Method and system for boosting the average revenue per user of products or services

• US 7,917,383 B2
• Filed: 12/01/2005
• Issued: 03/29/2011
• Est. Priority Date: 11/11/2005
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Canale, Paolo, Maga, Matteo, Bohe, Astrid
• Primary Examiner(s): Abdi; Kambiz
• Assistant Examiner(s): Monfeldt; Sarah M
• Application Number:
  US11/291,439
• Publication Number:
  US 20070112615A1
• Time in Patent Office: 1,944 Days
• Field of Search: 705/10
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

An enhanced business reporting methodology and system architecture provide a way to receive, store, and manipulate customer and revenue data to increase average revenue per user for a business. The system and method analyze a business'"'"'s current revenue streams and historical revenue trends to identify those revenue streams that may benefit from stimulation, assess the effectiveness of past marketing campaigns, select a revenue stream and strategy to increase the revenue for the selected revenue stream, select a previously successful marketing campaign consistent with the selected strategy. The system and method analyze the customer base to identify characteristics of customers who have responded positively to previous marketing campaigns and whose revenue has increased as a result, in order to create statistical models to identify customers with similar traits, and apply the statistical models to score and select customers to contact during future campaigns.

Claims

1. A method consisting of:

extracting, by a processor executing an application program, revenue and customer data from external enterprise operating systems, and loading the data into a data mart;

accessing, by the processor executing the application program, the data in the data mart to build an interactive diagnostic tree displayed to and manipulated by a user via a user access module, the diagnostic tree providing an analysis of the enterprise'"'"'s revenue based on individual revenue streams, each revenue stream corresponding to the sale or use of various products or services offered by the enterprise, the interactive diagnostic tree displaying average revenue per user (ARPU) of the products or services corresponding to each revenue stream;

generating, by the processor executing a user access module, interactive reports illustrating ARPU trends for analyzing ARPU changes over time, the diagnostic tree analysis and the ARPU trend analysis assisting a user to target a revenue stream for ARPU boosting and assessing marketing campaign results based on ARPU trends accompanying such campaigns;

identifying, by the processor executing a user access module that receives commands from a user through a user interface, an appropriate ARPU increase lever that may be exploited by a direct marketing campaign to increase ARPU in the targeted revenue stream;

mining, by the processor executing a data mining module, revenue and customer data stored in the data mart to develop statistical models of customer characteristics, the statistical models consisting of;

a first model leading to a propensity for generating increased revenue; and

a second model leading to a propensity for accepting a marketing campaign offer;

scoring, by the processor executing the data mining module, individual customers, the scoring consisting of;

a first score according to the individual customer'"'"'s propensity to generate increased revenue; and

a second score according to the individual customer'"'"'s propensity to accept a marketing campaign offer;

filtering, by the processor executing the data mining module, the customer base based on the first score and the second score for each of the individual customers;

compiling, by the processor executing the data mining module, a target list of customers from the filtered results, the target list identifying customers likely to respond favorably to a campaign and most likely to generate additional revenue as a result of responding favorably to a campaign; and

executing, by the processor executing through a campaign channel, the direct marketing campaign.

2. The method of claim 1 wherein the diagnostic tree includes an ARPU reference value for each revenue stream, and an ARPU GAP value representing the difference between the actual ARPU value and the reference ARPU value for each revenue stream.

3. The method of claim 2 wherein each revenue stream is divided into a plurality of selectable resolution levels, each level dividing the revenue stream into a narrower definition of the revenue source, the interactive diagnostic tree displaying the ARPU, reference ARPU and ARPU gap values for each revenue stream corresponding to the selected resolution level.

4. The method of claim 1 wherein generating interactive reports illustrating ARPU trends includes displaying at least one of ARPU, ARPU reference and ARPU gap values for a revenue stream from multiple different time periods.

5. The method of claim 1 wherein revenue data are received from an enterprise billing system in synchronization with the enterprise'"'"'s billing cycle.

