Abstract:
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.

Description:
TECHNICAL FIELD 
       [0001]    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 
       [0002]    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. 
         [0003]    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 
       [0004]    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. 
         [0005]    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. 
         [0006]    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. 
         [0007]    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. 
         [0008]    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. 
         [0009]    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. 
         [0010]    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. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  is a block diagram of one embodiment of a communication network, shown in simplified form and referenced to a time when a given subscriber belongs to the network. 
           [0012]      FIG. 2  is a block diagram showing the communication network of  FIG. 1  as a first network in juxtaposition with a second, external communication network, where the illustration is referenced to a time when the given subscriber has churned from the first network and joined the second network. 
           [0013]      FIG. 3  is a block diagram of one embodiment of an apparatus for collecting call data records for a network and one embodiment of a processing apparatus for evaluating such records to determine new network affiliations of a churned subscriber. 
           [0014]      FIG. 4  is a block diagram on example details for the processing apparatus introduced in  FIG. 3 . 
           [0015]      FIG. 5  is a logic flow diagram of one embodiment of a processing method that is implemented by the processing apparatus of  FIGS. 3 and 4 , for example. 
           [0016]      FIG. 6  is a diagram of one embodiment of a processing timeline for predicting subscribers likely to churn, collecting pre-churn call data records, detecting actual churn, collecting post-churn call data records, and using the collected records to identify the new network affiliations of churned subscribers. 
           [0017]      FIG. 7  is a logic flow diagram of one embodiment of a processing method for identifying the new network affiliations of churned subscribers. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]      FIG. 1  illustrates a first network  10  having a plurality of subscribers  12 , including subscribers  12 - 1 ,  12 - 2 , . . . ,  12 -N. No particular number of subscribers  12  is implied; rather, the figure simply illustrates that there is a plurality of subscribers  12  that “belong” to the first network  10  for a given time frame, with example ones of those subscribers  12  specifically enumerated. 
         [0019]    According to one or more embodiments, the network  10  is 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&amp;T, VERIZON, ORANGE, VODAFONE, and CHINA MOBILE. In this context, the depiction of subscribers  12  will 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 network  10  through a subscription agreement. 
         [0020]    The first network  10  is configured to collect or otherwise accumulate first call data records (CDRs)  14  over one or more first data collection windows. As an example, these first CDRs  14  include 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. 
         [0021]    Assume that during these one or more first data collection windows that various ones of the subscribers  12  make calls  16  to each other, where such calls  16  are suggested by the dashed lines interconnecting certain subscribers  12 . More particularly, assume that there are regularly occurring calls  16  between the subscriber  12 - 1  and the subscribers  12 - 2 ,  12 - 3 , . . . ,  12 -N. Thus, while the CDRs  14  may 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 subscribers  12 . 
         [0022]    Assume that, at some point, the subscriber  12 - 1  “departs” from the first network operator—i.e., the subscriber  12 - 1  terminates its subscription agreement and thus becomes a former subscriber  12 - 1 . See  FIG. 2 , which depicts the former subscriber  12 - 1  operating in an unknown external network  20  as external network subscriber  22 - 1 , among a plurality of subscribers  22  affiliated with the external network  20 . 
         [0023]    Assume that the network  10  continues accumulating CDRs, including second CDRs  24 , where at least some portion of the second CDRs  24  correspond to a time after the subscriber  12 - 1  departs—i.e., terminates its network subscription—and thereby becomes former subscriber  12 - 1 . Thus, one has first CDRs  14  accumulated at least partly in advance of the subscriber  12 - 1  departing and second CDRs  24  accumulated at least partly subsequent to the subscriber  12 - 1  departing. These may be regarded as “pre-departure” CDRs  14  and “post-departure” CDRs  24 . 
         [0024]    Now, imagine that before departure, subscriber  12 - 1  made calls to subscriber  12 - 2  having characteristic call times and/or call durations. As an example, assume that subscriber  12 - 1  called subscriber  12 - 2  on most weekdays at 9:00 AM. Expanding the example, assume that subscriber  12 - 1  regularly received calls from subscriber  12 - 3 , primarily on late Sunday afternoons, and that these calls rarely lasted less than sixty minutes. All such activity is captured in the first CDRs  14 . In a particular example, the first CDRs  14  include called/calling numbers, call dates, and call start/end times. All such calling activity is suggested by the dash lines interconnecting subscriber  12 - 1  with subscribers  12 - 2 ,  12 - 3 , and so on, as shown in  FIG. 1   
         [0025]    Then, as noted, the subscriber  12 - 1  becomes a former subscriber but subscribers  12 - 2  and  12 - 3  continue their affiliation with the first network operator—i.e., they remain subscribers of the first network operator. Thus, as the network  10  accumulates the second CDRs  24  over one or more later, second data collection windows that cover at least some time after the departure of the subscriber  12 - 1 , these second CDRs  24  will continue capturing call details for the subscribers  12  that remain with the first network operator, including the remaining subscribers  12 - 2  and  12 - 3 . 
