Method and apparatus for identifying network affiliations of churned subscribers

In one aspect, the teachings herein provide a method and apparatus for identifying the new network affiliation of a churned subscriber that has moved from an old network operator to a new network operator. Knowing the new network affiliation of a former subscriber provides a number of significant advantages to the old network operator. By way of non-limiting example, the old network operator can undertake new or adjusted advertising targeted at retaining remaining subscribers of like demographics. Additionally or alternatively, the old network operator can review pricing and service plans from the new network operator that are comparable to that used by the former subscriber, and make competitive pricing or service adjustments.

TECHNICAL FIELD

The present invention generally relates to communication networks having associated subscribers, and particularly relates to a method and apparatus for identifying network affiliations of churned subscribers.

BACKGROUND

Network operators constantly compete for new subscribers, frequently by poaching subscribers from competing network operators. Subscribers become “churners” when they discontinue their subscription (service plan) with one operator and move to a new operator. Network operators work hard to minimize churn, given the costs associated with obtaining new subscribers versus retaining existing subscribers.

Minimizing churn requires an understanding of who churned and why. Thus, churn analysis might be supposed as having three main components: (1) predicting whether a particular subscriber will churn and when; (2) detecting that a particular subscriber has churned; and (3) preventing additional churn by identifying why a particular subscriber churned. Network operators use certain aspects of subscriber churn prediction and detection, but churn prevention based on churner identification is a nascent technology.

SUMMARY

In one aspect, the teachings herein provide a method and apparatus for identifying the new network affiliation of a churned subscriber that has moved from an old network operator to a new network operator. Knowing the new network affiliation of a former subscriber provides a number of significant advantages to the old network operator. By way of non-limiting example, the old network operator can undertake new or adjusted advertising targeted at retaining remaining subscribers of like demographics. Additionally or alternatively, the old network operator can review pricing and service plans from the new network operator that are comparable to that used by the former subscriber, and make competitive pricing or service adjustments.

In one embodiment, a method of identifying new network affiliations of former subscribers that have left a first network operator includes determining in-network calling behavior for a former subscriber of the first network operator, based on evaluating first call data records (CDRs) accumulated for a first data collection window occurring at least partly before the former subscriber left the first network operator. The method further includes determining out-of-network calling behavior for one or more remaining subscribers that are identified from the in-network calling behavior of the former subscriber, based on evaluating second CDRs accumulated for a second data collection window occurring at least partly after the former subscriber left the first network operator.

Using these behavioral determinations, the method continues with correlating the in-network calling behavior with the out-of-network calling behavior, to identify an external network subscriber that is involved in out-of-network calls with said one or more remaining subscribers that characteristically match prior in-network calls between said one or more remaining subscribers and the former subscriber. Further, the method includes identifying the new network affiliation of the former subscriber according to the external network number associated with the identified external network subscriber.

Another embodiment includes a processing apparatus that is configured to identify new network affiliations of former subscribers that have left a first network operator. The processing apparatus includes a pattern recognition processor, a correlation processor, and an identification processor. As a non-limiting example, one or more of these processors may be functionally implemented in a computer, such as a server, based on the execution of stored computer program instructions.

The pattern recognition processor is configured to determine in-network calling behavior for a former subscriber of the first network operator, based on evaluating first call data records (CDRs) accumulated for a first data collection window occurring at least partly before the former subscriber left the first network operator. Further, the pattern recognition processor determines out-of-network calling behavior for one or more remaining subscribers that are identified from the in-network calling behavior of the former subscriber, based on evaluating second CDRs accumulated for a second data collection window occurring at least partly after the former subscriber left the first network operator.

The correlation processor is configured to correlate the in-network calling behavior with the out-of-network calling behavior, to identify an external network subscriber that is involved in out-of-network calls with said one or more remaining subscribers that characteristically match prior in-network calls between said one or more remaining subscribers and the former subscriber. Based on this identification, the identification processor is configured to identify the new network affiliation of the former subscriber according to the external network number associated with the identified external network subscriber. For example, the external network subscriber is “identified” in terms of the associated external network number, and the affiliated network operator is identified from the prefix portion of that number.

