Patent Publication Number: US-2007118444-A1

Title: Analytic tool for evaluating average revenue per user for multiple revenue streams

Description:
PRIORITY CLAIM  
      This application claims the benefit of EPO Application No. ,filed______ filed assigned attorney docket number 10022-685 and Italian Application No. MI2005A002164, filed Nov. 11, 2005 assigned attorney docket number 10022-735, both of which are incorporated herein by reference in their entirety.  
     BACKGROUND  
      The present invention relates to an analytic tool for analyzing revenue. As a key component of profit a healthy revenue stream is essential for the success of any commercial enterprise. In order to increase profits a business must either increase revenue, cut costs, or both increase revenue and cut costs. However, whereas cost cutting has a finite limit, revenue increases are substantially unbounded. Increasing revenue is the only real long term solution for producing consistent sustained profitability increases over time. Therefore, a successful business must be ever vigilant for sources of additional revenue.  
      Traditionally, businesses have viewed revenue from the perspective of the products and services sold. Strong sales of products and services lead to strong revenue and, if costs are held in check, to high profitability. Poor sales lead to poor revenue and low profitability. From this perspective, increased sales are the key to increased profitability. Typically, increased sales means finding and attracting new customers. For many businesses finding new customers can present a significant challenge, especially in mature markets where new customers may be hard to come by.  
      The reliance on ever increasing sales to an ever expanding customer base ignores an important pool of potential additional revenue, namely a business&#39;s existing customer base. If existing customers can be induced to purchase more products or increase their use of services revenue goes up, often at much less cost than attracting new customers. Existing customers are at least somewhat known quantities. They are easier to reach than non-customers, and their consumption and usage patterns may be analyzed to determine which additional products or services may be of interest to them. Efficient targeted campaigns may be developed to contact existing customers in order to stimulate revenue growth.  
      The shortcomings of the traditional way of looking at revenue, i.e. from the prospective of the products and services sold, are readily apparent when one tries to identify opportunities for stimulating revenue among existing customers. Sales numbers may reflect the popularity (or lack thereof) of various products and services, but they say little about the customers themselves. How much revenue is the average customer generating for one service compared to another? How does customer revenue for particular products and services compare with industry averages? Which customers are likely to generate additional revenue in response to marketing campaign offers? 
      The answers to these questions and others like them can have a profound effect on the strategies businesses employ for stimulating additional revenue. To answer these and other such questions, a more customer focused view of revenue is required. For example, by considering the average revenue per user (ARPU) generated by a product or service, a business can more readily determine which of the products and services it offers provide the best opportunities for increasing revenue. Services where ARPU is low or below industry averages may be fertile ground for revenue stimulation efforts. In contrast, services where the ARPU is already high may be appropriate areas for increased sales efforts outside the existing customer base in order to attract additional high revenue customers.  
      Shifting the revenue focus from products and services to customers and users requires accounting systems and analysis tools which heretofore have not been available.  
     BRIEF SUMMARY  
      The present invention relates to an analytic tool for and method of analyzing a business&#39;s revenue from a customer or user perspective. According to the invention a business enterprise&#39;s revenue stream is broken down into a plurality of narrowly defined components that relate to the enterprise&#39;s products or services. Customer and revenue Data are collected in a manner that allows the revenue generated by each customer to be assigned to an appropriate revenue component or source corresponding to the products or services the customer has purchased or used.  
      Based on such particularized data, it is possible to calculate the average revenue per user (ARPU) of each individual revenue component. Target or reference ARPU values may be provided for each revenue component to provide benchmarks for evaluating the revenue performance of the various components of the overall revenue stream. An ARPU gap may be calculated based on the difference between actual ARPU values and ARPU reference values. ARPU increase opportunities may be identified based on the performance of the various revenue components.  
      According to an embodiment of the invention, an analytic tool for analyzing a business&#39;s revenue is provided. The tool includes a data storage device adapted to receive and store customer and revenue data. A data manipulation module associated with the data storage device derives calculated values from data stored in the data storage device, including for example, the average revenue per user of products or services sold by the business. An interface device is provided for interacting with a user and displaying data including the calculated values stored in the data storage device. The interface device is adapted to display a diagnostic tree representing the business&#39;s revenue stream decomposed into a plurality of contributory revenue components. A calculated value such as the average revenue per user associated with the revenue generated from the contributory components of the revenue stream is displayed in association with the revenue component from which it was derived.  