         [0026]    It is advantageously recognized herein that the former subscriber  12 - 1  most likely has affiliated with another network operator and it is also likely that the remaining subscribers  12 - 2  and  12 - 3  and the former subscriber  12 - 1  will continue engaging in calls  26  that are consistent with their historic call patterns. Thus,  FIG. 2  uses dashed lines to depict calling activity between the remaining subscribers  12 - 2 ,  12 - 3 , and so on, and the former subscriber  12 - 1  now operating as external network subscriber  22 - 1 . The second CDRs  24  capture such calling activity. 
         [0027]    Thus, the CDRs  14  include call detail records exhibiting characteristic call patterns  18  between subscriber  12 - 1 , and other subscribers  12 - 2 ,  12 - 3 , etc., for a pre-departure time period, and CDRs  24  include call detail records exhibiting characteristic call patterns  28  between the former subscriber  12 - 1  (now operating as external network subscriber  22 - 1 ) and the subscribers  12 - 2 ,  12 - 3 , etc., who have remained with the first network operator. According to advantageous processing disclosed herein, the first call patterns  18  are derived or otherwise obtained from the CDRs  14  and correlated with the second call patterns  28  similarly obtained from the CDRs  24 . 
         [0028]    From such correlation, one may identify calls between remaining subscribers  12  and an external network subscriber  22 - 1  that exhibit substantially the same patterns as calls between those same remaining subscribers  12  and a former subscriber  12 - 1 . In that regard, the identified external subscriber  22  may be deemed to be the former subscriber  12 - 1 , with a high degree of confidence. 
         [0029]    Before turning to details for carrying out such identification, one point worth making is that  FIGS. 1 and 2  present a simplified case where all of the subscribers  12  operate within the depicted confines of the first network  10  and all external network subscribers  22  operate within the confines of the external network(s)  20 . This depiction is not meant to suggest that the CDRs  14  and/or CDRs  24  do not include call detail records for calls involving “roaming” subscribers, wherein a given subscriber makes calls through a visited network rather than the subscriber&#39;s home network. 
         [0030]    What is key, however, is that the CDRs  14  capture data for “in-network” calls  16  between the former subscriber  12 - 1  and one or more other subscribers  12  of the first network operator. Here, an “in-network” call does not necessarily mean that one or both involved subscribers  12  are not roaming, but rather means simply that one subscriber  12  of the first network operator calls another subscriber  12  of the first network operator. In contrast, the CDRs  24  capture data for “out-of-network” calls  26 , where subscribers  12  of the first network operator make or receive calls to or from an external network subscriber  22 . In fact, the first CDRs  14  and second CDRs  24  may be comprehensive and each one may capture all calling activity, both in- and out-of-network. 
         [0031]    But it will be understood that calls  16  occur between subscriber  12 - 1  and various other subscribers  12  before the departure of subscriber  12 - 1 , meaning that CDRs  14  collected before the subscriber  12 - 1  departs the network  10  will exhibit call patterns characteristic for the in-network communications behavior between subscriber  12 - 1  and  12 - 2 , and so on. Then, with CDRs  24  collected at least partly after departure of the subscriber  12 - 1 , those CDRs will not include call records explicitly for the former subscriber  12 - 1 , but will include call records for the remaining subscribers  12 - 2 ,  12 - 3 , and so on. 
         [0032]    Thus, to the extent that those remaining subscribers  12  continue their characteristic communications behavior with respect to the former subscriber  12 - 1 , calls to/from the former subscriber  12 - 1 , now operating as the external network subscriber  22 - 1 , will be captured in the CDRs  24  and those calls will exhibit the same characteristic call times, durations, etc., as exhibited by the earlier calls between the subscriber  12 - 1  and subscribers  12 - 2 ,  12 - 3 , etc., before the departure of subscriber  12 - 1 . 
         [0033]      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. 3  depicts a “Charging Reporting System” (CRS)  30  that accumulates or otherwise stores CDRs for any number of data accumulation windows. Thus, the CRS  30  holds CDRs denoted as CDRs  14  collected before the departure of subscriber  12 - 1  (and/or any number of other subscribers  12 ) and CDRs  24  collected after that departure. The CRS  30  may also be referred to as a “CDR server,” signifying its role as a source for the CDRs  14  and  24 . 