Of course, it will be appreciated that the present invention is not limited to the above features and advantages. Indeed, those skilled in the art will recognize additional features and advantages upon reading the following detailed description, and upon viewing the accompanying drawings.

DETAILED DESCRIPTION

FIG. 1illustrates a first network10having a plurality of subscribers12, including subscribers12-1,12-2, . . . ,12-N. No particular number of subscribers12is implied; rather, the figure simply illustrates that there is a plurality of subscribers12that “belong” to the first network10for a given time frame, with example ones of those subscribers12specifically enumerated.

According to one or more embodiments, the network10is a wireless communications network, such as a cellular network, and it is owned or otherwise controlled by a first network operator. Non-limiting examples of network operators include AT&T, VERIZON, ORANGE, VODAFONE, and CHINA MOBILE. In this context, the depiction of subscribers12will be understood to be synonymous with the depiction of subscriber devices (user equipment, also referred to UEs), such as mobile telephones, wireless modems, or essentially any other type of wireless communication device that is affiliated with the first network10through a subscription agreement.

The first network10is configured to collect or otherwise accumulate first call data records (CDRs)14over one or more first data collection windows. As an example, these first CDRs14include called/calling number information, call times, call duration, and, possibly, location information for the called and/or called party. “Call” as used herein means traditional voice calls in at least one embodiment. However, other embodiments are contemplated where the term “call” additionally or alternatively means “data” calls, such as may be used in multimedia connections or other data sessions.

Assume that during these one or more first data collection windows that various ones of the subscribers12make calls16to each other, where such calls16are suggested by the dashed lines interconnecting certain subscribers12. More particularly, assume that there are regularly occurring calls16between the subscriber12-1and the subscribers12-2,12-3, . . . ,12-N. Thus, while the CDRs14may be comprehensive and include call details for all calls to/from the entire population of subscribers, they will at least include call details for calls to/from at least one subset of the subscribers12.

Assume that, at some point, the subscriber12-1“departs” from the first network operator—i.e., the subscriber12-1terminates its subscription agreement and thus becomes a former subscriber12-1. SeeFIG. 2, which depicts the former subscriber12-1operating in an unknown external network20as external network subscriber22-1, among a plurality of subscribers22affiliated with the external network20.

Assume that the network10continues accumulating CDRs, including second CDRs24, where at least some portion of the second CDRs24correspond to a time after the subscriber12-1departs—i.e., terminates its network subscription—and thereby becomes former subscriber12-1. Thus, one has first CDRs14accumulated at least partly in advance of the subscriber12-1departing and second CDRs24accumulated at least partly subsequent to the subscriber12-1departing. These may be regarded as “pre-departure” CDRs14and “post-departure” CDRs24.

Now, imagine that before departure, subscriber12-1made calls to subscriber12-2having characteristic call times and/or call durations. As an example, assume that subscriber12-1called subscriber12-2on most weekdays at 9:00 AM. Expanding the example, assume that subscriber12-1regularly received calls from subscriber12-3, primarily on late Sunday afternoons, and that these calls rarely lasted less than sixty minutes. All such activity is captured in the first CDRs14. In a particular example, the first CDRs14include called/calling numbers, call dates, and call start/end times. All such calling activity is suggested by the dash lines interconnecting subscriber12-1with subscribers12-2,12-3, and so on, as shown inFIG. 1

Then, as noted, the subscriber12-1becomes a former subscriber but subscribers12-2and12-3continue their affiliation with the first network operator—i.e., they remain subscribers of the first network operator. Thus, as the network10accumulates the second CDRs24over one or more later, second data collection windows that cover at least some time after the departure of the subscriber12-1, these second CDRs24will continue capturing call details for the subscribers12that remain with the first network operator, including the remaining subscribers12-2and12-3.

It is advantageously recognized herein that the former subscriber12-1most likely has affiliated with another network operator and it is also likely that the remaining subscribers12-2and12-3and the former subscriber12-1will continue engaging in calls26that are consistent with their historic call patterns. Thus,FIG. 2uses dashed lines to depict calling activity between the remaining subscribers12-2,12-3, and so on, and the former subscriber12-1now operating as external network subscriber22-1. The second CDRs24capture such calling activity.

