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METHOD AND SYSTEM FOR DESIGNING STRATEGIES FOR BUSINESS VERTICAL

IP.com Disclosure Number: IPCOM000195167D
Publication Date: 2010-Apr-22
Document File: 6 page(s) / 43K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system for designing strategies for retailers or other such business verticals is disclosed. The proposed technique uses a version of association rule mining on transaction data that makes it especially suited for designing strategies for cross-sell and store layout in retail environment.

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RP13339

METHOD AND SYSTEM FOR DESIGNING STRATEGIES FOR BUSINESS VERTICAL

BRIEF ABSTRACT

    A method and system for designing strategies for retailers or other such business verticals is disclosed. The proposed technique uses a version of association rule mining on transaction data that makes it especially suited for designing strategies for cross-sell and store layout in retail environment.

KEYWORDS

    Cross-sell, store layout, association rule mining, product centric association mining, customer clusters, temporal rules, store optimization, purchase behavior, demographics, CRM tools

DETAILED DESCRIPTION

    In the competitive business environment prevailing today, businesses are forced to compete on the basis of knowledge. Data mining tools and techniques provide applications with novel and significant knowledge that is used in various business strategies.

    Generally, association rule mining tools are employed to acquire pertinent business knowledge in a retail environment. However, as datasets grow exponentially with the size of dataset selecting 'interesting rules' is paramount. A measure of how interesting a rule is referred to as its 'interestingness'. The association rule mining is typically used in retail environments to identify product

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RP13339

associations and analyze markets. Some conventional association rule mining tools mine rules based on user specified support and confidence measures that allows the user to determine the interestingness of the association rules. User specified support and confidence measures are generally product centric.

    However the association rules tend to be biased due to some segments of the customer population. Further, customer behavior over a period of time is not revealed by conventional association rule mining tools. Furthermore, such association rule mining tools are typically product centric and interestingness measures provided by such product centric mining tools are not universally appropriate for providing knowledge that is applicable for designing other important business strategies. Some of the other important business strategies in the retail environment for example include cross-sell and store optimization. Conventional association rule mining tools do not provide interestingness measures that are appropriate for designing strategies for cross-sell and store optimization in retail environment. Therefore there is need in the art for a method and system for designing strategies in retail environment and other such business verticals.

    Figure 1 is a block diagram illustrating a system and method for designing strategies for retail environments and other similar business verticals using a version of association rule mining on transaction data. The proposed system and method are especially suited for designing strategies for cross-sell and store layout in a retail environment.

    The proposed system and method incorporates the following capabilities into the association rule mining on...