         [0034]      FIG. 3  also depicts a processing apparatus  34  that is configured to generate churned subscriber data  36  from the CDRs  14  and  24 . To do so, in at least one embodiment, the processing apparatus  34  is configured to evaluate or otherwise process the CDRs  14  and  18  to generate the first call patterns  18  and second call patterns  28 , respectively. In a non-limiting example, the processing apparatus  34  generates the call patterns  18  and  28  for at least some of the subscribers  12  represented in the CDRs  14  and  18  by determining statistical call data. 
         [0035]    Continuing this example, each subscriber  12  can be identified by its network number, the most frequently called/calling numbers are identified for the subscriber  12 , 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. 
         [0036]    In any case, the processing apparatus  34  evaluates the first and second call patterns  18  and  28 , to generate the churned subscriber data  36 . The churned subscriber data  36  comprises new operator affiliation data  38  and/or churner behavior data  40 . 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 network  10  over a given time period. 
         [0037]    For example purposes, one may assume that the CRS  30  and processing apparatus  34  are associated with the first network  10  and operate under control of the first network operator. Of course, any number of network operators may have their own CRSs  30  and processing apparatuses  34 , to produce their own sets of churned subscriber data  36 . In any case, with its association with the first network operator, it will be assumed that the processing apparatus  34  has access—e.g., secure access—to the first CDRs  14  and second CDRs  24  for subscribers  12  in the first network  10 , for any number of record accumulation periods. 
         [0038]    As a non-limiting example, the CDRs  14  and  24  span multiple monthly billing periods, including one or more periods where a certain subscriber  12 - x  was part of the subscriber base for the first network  10 , and one or more months subsequent to the subscriber  12 - x  terminating its subscription with the first network  10  and, presumably, newly affiliating with an unknown, competing network. Here, the “x” suffix denotes some particular subscriber  12 , from among the overall plurality of subscribers  12  of the first network  10 , and  22 - x  may be used to denote that same former subscriber operating as an external network subscriber. 
         [0039]    With reference to the illustration provided by  FIG. 4 , one sees an example of the processing apparatus  34  contemplated in one or more embodiments contemplated herein. The illustrated processing apparatus  34  comprises processing and control circuits  40 , which may comprise one or more microprocessor-based circuits. In one example, the processing apparatus  34  comprises a configured computer, such as a PC-based server running one or more stored computer programs. 
         [0040]    At least functionally, the processing and control circuits  40  comprise a pattern recognition processor  42 , a correlation processor  44 , and an identification processor  46 . The processing and control circuits  40  further include or are associated with storage  48 , which provides storage for one or more computer programs  50 , the CDRs  14  and  24 , and processed data  52 . Note that the processed data  52  comprises the first and second call patterns  18  and  28 , and data derived from them, such as correlation results, etc. 
         [0041]    Still further, the example processing apparatus  34  includes at least one communication interface, such as communication interfaces  54  and  56 . Here, the processing apparatus  34  receives the CDRs  14  and  24  from the CRS  30  via the communication interface  54 . Further, in the example illustration, the processing apparatus  34  outputs the churned subscriber data  36  via the communication interface  56 , 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 data  36 . 
         [0042]    In at least one embodiment, the illustrated processing apparatus  34  is configured to identify new network affiliations of former subscribers that have left a first network operator. Correspondingly, the pattern recognition processor  42  is 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)  14  accumulated for a first data collection window occurring at least partly before the former subscriber  12 - 1  left the first network operator. The pattern recognition processor  42  also determines out-of-network calling behavior for one or more remaining subscribers  12  that are identified from the in-network calling behavior of the former subscriber  12 - 1 , based on evaluating second CDRs  24  accumulated for a second data collection window occurring at least partly after the former subscriber  12 - 1  left the first network operator. 
         [0043]    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 CDRs  14  for this identified subset and then uses billing/account information to identify any of those subscribers  12  that actually churned (quit). For any given one of the churned subscribers  12 , the CDRs  14  can be analyzed to identify the in-network “social connections” of the churned subscriber  12 . These social connections are other subscribers  12  within the network  10  who are/were involved in regular calling activity with the churned subscriber  12 . Now having identified those social connections, second CDRs  24  can be collected for the churned subscriber&#39;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. 