Thus, the CDRs14include call detail records exhibiting characteristic call patterns18between subscriber12-1, and other subscribers12-2,12-3, etc., for a pre-departure time period, and CDRs24include call detail records exhibiting characteristic call patterns28between the former subscriber12-1(now operating as external network subscriber22-1) and the subscribers12-2,12-3, etc., who have remained with the first network operator. According to advantageous processing disclosed herein, the first call patterns18are derived or otherwise obtained from the CDRs14and correlated with the second call patterns28similarly obtained from the CDRs24.

From such correlation, one may identify calls between remaining subscribers12and an external network subscriber22-1that exhibit substantially the same patterns as calls between those same remaining subscribers12and a former subscriber12-1. In that regard, the identified external subscriber22may be deemed to be the former subscriber12-1, with a high degree of confidence.

Before turning to details for carrying out such identification, one point worth making is thatFIGS. 1 and 2present a simplified case where all of the subscribers12operate within the depicted confines of the first network10and all external network subscribers22operate within the confines of the external network(s)20. This depiction is not meant to suggest that the CDRs14and/or CDRs24do not include call detail records for calls involving “roaming” subscribers, wherein a given subscriber makes calls through a visited network rather than the subscriber's home network.

What is key, however, is that the CDRs14capture data for “in-network” calls16between the former subscriber12-1and one or more other subscribers12of the first network operator. Here, an “in-network” call does not necessarily mean that one or both involved subscribers12are not roaming, but rather means simply that one subscriber12of the first network operator calls another subscriber12of the first network operator. In contrast, the CDRs24capture data for “out-of-network” calls26, where subscribers12of the first network operator make or receive calls to or from an external network subscriber22. In fact, the first CDRs14and second CDRs24may be comprehensive and each one may capture all calling activity, both in- and out-of-network.

But it will be understood that calls16occur between subscriber12-1and various other subscribers12before the departure of subscriber12-1, meaning that CDRs14collected before the subscriber12-1departs the network10will exhibit call patterns characteristic for the in-network communications behavior between subscriber12-1and12-2, and so on. Then, with CDRs24collected at least partly after departure of the subscriber12-1, those CDRs will not include call records explicitly for the former subscriber12-1, but will include call records for the remaining subscribers12-2,12-3, and so on.

Thus, to the extent that those remaining subscribers12continue their characteristic communications behavior with respect to the former subscriber12-1, calls to/from the former subscriber12-1, now operating as the external network subscriber22-1, will be captured in the CDRs24and those calls will exhibit the same characteristic call times, durations, etc., as exhibited by the earlier calls between the subscriber12-1and subscribers12-2,12-3, etc., before the departure of subscriber12-1.

FIG. 3, in particular, provides one example of a processing apparatus configured to identify new network affiliations of former subscribers that have left a first network operator. In this regard,FIG. 3depicts a “Charging Reporting System” (CRS)30that accumulates or otherwise stores CDRs for any number of data accumulation windows. Thus, the CRS30holds CDRs denoted as CDRs14collected before the departure of subscriber12-1(and/or any number of other subscribers12) and CDRs24collected after that departure. The CRS30may also be referred to as a “CDR server,” signifying its role as a source for the CDRs14and24.

FIG. 3also depicts a processing apparatus34that is configured to generate churned subscriber data36from the CDRs14and24. To do so, in at least one embodiment, the processing apparatus34is configured to evaluate or otherwise process the CDRs14and18to generate the first call patterns18and second call patterns28, respectively. In a non-limiting example, the processing apparatus34generates the call patterns18and28for at least some of the subscribers12represented in the CDRs14and18by determining statistical call data.

Continuing this example, each subscriber12can be identified by its network number, the most frequently called/calling numbers are identified for the subscriber12, along with identifying call times and durations for those calls. One or more additional parameters, such as called/calling number location data also may be collected and characterized. All such data may be stored in a data structure that is indexed on a per subscriber basis. Of course, this is a non-limiting example.