      According to another embodiment, a revenue analysis tool is provided which includes a data storage device for storing customer and revenue data. An access module adapted to receive data from the data storage device is also provided. The access module includes a processor and processing instructions for generating a diagnostic tree representing an enterprise&#39;s revenue sources. The diagnostic tree includes average revenue per user values for various revenue sources. The access module further includes an interface for displaying the diagnostic tree and allowing a user to select portions of the diagnostic tree to be displayed. Average revenue per user values are calculated and displayed for revenue sources contained in the portion of the diagnostic tree selected to be displayed.  
      Finally, a method of analyzing a business&#39;s revenue is provided. The method includes constructing a diagnostic tree depicting an enterprise&#39;s revenue sources. The various revenue streams are divided into a plurality of separate narrower revenue components that reflect the products or services from which the revenue is generated. Customer and revenue data are received from various operating systems. The revenue data are allocated to appropriate revenue components of the diagnostic tree based on customer use of the products or services associated with various revenue components. An average revenue per user (ARPU) value may be calculated from the allocated revenue for each revenue component of the diagnostic tree. At least a portion of the diagnostic tree is displayed for a user. The user may use the displayed data to evaluate the ARPU performance of the various revenue components displayed in the diagnostic tree.  
      Other systems, methods, features and advantages of the invention will be, or will become, apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the following claims. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       FIG. 1  is a block diagram of a system for providing a customer based analytic tool for analyzing revenue.  
       FIG. 2  is a diagnostic tree for analyzing the average revenue per user among a plurality of revenue streams;  
       FIG. 3  is a portion of fully developed six level diagnostic tree for analyzing the average revenue per user of a telecommunications service provider.  
       FIG. 4  is another portion of fully developed six level diagnostic tree for analyzing the average revenue per user of a telecommunications service provider.  
       FIG. 5  is another portion of fully developed six level diagnostic tree for analyzing the average revenue per user of a telecommunications service provider.  
       FIG. 6  is yet another portion of fully developed six level diagnostic tree for analyzing the average revenue per user of a telecommunications service provider. 
    
    
     DETAILED DESCRIPTION OF THE DRAWINGS AND THE PRESENTLY PREFERRED EMBODIMENTS  
      The present invention relates to an analytic tool for investigating and analyzing a business&#39;s revenue sources. The tool provides an interactive revenue diagnostic tree which decomposes a business&#39;s revenue stream into constituent components. Individual revenue sources can be analyzed on a per customer or per user basis. The tool is capable of calculating and displaying the average revenue per user (ARPU) of the various products and services that comprise the sources of the various contributory revenue streams. Actual ARPU values may be compared to forecasted values or industry averages for like products or services. An ARPU gap may be calculated based on the differences between the actual ARPU values and the forecasted or industry average values. The ARPU gap may provide a simple quick measure of the overall performance of a revenue stream.  
      The present tool is adapted to be interactive. A user may elect to view ARPU data at various levels of de-composition. If an intermediate level is displayed, the ARPU, ARPU reference and ARPU gap values are calculated and displayed for whichever level is chosen. This feature allows the user to examine the revenue stream at multiple different stages. Further, the user may filter the ARPU data by various customer attributes in order to investigate ARPU among various segments of the customer population.  
      The analytic tool of the present invention may be a component of a wider system for boosting ARPU. For example, the analytic tool may be incorporated in the system and method for boosting ARPU disclosed in the copendiing patent application entitled Method and System for Boosting the Average Revenue Per User of Products or Services Application No.______ filed on ______, the entire disclosure of which is incorporated herein by reference.  
       FIG. 1  shows a block diagram of the system architecture  100  of a system for boosting ARPU.  