         [0044]    To better appreciate this capability, consider that the correlation processor  44  is configured to correlate the in-network calling behavior involving a given former subscriber  12 - 1  and one or more other subscribers  12 , with the later, out-of-network calling behavior of those same other subscribers  12 . This allows the correlation processor  44  to identify an external network subscriber  22 - 1  that is involved in out-of-network calls  26  with the same one or more remaining subscribers  12  that characteristically match prior in-network calls  16  between the one or more remaining subscribers  12  and the former subscriber  12 - 1 . In turn, the identification processor  46  is configured to identify the new network affiliation of the former subscriber  12 - 1  according to the external network number associated with the identified external network subscriber. 
         [0045]    In one or more embodiments, the pattern recognition processor  42  is configured to determine the in-network calling behavior of the former subscriber  12 - 1  by identifying first call patterns  18  in the first CDRs  24 , the first call patterns  18  reflecting one or more call characteristics for in-network calls  16  involving the former subscriber  12 - 1  during the first data collection window. The pattern recognition processor  42  is further configured to determine the out-of-network calling behavior by identifying second call patterns  28  in the second CDRs  24 . These second call patterns  28  reflect one or more call characteristics for out-of-network calls  26  involving the one or more remaining subscribers  12  during the second data collection window. Again, the one or more remaining subscribers  12  to look at, in terms of their out-of-network calling behavior, can be one or more subscribers  12  that are known from the first CDRs  14  to have prior, regular in-network calling activity with the former subscriber  12 - 1 . 
         [0046]    In at least one embodiment, the correlation processor  44  is 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 calls  16  represented in the first call patterns  18  and the out-of-network calls  26  represented in the second call patterns  28  characterized using like parameters—such as call times, durations, etc.—the correlation processor  44  is configured to compare respective parameters of the same type between the first and second call patterns  18  and  28 , to identify matching in-network and out-of-network call characteristics. 
         [0047]    Thus, in at least one embodiment, the correlation processor  44  is configured to correlate the in-network calling behavior with the out-of-network calling behavior by determining whether there are out-of-network calls  26  between an external network subscriber  22 - 1  and the one or more remaining subscribers  12  during a second data collection window that characteristically matches in-network calls  16  between the former subscriber  12 - 1  and the same one or more remaining subscribers  12  during the first data collection window. If so, the correlation processor  44  and/or the identification processor  46  identifies external network subscriber  22 - 1  as the former subscriber  12 - 1 . Here, “identifies” does not necessarily mean determining personally identifying information regarding the actual identity of the former subscriber  12 - 1 . In one or more embodiments, identifying an external network subscriber  22  as being a former subscriber  12  means recognizing from correlation of the above-discussed in-network and out-of-network calling behaviors that the external subscriber  22  is likely—at some defined statistical confidence level—to be the former subscriber  12 . 
         [0048]    Indeed, there may be cases where, when trying to identify a given former subscriber  12 , relatively good correlations may be observed for more than one external subscriber  22 . 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 subscriber  22  as possibly being the former subscriber  12 , the correlation processor  44  is further configured to compare the degree of correlation exhibited for each of the more than one external subscribers  22 , or to perform further correlations involving additional call characterization parameters, to identify the one external network subscriber  22  that is most likely to be the former subscriber  12 . 
         [0049]    Further, in at least one embodiment, the identification processor  46  is 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 subscriber  22  has been identified as being the former subscriber  12 . 
         [0050]    Along these same lines, it will be appreciated that the processing apparatus  34  may deem the correlation results to be too low to justify inferring from the call patterns  18  and  28  that a given external network subscriber  22  is a former subscriber  12 . 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 CDRs  18  match the characteristic call times/durations and locations determined from the second CDRs  28 . 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. 
         [0051]    It should also be noted, as an example, that the first CDRs  14  are accumulated by or for the processing apparatus  34  based on identifying a subset of current subscribers  12  that are considered likely to leave the first network operator in the near-term. As such, it is likely that one or more of these subscribers  12  actually will leave the network  10  and thereby become former subscribers  12 . Thus, for any given one of those subscribers  12  in the subset that actually do later leave the network  10 , the first CDRs  14  will have been accumulated while those former subscriber  12  were still with the network  10 . Put simply, the first network operator can identify a subset of subscribers  12  that 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 subscribers  12  in the subset that later churn. 
         [0052]    In this regard, for any given former subscriber  12 , the processing apparatus  34  is configured to determine in-network social connections for the former subscriber  12  using these first CDRs  14  and identify the in-network calling behaviors for the former subscriber  12  with respect to those in-network social connections. Here, it will be appreciated that these social connections are the particular ones of the other subscribers  12  of the first network operator that were “linked” or “associated” with the former subscriber  12  via in-network calling activity. 