In any case, the processing apparatus34evaluates the first and second call patterns18and28, to generate the churned subscriber data36. The churned subscriber data36comprises new operator affiliation data38and/or churner behavior data40. The former data comprises, for example, identification of one or more external network operators with which one or more former subscribers appear to have affiliated. The latter data comprises, for example, information reflecting statistical trends, such as identification of which competing network operators appear to have gained the most former subscribers of network10over a given time period.

For example purposes, one may assume that the CRS30and processing apparatus34are associated with the first network10and operate under control of the first network operator. Of course, any number of network operators may have their own CRSs30and processing apparatuses34, to produce their own sets of churned subscriber data36. In any case, with its association with the first network operator, it will be assumed that the processing apparatus34has access—e.g., secure access—to the first CDRs14and second CDRs24for subscribers12in the first network10, for any number of record accumulation periods.

As a non-limiting example, the CDRs14and24span multiple monthly billing periods, including one or more periods where a certain subscriber12-xwas part of the subscriber base for the first network10, and one or more months subsequent to the subscriber12-xterminating its subscription with the first network10and, presumably, newly affiliating with an unknown, competing network. Here, the “x” suffix denotes some particular subscriber12, from among the overall plurality of subscribers12of the first network10, and22-xmay be used to denote that same former subscriber operating as an external network subscriber.

With reference to the illustration provided byFIG. 4, one sees an example of the processing apparatus34contemplated in one or more embodiments contemplated herein. The illustrated processing apparatus34comprises processing and control circuits40, which may comprise one or more microprocessor-based circuits. In one example, the processing apparatus34comprises a configured computer, such as a PC-based server running one or more stored computer programs.

At least functionally, the processing and control circuits40comprise a pattern recognition processor42, a correlation processor44, and an identification processor46. The processing and control circuits40further include or are associated with storage48, which provides storage for one or more computer programs50, the CDRs14and24, and processed data52. Note that the processed data52comprises the first and second call patterns18and28, and data derived from them, such as correlation results, etc.

Still further, the example processing apparatus34includes at least one communication interface, such as communication interfaces54and56. Here, the processing apparatus34receives the CDRs14and24from the CRS30via the communication interface54. Further, in the example illustration, the processing apparatus34outputs the churned subscriber data36via the communication interface56, such as for further use by an advertising system which uses the data to adapt or generate advertising and/or subscriber incentive programs as a function of the churned subscriber data36.

In at least one embodiment, the illustrated processing apparatus34is configured to identify new network affiliations of former subscribers that have left a first network operator. Correspondingly, the pattern recognition processor42is configured to determine in-network calling behavior for a former subscriber, e.g.,12-1, of the first network operator, based on evaluating first call data records (CDRs)14accumulated for a first data collection window occurring at least partly before the former subscriber12-1left the first network operator. The pattern recognition processor42also determines out-of-network calling behavior for one or more remaining subscribers12that are identified from the in-network calling behavior of the former subscriber12-1, based on evaluating second CDRs24accumulated for a second data collection window occurring at least partly after the former subscriber12-1left the first network operator.

As an example, the first network operator identifies a subset of subscribers in its overall subscriber population that are most likely to terminate their subscriptions by the next billing cycle (or at some near-term future time). The first network operator thus collects the first CDRs14for this identified subset and then uses billing/account information to identify any of those subscribers12that actually churned (quit). For any given one of the churned subscribers12, the CDRs14can be analyzed to identify the in-network “social connections” of the churned subscriber12. These social connections are other subscribers12within the network10who are/were involved in regular calling activity with the churned subscriber12. Now having identified those social connections, second CDRs24can be collected for the churned subscriber's social connections, to see whether any one or more of them are engaging in out-of-network calls that characteristically match prior in-network calls between the churned subscriber and those same social connections.

To better appreciate this capability, consider that the correlation processor44is configured to correlate the in-network calling behavior involving a given former subscriber12-1and one or more other subscribers12, with the later, out-of-network calling behavior of those same other subscribers12. This allows the correlation processor44to identify an external network subscriber22-1that is involved in out-of-network calls26with the same one or more remaining subscribers12that characteristically match prior in-network calls16between the one or more remaining subscribers12and the former subscriber12-1. In turn, the identification processor46is configured to identify the new network affiliation of the former subscriber12-1according to the external network number associated with the identified external network subscriber.