      The diagnostic tree of the present invention is among the many ARPU boosting tools provided by the system architecture  100 . The system architecture  100  includes a plurality of data sources  102 ,  104 ,  106 . A dedicated data mart  110  forms the core of the system architecture  100 . A population architecture  108  is provided to perform extraction, transformation and loading functions for populating the data mart  110  with the data from the various data sources  102 ,  104 ,  106 . A data manipulation module  114  prepares data stored in the data mart  110  to be input to other applications such as a data mining module  116  and an end user access module  118 , or other applications. The end user access module  118  provides an interface through which business users may interact with, view and analyze the data collected and stored in the data mart  110 . The end user access module  118  may be configured to generate a plurality of predefined reports  120  for analyzing the data. Among the reports included in the user access module is the revenue diagnostic tree analysis which forms the output of the present analytic tool. The user access module  118  includes online analytical processing (OLAP) that allows a user to manipulate and contrast data “on-the-fly” to gain further insight into revenue data, historical trends, and the characteristics of customers who have responded positively to ARPU stimulation efforts in the past. External systems such as CRM  122  may also consume the data stored in the data mart  110 .  
      In order to support ARPU boosting methods and the diagnostic tree analysis of the present invention, the data mart  110  must be populated with revenue and customer data for each customer in the customer base. Revenue data may be provided by the enterprise billing system. Customer demographics, geographic data, and other data may be provided from a customer relationship management system (CRM). If the enterprise is a telecommunications services provider, usage patterns, traffic and interconnection data may be provided directly from network control systems. Alternatively, all or some of the data necessary to populate the data mart  110  may be provided by a data warehouse system or other mass storage system.  
      According to an embodiment, the data requirements of the system architecture  100  are pre-configured and organized into logical flows, so that the data source systems  102 ,  104 ,  106 , etc., supply the necessary data at the proper times to the proper location. Typically this involves writing a large text file (formatted as necessary) containing all of the requisite data to a designated directory. In order to duplicate the decomposition tree the revenue data must be broken down by each service, and each value identified by customer. Because of the monthly billing cycle of most enterprises the data typically will be extracted on a monthly basis to update the data mart  110 .  
      The population architecture  108  is an application program associated with the data mart  110 . The population architecture is responsible for reading the text files deposited in the designated directories by the various data sources at the appropriate times. The population architecture may perform quality checks on the data to ensure that the necessary data are present and in the proper format. The population architecture  108  includes data loading scripts that transform the data and load the data into the appropriate tables of the data mart&#39;s  110  data model.  
      The data mart  110  is a traditional relational database and may be based on, for example, Oracle 9i or Microsoft SQL Server platforms. The data mart  110  is the core of the system architecture  100 . The customer and revenue data are optimized for fast access and analytic reporting according to a customized data model. Star schemas allow an efficient analysis of key performance indicators by various dimensions. Flat tables containing de-normalized data are created for feeding predictive modeling systems.  
      The end-user access module  118  pulls data from the data mart  110  to be displayed in the diagnostic tree. The end user access module  118  includes online analytical processing capabilities based on market standard reporting software. Because all of the data are accumulated and stored on a customer by customer basis, the online analytical processing capabilities of the end user access module  118  allow the end user to alter display criteria and filter customers by various customer attributes to significantly expand the business intelligence insights that may be gleaned from the diagnostic tree.  
      An example of an interactive diagnostic tree display  140  is shown in  FIG. 2 . The diagnostic tree breaks down an enterprise&#39;s revenue stream along product or service lines into narrower and narrower revenue components as one moves further up the diagnostic tree. The diagnostic tree  140  shown in  FIG. 2  has been generated to illustrate the revenue stream of a telecommunications service provider (Telecom). The diagnostic tree  140  shows the Telecom&#39;s revenue stream decomposed down to three levels of detail. A first column  42  includes all revenue components. A second column  44  shows Level  1  revenue components. These include revenue from: fixed (land-line) phone services  60 ; Internet services  62 ; mobile telephone services  64 ; and value added services (VAS)  66 . A third column  46  shows Level  2  revenue components. In Level  2  revenue from fixed services has been broken out into: Indirect—Carrier Pre Selection (CPS)  68 , and Indirect—Carrier Selection (CS)  70  components. Internet revenue  62  has only one level  2  component, namely Fixed Revenue  72 . Mobile services revenue  64  is broken out into Direct—GSM  74  and Direct—UMTS components in Level  2 . Level  1  VAS  66  is broken out into VAS Not-Voice  78 , and VAS Voice  80  components in Level  2 . A fourth column  48  shows Level  3  revenue components. In Level  3  the Fixed Indirect—CPS revenue component  68  corresponds to a single component FI-Outgoing On Net  82 . The Fixed Indirect—CS Level  2  revenue component  70  is broken out into FIC Outgoing Off Net  84 , and FIC Outgoing On Net  86  in Level  3 . Revenue from Level  2  Internet Fixed  72  services is broken out into Direct  88  and Indirect  90  revenue components. Mobile Direct—GSM  74  revenue is broken out into MDS-Outgoing Off Net  92 , and MDS-Outgoing On Net  94  in Level  3 . Mobile Direct UMTS Level  2  revenue has a single level  3  component MDU-Outgoing On Net  96 . Level  2  Value Added Services Not Voice  78  include level  3  revenue components Messaging P2P 98, and Messaging P2P-M2P 100. Finally, VAS Voice  80  includes the sole level  3  revenue component Voice Mail  102 .  
