Versioning in a rules based decision management system

Details Versioning in a rules based decision management system Versioning in a rules based decision management system

1998-12-23

2003-04-08

Morse, Gregory (Department: 2122)

Data processing: software development, installation, and managem

Software program development tool

C706S045000

Reexamination Certificate

active

06546545

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a decision management system for creating and applying strategies to manage clients, such as customers, accounts or applicants, of an organization. More specifically, the present invention relates to a decision management system which uses versioning to create, select and apply different strategy versions.
2. Description of the Related Art
A typical organization maintains a significant amount of information about its clients, where clients refer to the customers, accounts or applicants for services of the organization. This information can be effectively used, for example, to increase productivity and reduce costs, while achieving the goals of the organization. Such goals may be to improve profitability and maximize customer value.
For example, a company may sell various products to its customers, and may maintain a significant amount of information relating to its customers. This information can be used to improve many critical interactions with the customers, such as marketing communications, sales calls, customer service, collections, and general relationship management activities.
Consider the following examples.
Assume that a diversified financial services company is trying to leverage its customer base by cross-selling its various products. It currently uses limited internal customer information and credit bureau information to identify existing customers for cross-sell campaigns. For example, they might send “invitations to apply” for a home equity loan to those customers who own a mortgage with the company, and meet a minimum credit bureau score threshold. Imagine how much more powerful their cross-selling efforts would be if they could use information from all of the customers' accounts to offer pre-approved home equity loans to customers where the likelihood of a sale was high, the probability of default was low, and the financial value of that sale was high.
As another example, assume that a regional bell operating company is currently applying only age-based criteria (e.g., “days past due”) to its accounts receivable portfolio to identify candidates for its collections department and to handle those customers. The content of the outbound collection notices and phone calls is driven solely by the age and amount of a customer's unpaid balance. Imagine if the company had a tool that helped it select and prioritize collection accounts based on the likelihood of a customer interaction making a bottom line difference. Instead of calling or writing all overdue accounts, they could focus resources on those where the customer interaction would make the greatest difference. In addition, they would save the expense and ill will generated by calling customers who would pay without a collections contact.
As a still further example, assume that a manager of a large telephone customer service center for a super-regional bank has been given only hard-line corporate policy to make decisions about fee and rate concessions. While her service reps attempt to stay to the company line, she is deluged with requests from good customers to talk to the manager. She uses her judgment based on the incomplete information available to her to decide which concessions are appropriate to prevent attrition of profitable customers. Just imagine if the service reps had guidelines that were specific to each customer, based upon customer data that indicates their value to the organization, likelihood of attrition, risk level, and other characteristics. The manager could stand by these guidelines with confidence. There would be no concessions made to unprofitable customers, fewer manager overrides, shorter calls, and reduced attrition of the customers they want to keep.
As diverse as the above examples appear on the surface, they share several common characteristics. Each involves a large customer base and a high volume of customer interactions. Each organization has a substantial amount of accumulated data regarding the characteristics, purchasing/behavior patterns, and profitability of customers (though the data may not yet be well organized or analyzed). Each organization has an opportunity to improve performance substantially by treating different customers and customer groups differently, due to diversity in customer relationships and their potential. In each case, there are desired outcomes that could result from alternative customer interactions (e.g., customer purchases a product, pays an outstanding bill, increases deposit balances), and those outcomes can readily be identified, quantified, and tracked.
Therefore, each of the above examples depicts a business situation that currently is not fully benefitting from decision support and therefore is yielding less than optimal results.
There are software based products in the marketplace which can organize information to make more effective decisions. For example, the American Management Systems (AMS) Strata™ decision support system release 2.0 (hereinafter Strata™ release 2.0) is a software based system which applies predictive modeling techniques to customer data, to thereby generate dramatic improvements in the effectiveness and profitability of customer interactions.
FIG. 1
is a diagram illustrating the general concept of a software-based decision management system, such as Strata™ release 2.0, which applies predictive modeling techniques to customer data.
Referring now to
FIG. 1
, a software based system
10
receives information from operational and/or customer information systems
20
, such as, for example, billing systems, account management systems, credit bureau systems and data warehouses. Software based system
10
prioritizes and tailors customer interactions based on predictive information, specific business rules, and continually evolving decision strategies. Software based system
10
then determines an appropriate action which is to be taken by an action-taking system
30
. An appropriate action to be taken could include, for example, a call to a customer, a specific collections procedure or a specific marketing action.
A decision management system as in
FIG. 1
can provide superior results, such as increased revenue generation, improved cost-effectiveness and enhanced customer relationships.
FIG. 2
is a more detailed diagram illustrating the operation of the decision management system Strata™ release 2.0.
Referring now to
FIG. 2
, in step
40
, an inbound event is a trigger that is received from one or more external systems to identify that a particular client event has occurred. Such events may be automatically generated due to client behavior or systematically produced at specified time intervals (i.e., monthly). Examples of inbound events include a customer declaring bankruptcy, a credit underwriting decision request, a credit account delinquency, an income statement cycle date, or a routine evaluation date (a periodic, scheduled evaluation).
From step
40
, the system moves to step
50
, where a client is assigned to a segment. A segment is a grouping of clients based on a characteristic by which the clients will be separated for applying different rules. Generally, a segment is a high level segregation of clients for the purpose of associating largely independent high level strategy. Segments are completely separate groups of clients, for which a unique set of evaluation processes have been defined. For example, a telecommunications company might have a segment for residential customers and another for business customers.
From step
50
, the system moves to step
60
, where clients are randomly grouped into different test groups for the purpose of applying competing policy rules, strategy, or experiments. Generally, test groups allow for strategy comparison. Just as in research environments, the behavior or outcomes of an experimental “test” population is compared to that of a “control” group that is not exposed to the experimental treatment. A strategist can specify what percentage of the clients should be randomly assigned

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