In one or more embodiments, the pattern recognition processor42is configured to determine the in-network calling behavior of the former subscriber12-1by identifying first call patterns18in the first CDRs24, the first call patterns18reflecting one or more call characteristics for in-network calls16involving the former subscriber12-1during the first data collection window. The pattern recognition processor42is further configured to determine the out-of-network calling behavior by identifying second call patterns28in the second CDRs24. These second call patterns28reflect one or more call characteristics for out-of-network calls26involving the one or more remaining subscribers12during the second data collection window. Again, the one or more remaining subscribers12to look at, in terms of their out-of-network calling behavior, can be one or more subscribers12that are known from the first CDRs14to have prior, regular in-network calling activity with the former subscriber12-1.

In at least one embodiment, the correlation processor44is configured to correlate the in-network calling behavior with the out-of-network calling behavior by correlating values representing the one or more call characteristics for the in-network calls with respective values representing the one or more call characteristics for the out-of-network calls. That is, with the in-network calls16represented in the first call patterns18and the out-of-network calls26represented in the second call patterns28characterized using like parameters—such as call times, durations, etc.—the correlation processor44is configured to compare respective parameters of the same type between the first and second call patterns18and28, to identify matching in-network and out-of-network call characteristics.

Thus, in at least one embodiment, the correlation processor44is configured to correlate the in-network calling behavior with the out-of-network calling behavior by determining whether there are out-of-network calls26between an external network subscriber22-1and the one or more remaining subscribers12during a second data collection window that characteristically matches in-network calls16between the former subscriber12-1and the same one or more remaining subscribers12during the first data collection window. If so, the correlation processor44and/or the identification processor46identifies external network subscriber22-1as the former subscriber12-1. Here, “identifies” does not necessarily mean determining personally identifying information regarding the actual identity of the former subscriber12-1. In one or more embodiments, identifying an external network subscriber22as being a former subscriber12means recognizing from correlation of the above-discussed in-network and out-of-network calling behaviors that the external subscriber22is likely—at some defined statistical confidence level—to be the former subscriber12.

Indeed, there may be cases where, when trying to identify a given former subscriber12, relatively good correlations may be observed for more than one external subscriber22. Thus, in at least one embodiment, for the case where correlation of the in-network and out-of-network calling behaviors identifies more than one external network subscriber22as possibly being the former subscriber12, the correlation processor44is further configured to compare the degree of correlation exhibited for each of the more than one external subscribers22, or to perform further correlations involving additional call characterization parameters, to identify the one external network subscriber22that is most likely to be the former subscriber12.

Further, in at least one embodiment, the identification processor46is configured to determine a confidence value based on correlation results determined from correlating the in-network and out-of-network calling behaviors. The confidence value indicates a confidence at which the external network subscriber22has been identified as being the former subscriber12.

Along these same lines, it will be appreciated that the processing apparatus34may deem the correlation results to be too low to justify inferring from the call patterns18and28that a given external network subscriber22is a former subscriber12. For example, the correlation results take on an overall value reflecting the degree to which characteristic call times/durations and locations determined from the first CDRs18match the characteristic call times/durations and locations determined from the second CDRs28. Here, like correlation parameters, such as characteristic call times relating one called number to another calling number, may be correlated for and between subscribers, to develop a numeric correlation result. A correlation of zero represents no match, while a correlation of one represents complete matching. Intermediate values, such as 0.8 or 0.2 represent relatively high and relatively low matches, respectively. Of course, these are examples and other numeric ranges or representations may be used.

It should also be noted, as an example, that the first CDRs14are accumulated by or for the processing apparatus34based on identifying a subset of current subscribers12that are considered likely to leave the first network operator in the near-term. As such, it is likely that one or more of these subscribers12actually will leave the network10and thereby become former subscribers12. Thus, for any given one of those subscribers12in the subset that actually do later leave the network10, the first CDRs14will have been accumulated while those former subscriber12were still with the network10. Put simply, the first network operator can identify a subset of subscribers12that are most likely to churn (terminate their subscriptions) and then collect CDRs for that subset, so that in-network calling behavior can be determined for any subscribers12in the subset that later churn.

