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

• US 20070112615A1
• Filed: 12/01/2005
• Published: 05/17/2007
• 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
• Granted Patent: US 7,917,383 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• 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

A method of increasing average revenue per user and a system architecture for providing analytical tools for implementing the method are provided. The method of increasing revenue relies on executing direct contact marketing campaigns to stimulate revenue growth. A business'"'"'s current revenue stream and historical revenue trends are analyzed to determine product lines or services that may benefit from revenue stimulation. Past marketing campaigns are assessed to determine their effectiveness. A strategy is selected for increasing revenue in a revenue stream that has been selected for revenue boosting activities. A marketing campaign that has been successful in the past and is consistent with the selected strategy is selected for stimulated revenue growth. Next, the customer base is analyzed to identify characteristics of customers who have responded positively to marketing campaigns in the past and whose revenue increased as a result, in order to identify customers with similar traits that may be exploited in future campaigns. Statistical models are created to determine individual customer'"'"'s propensity to respond positively to a campaign and propensity to generate increased revenue. Customers are scored according to their propensities, and the scoring results are used to select an optimal mix of customers who are likely to accept a marketing campaign offer, and those who will likely generate the greatest increase in revenue. These customers are contacted during the campaign. A system architecture is provided for receiving, storing and manipulating all of the customer and revenue data needed to perform the analysis required of the inventive method and report the results.

Claims

1. A method of increasing enterprise revenue, the method to be performed on a system architecture including an optimized data mart, a data mining tool and a user access module including a user interface, the method comprising:

receiving revenue and customer data on a regular basis over time from external enterprise operating systems, and loading the data into the data mart;

accessing the data in the data mart to build an interactive diagnostic tree to be displayed for and manipulated by a user via the 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 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 an appropriate ARPU increase lever that may be exploited by a direct marketing campaign to increase ARPU in the targeted revenue stream;

mining revenue and customer data stored in the data mart to develop statistical models of customer characteristics leading to a propensity for generating increased revenue and a propensity for accepting a marketing campaign offer;

scoring individual customers according to their propensity to generate increased revenue and their propensity to accept a marketing campaign offer;

filtering the customer base based on customer scores for both their propensity to generate increased revenue and their propensity to respond favorably to a marketing campaign;

compiling a target list of customers from the filtered results, the target list representing an optimal mix of customers likely to respond favorably to a campaign who will most likely generate additional revenue as a result of responding favorably to a campaign; and

executing the direct marketing campaign.

2. The method of increasing enterprise revenue 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 increasing enterprise revenue 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 increasing enterprise revenue 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 increasing enterprise revenue 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 increasing enterprise revenue of claim 1 wherein customer data are received from a customer relationship management system.

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

8. The method of increasing enterprise revenue 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 increasing enterprise revenue of claim 8 wherein an appropriate ARPU increase lever may be selected 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 for facilitating the creation of direct marketing campaigns for boosting a business'"'"'s revenue from established revenue streams, wherein creating the direct marketing campaigns includes analyzing a plurality of revenue streams to identify a revenue stream whose revenue performance is below a desired level, selecting a direct marketing campaign that includes a product or service offering that will likely increase revenue in the under performing revenue stream when accepted by a customer, calculating a propensity to accept a campaign offer and a propensity for generating increased revenue for individual customers, and selecting a targeted group of customers to communicate the campaign offering based on each customer'"'"'s propensity to accept the offer and each customer'"'"'s propensity for generating increased revenue such that the targeted group includes an optimal mix of customers who will likely accept the campaign offer and who will show the highest propensity to generate increased revenue should they accept the campaign offer, the business intelligence system comprising:

a data mart for storing customer and revenue data optimized for fast access and analytical reporting;

a population architecture adapted to extract data from external business operating systems, transform the data, and load 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 for preparing and transporting data between the data mart and the data mining module;

a data mining module for generating statistical models for characterizing the revenue and customer data, including models for calculating a customer'"'"'s propensity to accept a campaign offer, and for calculating a customer'"'"'s propensity to generate increased revenue; and

a user access module including an interface for receiving commands from a user and displaying analysis results, the user access module including a plurality of preconfigured and configurable reports for presenting customer and revenue data to a user, and including online-analytic processing to pull data from the data mart to populate reports requested by a user.