      The diagnostic tree  140  displays the calculated average revenue per user (ARPU) for each revenue component displayed in Level  3  in the sixth column  52 . The diagnostic tree also displays a target or reference ARPU value for each revenue component displayed in level  3  in the seventh column  54 . The next column  56  displays the ARPU gap between the actual ARPU value and the target or reference ARPU value for each revenue component displayed in level  3 . A “Dashboard” icon is displayed in column  50  for each revenue component displayed in level  3 . The “DASHBOARD” icon provides a quick visual indication of the size of the ARPU gap (column  56 ) for each data stream and whether the gap is positive or negative.  
      As described above, the system architecture  100  supporting the diagnostic tree analysis calculates the ARPU values displayed in column  52  directly from customer invoice data each month. The target ARPU values in column  54  may be based on market forecasts, performance goals, industry averages or other benchmarks. According to an embodiment, the diagnostic tree  140  is a dynamic, interactive tool. A user may select the level for which ARPU data are to be displayed via the interface provided by the user access module  118 . For example, selecting level  2  will cause ARPU values, ARPU reference values, and ARPU gap values to be displayed for each revenue stream identified in level  2 . In this case, the ARPU, ARPU reference and ARPU gap values displayed will represent the aggregate ARPU, ARPU reference and ARPU gap values from all of the revenue streams that contribute to the displayed level  2  components. Alternatively, or in addition to displaying different levels of ARPU analysis, a user may choose to view ARPU data for only a certain segment of the customer population. For instance, a user may choose to view level  2  ARPU data for all male customers age  25 - 34 . In this case, the column  58  displaying level  3  information would not be displayed, and column  52  would display ARPU data for the level  2  revenue components only. Further, the ARPU data in column  52  would be calculated only from male customers aged  25 - 34 .  
      The diagnostic tree provides marketers and business users a quick visual indication of which components of the revenue stream are performing well and which are in need of ARPU stimulation. Those revenue components for which the ARPU gap is positive are performing better than forecast or better than the industry trend, and those for which the ARPU gap is negative are performing worse. The revenue components having a negative ARPU gap are obvious targets for ARPU boosting efforts.  
      The revenue streams defined in  FIG. 2  are based on a detailed analysis of the revenue received by telecommunications service providers. The value of the diagnostic tree analysis of the present invention is based in large part on the logical breakdown of the revenue streams into their individual components. Such revenue breakdowns will vary from industry to industry, and from enterprise to enterprise depending on the nature and mix of products and services sold by the enterprise. An embodiment of a diagnostic tree according to the invention has been configured to extend to six levels of revenue stream decomposition. It has been determined that  6  levels is sufficient to characterize the revenue streams of most enterprises. Of course other embodiments may be devised having more or fewer levels of detail without deviating from the spirit or scope of the invention.  
       FIGS. 3, 4 ,  5  and  6  each show a portion of a complete six level revenue diagnostic tree for analyzing and displaying the ARPU values for a telecommunications service provider&#39;s entire revenue stream. Because of the size of the tree it has been divided between the four figures. Each figure displays a single first level revenue stream and all of its lower level components. The level  1  revenue streams are the same as those shown in  FIG. 2 . Thus,  FIG. 3  shows the portion of the decomposition tree  300  relating to Fixed Communications revenue  302 .  FIG. 4  shows the portion of the decomposition tree  400  relating to Internet revenue  402 .  FIG. 5  shows the portion of the decomposition tree  500  relating to Mobile Communication Services revenue  502 . And  FIG. 6  shows the portion of the decomposition tree  600  relating to Value Added Services (VAS) revenue  602 . Each portion of the overall revenue stream will be described in turn.  