6. The method of claim 1 wherein customer data are received from a customer relationship management system.

7. The method of claim 1 wherein customer data are received from a data warehouse system.

8. The method of claim 1 wherein the enterprise is a telecommunications service provider and the various revenue streams correspond to various telecommunications services provided by the telecommunications service provider.

9. The method of claim 8 wherein an appropriate ARPU increase lever is selectable from the group comprising:

increasing usage of an existing service;

increasing revenue demand for an existing service; and

enhancing the subscriber base of an existing service.

10. A business intelligence system architecture consisting of:

a processor;

a data mart, controlled by the processor, that stores customer and revenue data;

a population architecture that when executed by the processor extracts data from external business operating systems, transforms the data, and loads the data into the data mart, the external business operating systems being configured to supply revenue and customer data in synchronization with the business'"'"'s billing cycle;

a data manipulation module that when executed by the processor prepares and transports data between the data mart and the data mining module;

a data mining module that when executed by the processor generates statistical models that characterize the revenue and customer data, the statistical models consisting of;

a first model that calculates a customer'"'"'s propensity to accept a campaign offer, and a second model that calculates a customer'"'"'s propensity to generate increased revenue;

a user access module, including an interface, that when executed by the processor receives commands from a user and displays analysis results, the user access module including a plurality of preconfigured and configurable reports that present customer and revenue data to a user, and online-analytic processing to pull data from the data mart to populate reports requested by a user; and

an interactive diagnostic tree displayed to and manipulated by the user via the user access module, wherein the diagnostic tree providing an analysis of the revenue data based on individual revenue streams.

11. The business intelligence system architecture of claim 10 wherein the data manipulation model, when executed by the processor, pulls data from the data mart and creates flat files comprising customer records and associated customer attributes that the data mining module analyzes.

12. The business intelligence system architecture of claim 10 wherein the plurality of preconfigured reports includes the diagnostic tree analysis of the business'"'"'s revenue stream, the diagnostic tree analysis including average revenue per user (ARPU) values for a plurality of different revenue streams, ARPU target values associated with the different revenue streams, and an ARPU gap value corresponding to a difference between an ARPU value of a revenue stream and a corresponding ARPU target value.

13. The business intelligence system architecture of claim 10 wherein the plurality of preconfigured records include an ARPU trend analysis for illustrating changes in ARPU for one or more revenue streams overtime.

14. The business intelligence system architecture of claim 10 wherein the plurality of preconfigured reports include an analysis of the ARPU trend of various customer segments overtime, wherein the customer segments are defined by one or more common characteristics of customers.

15. The business intelligence system architecture of claim 10 wherein the preconfigured reports include interactive displays, controlled by the processor, that display customer distributions based on customer characteristics.

16. The business intelligence system architecture of claim 15 wherein said customer distributions are calculated by the data mining module executed by the processor by the data mining module filtering a first customer characteristic according to a second customer characteristic.

17. The business intelligence system architecture of claim 16 wherein said customer characteristics include a propensity to accept a marketing campaign offer and a propensity to generate increased revenue.

18. A method consisting of:

analyzing, by a processor executing an application program, the business'"'"'s sources of revenue to identify an under performing revenue stream, comprising constructing an interactive diagnostic tree displayed to and manipulated by a user via a user access module, wherein the interactive diagnostic tree illustrates a plurality of the business'"'"'s revenue streams and displays a average revenue per user (ARPU) value for each revenue stream;

selecting, by the processor executing the user access module that receives a user'"'"'s selection, a direct marketing campaign consistent with stimulating increased revenue from the identified under performing revenue stream;

building, by the processor executing a data mining module, two predictive models consisting of a first predictive model that determines a customer'"'"'s propensity to generate additional revenue,

and a second predictive model that determines a customer'"'"'s propensity to respond positively to a direct marketing campaign;

scoring, by the processor executing the data mining module, individual customers in the business'"'"'s customer base, the scoring consisting of;

a first score and a second score according to the first and second predictive models, respectively, to determine each customer'"'"'s propensities to generate increased revenue and to respond positively to a direct marketing campaign;

selecting, by the processor executing the user access module that receives selections from the user, customers to be targeted by the direct marketing campaign according to both the customer'"'"'s propensity to generate increased revenue and the customer'"'"'s propensity to respond positively to the campaign, such that a potential for revenue increase from the campaign is optimized by targeting customers who are predisposed to both respond positively to the campaign and generate increased revenue as a result of the customer'"'"'s positive response; and

executing, by the processor executing through a campaign channel, the direct marketing campaign that contacts the selected customers.