11. The business intelligence system architecture of claim 10 wherein the data manipulation model is adapted to pull data from the data mart and create large flat files comprising customer records and associated customer attributes to be analyzed by the data mining module.

12. The business intelligence system architecture of claim 10 wherein the plurality of preconfigured reports includes a 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, the customer segments 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 for displaying customer distributions based on customer characteristics.

16. The business intelligence system architecture of claim 16 wherein said customer distributions are calculated by 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 of increasing a business'"'"'s revenue by implementing a targeted direct marketing campaign directed toward select customer'"'"'s in the business'"'"'s existing customer base, the method comprising:

analyzing the business'"'"'s sources of revenue to identify an under performing revenue stream;

selecting a direct marketing campaign consistent with stimulating increased revenue from the identified under performing revenue stream;

building a first predictive model for determining a customer'"'"'s propensity for generating additional revenue;

building a second predictive model for determining a customer'"'"'s propensity to respond positively to a direct marketing campaign;

scoring individual customers in the business'"'"'s customer base according to the first and second predictive models to determine each customer'"'"'s propensities to generate increased revenue and to respond positively to a direct marketing campaign;

selecting customers to be targeted by the direct marketing campaign according to both their propensity to generate increased revenue and their 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 their positive response; and

executing the campaign by contacting the selected customers.

19. The method of increasing a business'"'"'s revenue of claim 18 wherein analyzing the business'"'"'s sources of revenue to identity an under performing revenue stream comprises constructing an interactive diagnostic tree that illustrates a plurality of the business'"'"'s revenue streams and displays a average revenue per user (ARPU) value for each revenue stream, 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 increasing a business'"'"'s revenue 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 may be selectively displayed for each resolution level.

21. The method of increasing a business'"'"'s revenue of claim 18 further comprising assessing previous targeted direct marketing campaigns to determining 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 increasing a business'"'"'s revenue of claim 18 further comprising analyzing customer attributes to identify characteristics of customers who generated increased revenue in the past, and to identify additional customer'"'"'s having similar characteristics who may show a greater propensity for revenue increase in the future.

23. The method of increasing a business'"'"'s revenue of claim 22 wherein analyzing customer attributes includes analyzing demographic data.

24. The method of increasing a business'"'"'s revenue of claim 22 wherein analyzing customer attributes includes analyzing geographical data.

25. The method of increasing a business'"'"'s revenue of claim 22 wherein analyzing customer attributes includes analyzing service subscription portfolios.

26. A method of creating a direct marketing campaign for increasing revenue from an established revenue stream, the method comprising:

analyzing a plurality of revenue streams to identify a revenue stream whose performance is below a desired level;

selecting 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 a propensity to accept a campaign offer for individual customers;

calculating a propensity for generating increased revenue for individual customers; and

selecting 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 for facilitating the design and implementation of direct marketing campaigns, the system architecture comprising;

a relational database having a data model optimized for fast retrieval and analytical reporting;

at least one data source for providing customer and revenue data;

a population architecture associated with the database for extracting data from at least one data source, transforming the extracted data, and loading the transformed data into the database according to the optimized data model;

a data manipulation module for pulling data from the database and preparing data for data mining;

a data mining module for analyzing 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 for scoring individual customers according to their individual propensity to accept a marketing campaign offer, and their individual propensity to generate increased revenue, and storing the scoring results in the database;

a user access module having access to data stored in the database, the user access module including an interface for displaying data and receiving commands from the user, the user access module including a plurality of pre-configured reports for presenting data including a distribution of customers based on their propensity to accept a marketing campaign offer and their propensity to generate increased revenue.