      Turning first to  FIG. 3 , Level  1  relates to revenue from Fixed wire line communications services  202 . Fixed service revenue  202  is split into three Level  2  components Indirect—CS  304 , Indirect—CPS  306 , and Direct-ULL  308 . Level  2  indirect—CS revenue  304  is split into an Outgoing On Net component  310  and an Outgoing Off-Net Component  312  in Level  3 . The Level  3  Outgoing On-Net component  310  is in turn split into To Fixed  326  and To Mobile  328  components in Level  4 . The Outgoing Off Net Level  3  component  312  is broken out in Level  4  into To Fixed  326 , To Mobile  334  and To International  336 . The Level  4  Outgoing Off-Net To Fixed  330  revenue is further broken out into Local  362  and National  364 .  
      The Level  2  component of fixed communication services Indirect CPS  306  is broken out into Outgoing On—Net  314  and Outgoing Off—Net  216  in Level  3 . The Level  3  Outgoing On—Net Revenue  314  is broken out into To Fixed  338  and To Mobile  340  components in Level  4 . The outgoing Off—Net revenue  316  is broken out into To Fixed  342 , To Mobile  344 , and To International  346  in Level  4 . The Outgoing Off—Net To Fixed  342  revenue of Level  4  is further broken out into Local  366  and National in Level  5 .  
      The Level  2  component of Fixed communications services revenue Direct-ULL  308  is broken out in Level  3  into Outgoing On Net  318 , Outgoing Off-Net  320 , Incoming Off-Net  322  and GN Other Operations  304 . The Level  3  Outgoing On-Net  318  revenue is broken out into To Fixed  348  and To Mobile  350  in Level  4 . The Level  3  Outgoing Off Net  320  revenue is broken out into To Fixed  352 , To Mobile  354  and To International  356  components in Level  4 . The Level  4  Direct Outgoing Off-Net To Fixed revenue  352  is further broken out into Local  370  and National  272  components in Level  5 . The Level  3  Fixed Direct-ULL Incoming Off Net revenue  322  is broken out into From Fixed  358  and From Mobile  360 . The Level  3  GN Other Operations  324  are broken out no further.  
      Turning to  FIG. 4 , the portion of the diagnostic tree  400  relating to Level  1  Internet revenue  402  is displayed. In Level  2  internet revenue is divided between Fixed  404  and Mobile  406  components. The Level  2  Internet Fixed  402  revenue is further broken out into Indirect  408 , Direct  410 , and Reverse  412  components in Level  3 . The Level  3  Internet Fixed Indirect  408  revenue is broken out no further. The Level  3  Internet Fixed Direct revenue is broken out into ULL (Unbundling of Local Loop)  420  and DSL (Digital Subscriber Line)  422  components in Level  4 . The Level  4  ULL  420  revenue is further broken out into Broad Band  428  and Narrow Band  430  components in Level  5 . The Level  3  Internet Fixed Reverse  412  revenue is broken out into Geographical Number  424  and Special Number  426  components in Level  4 . The Level  2  Internet Mobile revenue  406  is broken out into Direct-GSM  414 , Direct-GPRS  416  and Direct-UMTS  418  components in Level  3 . The Internet Mobile revenue streams are not further divided beyond Level  3 .  