19. The method of claim 18 wherein analyzing the business'"'"'s sources of revenue to identity an under performing revenue stream comprises an ARPU reference value for each revenue stream, and an ARPU gap for each revenue stream based on the difference between the ARPU value and ARPU reference value for each revenue stream.

20. The method of claim 19 wherein the interactive diagnostic tree includes a hierarchy of resolution levels, wherein at each resolution level the revenue streams are divided into smaller more focused revenue tributaries, and ARPU values, ARPU reference values and ARPU gap values are selectively displayed for each resolution level.

21. The method of claim 18 further comprising assessing previous targeted direct marketing campaigns to determine which offers have been successful in increasing revenue in the past, and amongst which customers the previous campaigns have been successful.

22. The method of claim 18 further comprising analyzing, by the data mining module when executed by the processor, customer attributes to identify characteristics of customers who generated increased revenue in the past, and additional customer'"'"'s having similar characteristics that show a greater propensity to increase revenue in the future.

23. The method of claim 22 wherein analyzing customer attributes includes analyzing demographic data.

24. The method of claim 22 wherein analyzing customer attributes includes analyzing geographical data.

25. The method of claim 22 wherein analyzing customer attributes includes analyzing service subscription portfolios.

26. A method consisting of:

analyzing, by a processor executing an application program, a plurality of revenue streams to identify a revenue stream whose performance is below a desired level, comprising accessing, by the processor executing the application program, data in a data mart to build an interactive diagnostic tree displayed to and manipulated by a user via a user access module;

selecting, by the processor executing the user access module that receives a user'"'"'s selection through a user interface that displays the interactive diagnostic tree, a direct marketing campaign that includes a product or service offer which will lead to an increase in revenue in the identified under performing revenue stream when the offer is accepted by a customer;

calculating by the processor executing a data mining module, two scores consisting of a first score indicative of a propensity to accept a campaign offer by individual customers, and

a second score indicative of a propensity for generating increased revenue by individual customers; and

selecting, by the processor executing the user access module that receives the user'"'"'s selection through a user interface, a target group of customers to whom the product or service offer will be offered based on each customer'"'"'s propensity to accept a campaign offer, and each customer'"'"'s propensity to generate increased revenue.

27. A system architecture consisting of:

a processor;

a relational database, controlled by the processor, having a data model;

a data source, accessible by a population architecture when executed by the processor, that provides customer and revenue data;

the population architecture, further when executed by the processor, extracts data from the data source, transforms the extracted data, and loads the transformed data into the relational database according to the data model;

a data manipulation module that when executed by the processor pulls data from the relational database and prepares the data to perform data mining;

a data mining module that when executed by the processor analyzes the customer and revenue data prepared by the data manipulation module to identify characteristics of customers leading to a propensity to accept a marketing campaign offer, and a propensity to generate increased revenue, and scores individual customers, the scoring consisting of;

a first score according to each customer'"'"'s individual customers individual propensity to accept a marketing campaign offer, and a second score according each customer'"'"'s individual propensity to generate increased revenue, and stores the scoring results in the relational database;

a user access module that when executed by the processor accesses the data stored in the database, wherein the user access module includes;

an interface that when executed by the processor displays data and receives commands from the user;

a plurality of pre-configured reports that when executed by the processor presents data including a distribution of customers based on the customers propensity to accept a marketing campaign offer and propensity to generate increased revenue; and

an interactive diagnostic tree displayed to and manipulated by the user via the user access module, wherein the diagnostic tree provides analysis including average revenue per user (ARPU) values for different revenue streams, ARPU target values associated with the different revenue streams, and an ARPU gap value corresponding to a difference between an ARPU value of a revenue stream and a corresponding ARPU target value.