         [0053]    With this basis, the second CDRs  24  are accumulated by or for the processing apparatus  34  based on collecting CDRs at least for the in-network social connections of the former subscriber  12 . Accordingly, the processing apparatus  34  is configured to identify the out-of-network calling behaviors for one or more of those in-network social connections. 
         [0054]    Also, note that the processing apparatus  34  may be configured to filter the first and second CDRs  14  and  24  in 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. 
         [0055]    In one approach to determining such behavior, the processing apparatus  34  is configured to determine the in-network calling behavior by determining most frequently called/calling numbers and characteristic call times and durations for in-network calls  16  involving a former subscriber  12  during a first data collection window during which the former subscriber  12  is still with the network  10 . The processing apparatus  34  is 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 calls  26  involving one or more remaining subscribers  12  during the second data collection window which falls at least partly after the former subscriber  12  has left the network  10 . 
         [0056]    Finally, it should also be noted that the first network operator can accumulate CDRs for all or some of its subscribers  12  on an ongoing basis and then select CDRs collected just before and just after the departure of a given subscriber  12 . That is, the network operator can collect CDRs routinely and then use detection of actual subscription terminations as a basis for identifying the former subscribers  12  for which the calling behavior processing is to be performed using the available CDRs. 
         [0057]      FIG. 5  illustrates a method  500  corresponding to the processing apparatus operations described above. As such, the method  500  may be implemented by programmatic configuration of the processing apparatus  34 —e.g., the execution of stored computer program instructions by one or more digital processing circuits within the processing apparatus  34  that correspondingly causes the processing apparatus  34  to carry out the method  500 . 
         [0058]    Broadly,  FIG. 5  presents the method  500  as a method of identifying new network affiliations of former subscribers that have left a first network operator. The method  500  includes determining (Block  502 ) in-network calling behavior for a former subscriber  12  of the first network operator, based on evaluating first CDRs  14  accumulated for a first data collection window occurring at least partly before the former subscriber  12  left the first network operator. The method  500  further includes determining (Block  504 ) out-of-network calling behavior for one or more remaining subscribers  12  that are identified from the in-network calling behavior of the former subscriber  12 , based on evaluating second CDRs  24  accumulated 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 patterns  18  and the out-of-network calling behavior is represented by, for example, the second call patterns  28 . 
         [0059]    The method  500  continues with correlating (Block  506 ) the in-network calling behavior with the out-of-network calling behavior, to identify an external network subscriber  22  that is involved in out-of-network calls with said one or more remaining subscribers  12  that characteristically match prior in-network calls between said one or more remaining subscribers  12  and the former subscriber  12 . 
         [0060]    Correspondingly, the method  500  includes (Block  508 ) identifying the new network affiliation of the former subscriber  12  according to the external network number associated with the identified external network subscriber  22 . 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 subscriber  22 . 
         [0061]    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 data  40  shown in  FIG. 3  can 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.” 
         [0062]    Consider  FIG. 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. 
         [0063]    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 T 1 , T 2 , T 3 , . . . , T 8 . 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&#39;s network;   monitoring the operator&#39;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; and   identifying patterns buried with the behavior of those detected churned subscribers.       
 
         [0070]    The identified patterns may be used to drive subscriber retention programs, advertising campaigns, pricing, etc. 
         [0071]    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. 
         [0072]    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&#39;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. 
         [0073]    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. 
         [0074]    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&#39;s network for and attempting to find external numbers with the same in-degree value were observed for the actual churners before they churned.       
 
         [0080]    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. 
         [0081]      FIG. 7  illustrates a method  700  of processing according to the example details given immediately above. The method  700  may be implemented in the processing apparatus  34 , or in another computer system operating under control of the first network operator. In any case, the method  700  includes collecting (Block  702 ) behavioral data, picking called/calling numbers, served MSIDSN, etc. Processing continues with performing (Block  704 ) in-degree and out-degree analysis for all distinct called and called numbers, and finding (Block  706 ) the numbers with the same in-degree as the churned subscribers. From these findings, the method  700  continues with determining (Block  708 ) the churners&#39; behavioral trends, and feeding (Block  710 ) the trend data or parameterized trend data into the network operator&#39;s advertising process (which may comprise making the electronic data available for use in an advertising analysis. 
         [0082]    Of course,  FIG. 7  represents just one of the several examples provided herein and should not be considered as limiting. More broadly, modifications and other embodiments of the disclosed invention(s) will come to mind to one skilled in the art having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the invention(s) is/are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of this disclosure. Although specific terms may be employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.