In this regard, for any given former subscriber12, the processing apparatus34is configured to determine in-network social connections for the former subscriber12using these first CDRs14and identify the in-network calling behaviors for the former subscriber12with respect to those in-network social connections. Here, it will be appreciated that these social connections are the particular ones of the other subscribers12of the first network operator that were “linked” or “associated” with the former subscriber12via in-network calling activity.

With this basis, the second CDRs24are accumulated by or for the processing apparatus34based on collecting CDRs at least for the in-network social connections of the former subscriber12. Accordingly, the processing apparatus34is configured to identify the out-of-network calling behaviors for one or more of those in-network social connections.

Also, note that the processing apparatus34may be configured to filter the first and second CDRs14and24in advance of determining the in-network and out-of-network calling behaviors, respectively, such that said first and second CDRs each comprise per call data records including one or more of: calling number, called number, served MSISDN, call duration, call volume, number of events, timestamp and a location data. Of course, filtering may not be done, but certain filtering offers advantages in terms of reducing processing complexity, data set sizes, etc. Indeed, certain filtering, such as that suggested above, may be used to reduce the CDRs to just those parameters useful in gleaning calling behavior.

In one approach to determining such behavior, the processing apparatus34is configured to determine the in-network calling behavior by determining most frequently called/calling numbers and characteristic call times and durations for in-network calls16involving a former subscriber12during a first data collection window during which the former subscriber12is still with the network10. The processing apparatus34is further configured to determine the out-of-network calling behavior by determining most frequently called/calling numbers and characteristic call times and durations for out-of-network calls26involving one or more remaining subscribers12during the second data collection window which falls at least partly after the former subscriber12has left the network10.

Finally, it should also be noted that the first network operator can accumulate CDRs for all or some of its subscribers12on an ongoing basis and then select CDRs collected just before and just after the departure of a given subscriber12. That is, the network operator can collect CDRs routinely and then use detection of actual subscription terminations as a basis for identifying the former subscribers12for which the calling behavior processing is to be performed using the available CDRs.

FIG. 5illustrates a method500corresponding to the processing apparatus operations described above. As such, the method500may be implemented by programmatic configuration of the processing apparatus34—e.g., the execution of stored computer program instructions by one or more digital processing circuits within the processing apparatus34that correspondingly causes the processing apparatus34to carry out the method500.

Broadly,FIG. 5presents the method500as a method of identifying new network affiliations of former subscribers that have left a first network operator. The method500includes determining (Block502) in-network calling behavior for a former subscriber12of the first network operator, based on evaluating first CDRs14accumulated for a first data collection window occurring at least partly before the former subscriber12left the first network operator. The method500further includes determining (Block504) out-of-network calling behavior for one or more remaining subscribers12that are identified from the in-network calling behavior of the former subscriber12, based on evaluating second CDRs24accumulated for a second data collection window occurring at least partly after the former subscriber left the first network operator. Here, the in-network calling behavior is represented by, for example, the first call patterns18and the out-of-network calling behavior is represented by, for example, the second call patterns28.

The method500continues with correlating (Block506) the in-network calling behavior with the out-of-network calling behavior, to identify an external network subscriber22that is involved in out-of-network calls with said one or more remaining subscribers12that characteristically match prior in-network calls between said one or more remaining subscribers12and the former subscriber12.

Correspondingly, the method500includes (Block508) identifying the new network affiliation of the former subscriber12according to the external network number associated with the identified external network subscriber22. For example, the new network affiliation is identified by identifying the network operator corresponding to the mobile number prefix included in the external network number of the identified external network subscriber22.

With the above in mind, it will be appreciated that it is broadly contemplated herein to analyze the behavioral characteristics of a subscriber before and after he or she has churned. The teachings herein provide a method and apparatus for identifying the competing network operators that have attracted former subscribers. In at least some embodiments, the teachings herein disclose methods and apparatus for identifying otherwise hidden patterns exhibited by all the churned subscribers of a given network operator. For example, the churned behavior data40shown inFIG. 3can include information from which the network operator can determine that most of the churned subscribers that belonged to service class “X” have churned out to a competitive operator “Y.”