      Turning now to  FIG. 5 , the portion of the diagnostic tree  500  stemming from Mobile revenue  502  is displayed. In Level  2  the Mobile  502  revenue is broken out into Direct-GSM  504 , Direct-GPRS  506 , and Direct-UMTS  508  components. In Level  3  the Level  2  Mobile Direct-GSM  504  revenue is further broken out into Outgoing On Net  510 , Outgoing Off Net  512 , incoming Off Net  514 , GN Other Operations  516 , and Roaming ITZ  518  components. The three letter code, in this case ITZ, identifies the country in which revenue from roaming charges are incurred. Here for example ITZ relates to revenue from roaming charges incurred in Italy. In Level  4  the Level  3  Mobile-direct-GSM Outgoing On Net  510  revenue is further broken out into To Fixed  540  and To Mobile  542  components. The mobile Direct-GSM Outgoing Off Net  512  revenue of Level  3  is broken out in Level  4  into To Fixed  544 , To Mobile  546 , and To International  548  components. The Level  3  Mobile Direct-GSM Incoming Off Net  514  is broken out into From fixed  550  and From mobile  552  components in Level  4 . Mobile Direct-GSM GN Other Operations  516  revenue is not broken out beyond Level  3 . The Mobile Direct-GSM Roaming ITZ  518  revenue of Level  3  is broken out in Level  4  into Outgoing  556  and Incoming  558  components.  
      The Level  2  Mobile Direct-GPRS  406  revenue is broken out in Level  3  in the same manner as the Mobile-Direct-GSM  504  revenue stream described above. Thus, the Mobile Direct GPRS  406  revenue stream is broken out in Level  3  into Outgoing On Net  520 , Outgoing Off Net  522 , Incoming Off Net  524 , GN Other Operations  526 , and Roaming ITZ  528  components. The Level  3  Mobile Direct-GPRS Outgoing On Net  520  revenue is broken out into To Fixed  560  and To Mobile  562  components in Level  4 . Level  3  Mobile Direct-GPRS Outgoing Off Net  522  revenue is broken out into To Fixed  564 , To Mobile  566 , and To International  568  components in Level  4 . Level  3  Mobile Direct-PRS Incoming Off Net  524  revenue is broken out into From Fixed  570  and From Mobile  572  components in Level  4 . Mobile direct-GPRS GN Other Operations  526  revenue is not broken out beyond Level  3 . Level  3  Mobile Direct-GPRS Roaming ITZ  528  revenue is broken out into Outgoing  574  and Incoming  576  components in Level  4 .  
      The Level  2  Mobile Direct-UMTS  508  revenue stream is broken out in Levels  3  and  4  in the same manner as the Mobile Direct GSM  504  revenue stream and the Mobile Direct-GPRS  506  revenue stream described above. Thus, the Mobile Direct-UMTS  508  revenue stream is broken out in Level  3  into Outgoing On Net  530 , Outgoing Off Net  532 , Incoming Off Net  534 , GN Other Operations  536  and Roaming ITZ  538  components. The Level  3  Mobile Direct-UMTS Outgoing Off Net  530  revenue stream is further broken out into To Fixed  578  and To Mobile  580  components in Level  4 . The Level  3  Mobile Direct-UMTS Outgoing Off Net  532  revenue stream is broken out into To Fixed  582 , To Mobile  584 , and To International  586  components in Level  4 . The Level  3  Mobile Direct—UMTS Incoming Off Net  534  revenue stream is broken out into From Fixed  588  and From Mobile  590  components in Level  4 . The Level  3  Mobile Direct-UMTS GN Other Operations  536  revenue stream is not broken out beyond Level  3 . The Level  3  Mobile Direct-UMTS roaming ITZ  538  revenue stream is broken out into Outgoing  592  and Incoming  594  components in Level  4 . This completes the decomposition of the Mobile  502  revenue stream.  
      Last we turn to  FIG. 6  which shows the portion  600  of the diagnostic tree stemming from Value Added Services (VAS)  802 . In Level  2  VAS revenue is broken out into VAS Voice  604 , and VAS Not Voice components. In Level  3  VAS Voice  604  is further broken out into Voice Mail  610 , and Other VAS Voice  612  components. Level  3  Voice Mail  610  revenue is broken out in Level  4  into Fixed  622  and Mobile  626  components. Similarly, Level  3  Other VAS Voice  612  revenue is broken out into Fixed  626  and Mobile  626  components as well.  
