of the network provider;

employing the trained churn model to determine, for a subscriber from the plurality of subscribers, a first proportional likelihood that a behavioral sequence of the subscriber matches the first sequential behavior activities of the trained churn model;

employing the trained non-churn model to determine a second proportional likelihood that the behavioral sequence of the subscriber matches the second sequential behavior activities of the trained non-churn model;

comparing the determined first and second proportional likelihoods to identify that the behavioral sequence of the subscriber is more similar to the first sequential behavior activities of the multiple first subscribers for the trained churn model than to the second sequential behavior activities of the multiple second subscribers for the trained non-churn model; and

sending, based at least in part on identifying that the behavioral sequence of the subscriber is more similar to the first sequential behavior activities of the multiple first subscribers for the trained churn model, one or more messages to the subscribers to influence future actions of the subscriber related to churn for the network provider.

21. The system of claim 20 wherein the at least one processor is further employed to use dynamic social network features of at least some data from the network provider regarding activities of the plurality of subscribers to construct the behavioral sequence of the subscriber.

22. The system of claim 20 wherein the at least one processor is further employed to receive data from at least one external source about the plurality of subscribers, and wherein at least one of the employing of the trained churn or the employing of the non-churn model is based in part on the received data.

23. The system of claim 20, wherein the at least one processor is further configured to apply an active-subscriber filter to select a subset of the plurality of subscribers that satisfy a selected activity level, to employ contextual data to separate the subset of subscribers into multiple segments, to build individual behavioral sub-models for each of the multiple segments, and to select, for each of the subscribers in the subset, one of the multiple segments to which the subscriber belongs, wherein the employing of the trained churn model and the employing of the trained non-churn model is performed for each subscriber of the subset, and wherein, for each subscriber in the subset, the trained churned and non-churned models employed for the subscriber are from the individual behavioral sub-models for the segment selected for the subscriber.

24. The system of claim 20 wherein the at least one processor is further configured to apply an active-subscriber filter to select a subset of the plurality of subscribers that satisfy a selected activity level, and wherein the trained churn and non-churn models further apply wavelet filtering to quantized variables of each subscriber in the subset to determine activity thresholds.