ConsiderFIG. 6, which illustrates an example process timeline of identifying the “landing zone” of a “churner.” Here, “landing zone” means the external network operator that a former subscriber, i.e., the churner, chooses to affiliate with.

In a normal churn analysis, churn-out period refers to the interval after which a churn prediction/detection process is triggered. Typically it will be at the end of a monthly billing cycle or at the end of a quarter. In the example, we have churn-out periods referred to as T1, T2, T3, . . . , T8. The processing implicated in the figure includes:predicting the list of churners;collecting behavioral data and constructing mobile social networks for the obtained list of churners;detecting the actual churners—i.e., those predicted churners on the list who actually churn from the given operator's network;monitoring the operator's network for similarity matches in characteristics between the churned-out subscribers and new external network subscribers;predicting at certain confidence levels the detection of actual churned subscribers within one or more of the competing external networks; andidentifying patterns buried with the behavior of those detected churned subscribers.

The identified patterns may be used to drive subscriber retention programs, advertising campaigns, pricing, etc.

Of further note, and as suggested earlier herein, there are advantageous approaches to determining the number of subscribers to evaluate. For example, the network operator may use a churn prediction method to predict a particular subset of current subscribers who might churn by the next billing period. CDRs and/or derived call patterns may be collected for just those predicted churners. Assuming that some of the predicted churners in fact remain with the network operator into the next billing cycle, CDRs and/or derived call patterns may be collected for these particular remaining subscribers.

It will be understood that the CDRs represent behavioral data, and that such data may be collected for a number of time periods, e.g., two churn periods. At the end of the collection period(s), a churn detection technique is used to find the subscribers that actually churned out of the operator's network. The “mobile social network” of the actual churners is stored for future monitoring. In at least one embodiment, the “mobile social network” is, at a minimum, a listing of the other subscribers of the operator that were involved in regular communications with the churned subscribers.

For the next couple of churn-out periods, the connected components of the actual churners in the mobile social network are monitored for a new external connection. Here, the “connected components” are one or more of those remaining subscribers that were identified as being within the mobile social network of the churned subscribers. If an external network subscriber exhibits the same social connectivity as one of the actual churners, that external network subscriber is deemed to be one of the churned subscribers. The new network affiliation is identified, for example, by analyzing the mobile number prefix associated with the external subscriber.

One approach to implementing the above technique includes:collecting CDRs for the appropriate time periods and subscribers;filtering the CDRs to keep only meaningful call data, such as calling number, called number, served MSISDN, duration, volume, number of events, timestamp and the location number;calculating social-network measures like in-degree and out-degree measures for all distinct called numbers and calling numbers;obtaining, e.g., for a current month, a list of predicted churners (probable churners);analyzing the called numbers external to the operator's network for and attempting to find external numbers with the same in-degree value were observed for the actual churners before they churned.

If more than one external number appears to identify a given actual churner, a better identification can be made by narrowing down the candidate external numbers based on comparing multiple call behaviors, such as duration, volume, number of events, location number and time of day (morning, afternoon, evening). Note that time-of-day analysis may be based on “slotting,” such as by considering a timeslot between 6:00 AM to 10:00 AM, a timeslot between 10:00 AM-6:00 PM, a timeslot between 6:00 PM to 12:00 AM, and a timeslot between 12:00 AM and 6:00 AM. Of course, other time-of-day quantizations may be used and the above examples are non-limiting.

FIG. 7illustrates a method700of processing according to the example details given immediately above. The method700may be implemented in the processing apparatus34, or in another computer system operating under control of the first network operator. In any case, the method700includes collecting (Block702) behavioral data, picking called/calling numbers, served MSIDSN, etc. Processing continues with performing (Block704) in-degree and out-degree analysis for all distinct called and called numbers, and finding (Block706) the numbers with the same in-degree as the churned subscribers. From these findings, the method700continues with determining (Block708) the churners' behavioral trends, and feeding (Block710) the trend data or parameterized trend data into the network operator's advertising process (which may comprise making the electronic data available for use in an advertising analysis.