      The decomposition of the Level  2  VAS Not Voice  606  component of the VAS  602  revenue stream is somewhat more complex. The Level  2  VAS Not Voice  606  revenue is broken out in Level  3  into Messaging P2P (peer to peer)  614  and messaging P2M-M2P (peer to machine-machine to peer)  616 . Level  3  Messaging P2P  614  is broken out into SMS  630  and MMS  632  components in Level  4 . Level  4  SMS  630  revenue is broken out into Direct-GSM  640 , Direct-GPRS  642 , and Direct-UMTS  644  components in Level  5 . Furthermore, SMS Direct-GSM  640  is broken out into Outgoing On Net  666 , Outgoing Off Net  668 , and Incoming Off Net  670  components in Level  6 . Similarly SMS Direct-GPRS  642  is broken out into Outgoing On Net  672 , Outgoing Off Net  674  and Incoming Off Net  676  components in Level  6 . SMS-Direct-UMTS  644  is broken out into Outgoing On Net  678 , Outgoing Off Net  680 , and Incoming Off Net  682  component. Level  4  messaging P2P MMS revenue  632  is broken out into Direct-GPRS  646  and direct-UMTS  648  components in Level  5 . In Level  6  MMS Direct-GPRS  646  is broken out into Outgoing On Net  684 , Outgoing Off Net  686 , and Incoming Off Net  688  components. MMS direct-UMTS  648  is similarly broken out into Outgoing On Net  690 , Outgoing Off Net  692 , and Incoming Off Net  694  components in Level  6 .  
      The VAS Not Voice Messaging P2M-M2P  616  Level  3  revenue stream is further broken out into SMS  634 , MMS  636  and Downloads  638  components in Level  4 . Like the Messaging P2P SMS  630  revenue stream and the Messaging P2P MMS  632  revenue stream, the Messaging P2M-M2P SMS  634  revenue steam is broken out into Direct-GSM  650 , Direct-GPRS  652 , and Direct-UMTS  654  components in Level  5 . Messaging P2M-M2P MMS  636  revenue is broken out into Direct-GPRS  656  and Direct-UMTS  658  components in Level  5 . Messaging P2M-M2P downloads  638  revenue is broken out into Direct-GSM  660 , Direct GPRS  662 , and Direct-UMTS  664  components in Level  5 . The messaging P2M-M2P SMS—Direct GSM  650  revenue of Level  5  is further broken out into Outgoing On Net  696  and Incoming Off Net  698  components in Level  6 . The messaging P2M-M2P SMS Direct-GPRS revenue is also broken out into Outgoing On Net  700 , and Incoming Off Net  702  components in Level  6 . Messaging P2M-M2P SMS direct-UMTS  654  revenue is similarly broken out into Outgoing On Net  704  and Incoming Off Net  706  components in Level  6 . The Level  5  messaging P2M-M2P MMS Direct-GPRS  656  revenue is broken out in Level  6  into Outgoing On Net  708 , Outgoing Off Net  710 , and Incoming Off Net  712  components. Similarly, Messaging P2M-M2P MMS Direct-UMTS  658  revenue is broken out into Outgoing On Net  714 , Outgoing Off Net  716 , and Incoming Off Net  718  components in Level  6 . The Downloads Direct-GSM  660  revenue of Level  5  is further broken out into Outgoing On Net  720  and Outgoing Off Net  722  components in Level  6 . Similarly downloads Direct-GPRS  662  revenue is broken out into Outgoing On Net  724  and Outgoing Off Net  726  Level  6  components. Lastly, Downloads Direct-UMTS  664  revenue is broken out into Outgoing On Net  728  and Outgoing Off Net  730  components in Level  6 .  
      The revenue diagnostic tree just described and displayed in  FIGS. 3-6  represents a preferred breakdown of a Telecom&#39;s revenue stream. Of course businesses in other industries would necessarily breakdown their revenue streams differently to reflect the nature of the products and services they offer. Furthermore, Telecoms themselves may choose to build revenue diagnostic trees that differ from that described herein. Regardless of the particular manner in which the revenue streams are decomposed and the component revenue streams defined, the purpose of the diagnostic tree is to break down revenue streams in smaller more meaningful units. This allows the revenue from the various component streams to be evaluated on a per user basis. Evaluating the performance of the various revenue streams on this basis a user may identify revenue streams that are performing poorly, and those that are performing well from a customer perspective. Such information can be an important factor in developing marketing strategies for increasing revenue.  
      While various embodiments of the invention have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the invention. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents.