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Factorial Design Expert System

a factorial design and expert system technology, applied in the field of statistics, marketing and experimental design, can solve the problems of conceptually difficult to understand factorial design for experimenters not well versed in statistics, and correspondingly difficult to interpr

Inactive Publication Date: 2012-02-16
LOYALTY BUILDERS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0012]Testing several factors together is faster, less expensive, and reveals the interactions among the factors. However factorial design is conceptually harder to understand for experimenters not well versed in statistics, and correspondingly harder to interpret, for example, by a typical small businessperson.
[0017]The user can extend the expert system using more factors in a straightforward way.

Problems solved by technology

However factorial design is conceptually harder to understand for experimenters not well versed in statistics, and correspondingly harder to interpret, for example, by a typical small businessperson.

Method used

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Examples

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Embodiment Construction

[0026]A description of preferred embodiments of the invention follows. The following definitions are used in this document:

Definitions

[0027]Factor—a variable to test; examples include but are not limited to collateral size, discount, extent of personalization, communications channel, etc.[0028]Level—the number of different values for a factor; typically there are two (5% off, 10% off) or three different levels; in a two level design, the higher value is typically denoted by a plus sign (+) and the lower value is denoted by a minus sign (−) when the values are numerical; the values are not necessarily numerical[0029]Treatment—the level value delivered to a subject; for example if the factor is discount level and the level values are 5% and 10%, then those subjects offered the 10% discount are said to have been given the + treatment[0030]Control group—a subset of the subject population that is set aside for a different treatment to determine the effect of a factor; the control group c...

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Abstract

An automated expert system that uses split-run and factorial design methods to determine which factors are most important in an experiment. The expert system is architected into Design, Execute and Evaluate phases, to assist a user in developing a Factorial Design experiment in which one, two or three factors are tested simultaneously. In a preferred embodiment, a database infrastructure and web client, browser-based methodology functions as the expert system (a “wizard”) to design experiments, build control groups and evaluate results, all with the goal of discovering what values for which factors will yield the optimum response from subjects.

Description

RELATED APPLICATIONS[0001]This application is a continuation of U.S. patent application Ser. No. 11 / 517,174, entitled “Factorial Design Expert System,” filed Sep. 7, 2006, which is related to U.S. patent application Ser. No. 11 / 517,180, entitled “Predicting Response Rate,” filed Sep. 7, 2006, and U.S. patent application Ser. No. 11 / 517,175, entitled “Online Direct Marketing System,” filed on Sep. 7, 2006. The entire teachings of the above applications are incorporated herein by reference.BACKGROUND OF THE INVENTION[0002]The present invention is generally related to statistics, marketing, and experimental design and more particularly related to an expert system that uses split-run and factorial design methods to determine which factors are most important in an experiment.[0003]Marketing is a process through which a company induces new and existing customers to buy its products and services. One familiar type of a marketing activity is advertising, where a company broadcasts its messa...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06Q30/02
CPCG06Q30/0203G06Q30/02
Inventor KLEIN, MARKJENKINS, BRIAN
Owner LOYALTY BUILDERS
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