Identifying target customers for campaigns to increase average revenue per user

• US 20070112614A1
• 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 8,762,193 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation

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 system for identifying a subset of customers from a wider customer population to be targeted during a campaign to increase revenue, the targeted subset of customers being statistically more likely to generate increased revenue than the remainder of the customer population, the system comprising:

a data mart for storing customer data about individual customers in the customer population;

a data mining tool for analyzing customer data and preparing first and second statistical models for scoring customers based on customer attributes quantified in said customer data, said first and second models generating first and second scores for the customers in the customer population,

a data manipulation module for preparing the customer data stored in the data mart for data mining, transporting said prepared data to the data mining tool, and storing the first and second scores for the customers in the data mart; and

a reporting tool having access to the customer data stored in the data mart, including the first and second scores for the customers, for reporting customer distributions based on said first and second customer scores such that customers having a combination of both a desirable first score and a desirable second score may be identified and targeted for the campaign.

2. The system of claim 1 wherein the reporting tool is adapted to define a first plurality of distribution classes for grouping customers according to the scores generated by the first statistical model, and a second plurality of distribution classes for grouping customers within one of the distribution classes of said first plurality of distribution classes according to the scores generated by the second statistical model.

3. The system of claim 2 wherein the first statistical model is adapted to calculate a customer'"'"'s propensity to respond to a marketing campaign and to score the customer based on said propensity to respond.

4. The system of claim 3 wherein the second statistical model is adapted to calculate a customer'"'"'s propensity to generate increased revenue and to score the customer based on said propensity to generate increased revenue.

5. The system of claim 2 wherein the first statistical model is adapted to calculate a customer'"'"'s propensity to generate increased revenue and to score the customer based on said propensity to generate increased revenue.

6. The system of claim 5 wherein the second statistical model is adapted to calculate a customer'"'"'s propensity to respond to a marketing campaign and to score the customer based on said propensity to respond.

7. The system of claim 1 wherein the reporting tool is adapted to:

define a first plurality of distribution classes for grouping customers according to the customer scores generated by the first statistical model and a second plurality of distribution classes for grouping customers according to the customer scores generated by the second statistical model;

assign customers to distribution classes within said first plurality of distribution classes based on their first score and assign customers to distribution classes within said second plurality of distribution classes based on their second score; and

filter the customers assigned to the distribution classes of said first plurality of distribution classes by the distribution classes to which they are assigned in said second plurality of distribution classes.

8. The system of claim 7 wherein the first statistical model is adapted to calculate a customer'"'"'s propensity to respond to a marketing campaign and to score the customer based on said propensity to respond and the second statistical model is adapted to calculate a customer'"'"'s propensity to generate increased revenue and to score the customer based on said propensity to generate increased revenue.

9. The system of claim 7 wherein the first statistical model is adapted to calculate a customer'"'"'s propensity to generate increased revenue and to score the customer based on said propensity to generate increased revenue and the second statistical model is adapted to calculate a customer'"'"'s propensity to respond to a marketing campaign and to score the customer based on said propensity to respond.

10. A method for identifying a subset of customers from a wider customer population to be targeted during a campaign to increase revenue, the targeted subset of customers being statistically more likely to generate increased revenue than the remainder of the customer population, the system comprising:

developing a first statistical model for scoring customers according to a first criterion;

developing a second statistical model for scoring customers according to a second criterion;

applying the first and second statistical models to customer data to generate first and second scores for individual customers in the customer population;

grouping customers according to their first and second scores; and

analyzing the customer groupings to identify a subset of the customer population in which customers have a combination of both a desirable first score and a desirable second score, indicating a statistically significant likelihood of accepting a campaign offer and generating increased revenue.

11. The method of claim 10 wherein the step of analyzing customer groupings comprises analyzing customer distribution according to customers'"'"' second scores in a group of customers grouped according to their first scores.

12. The method of claim 11 wherein the one of said first and second criteria is a customer'"'"'s propensity to accept a marketing campaign offer, and the other of said first and second criteria is a customer'"'"'s propensity to generate increased revenue.

13. The method of claim 10 wherein the first criterion is a customer'"'"'s propensity to accept a campaign offer and the second criterion is a customer'"'"'s propensity to generated additional revenue after accepting the campaign offer.

14. The method of claim 13 wherein the identified subset of customers is selected from those customers having a high propensity to accept the campaign offer, those who have the highest propensity to generate additional revenue as a result.

15. The method of claim 14 further comprising the step of training the first and second models on historical customer data relating to past implementation of a substantially similar marketing campaign.

16. A method of creating a customer list for a marketing campaign, the method comprising:

creating a first statistical model for determining a customer'"'"'s propensity to generate increased revenue;

creating a second statistical model for determining a customer'"'"'s propensity to respond to a marketing campaign;

scoring a plurality of customers according to their propensity to generate increased revenue and their propensity to respond positively to a marketing campaign;

evaluating the results of the scoring to identify a group of customers having both a high propensity for generating increased revenue and a high propensity to respond positively to a marketing campaign; and

adding the identified group of customers to the marketing campaign target list.

17. The method of claim 16 wherein evaluating the results of the scoring comprises grouping customers according to their propensity to respond to a marketing campaign scores and according to their propensity to generate increased revenue scores, selecting a group of customers grouped based on one of their propensity to respond to a marketing campaign scores and their propensity to generate increased revenue scores, and analyzing the distribution of customers within the selected group according to the other of their propensity to respond to a marketing campaign scores and their propensity to generate increased revenue scores.