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Method for determining optimal saleable units based on item affinity analysis from POS (TLOG/ITEM) data

IP.com Disclosure Number: IPCOM000216228D
Publication Date: 2012-Mar-26
Document File: 3 page(s) / 51K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method to capture new insights from the readily available Point of Sale (POS) data from controllers. The invention addresses methods of affinity analysis and describes a method of weighting and combining multiple methods of affinity analysis. The invention is a mechanism to group the items into a larger number of saleable products within the particular demography. It is an automated method to determine which items should be grouped together to form larger saleable units.

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Method for determining optimal saleable units based on item affinity analysis from POS (TLOG/ITEM) data

The Point of Sale (POS) data in large retail stores can be accessed, but this data has not been utilized to its full extent. Often there is a large amount of complexity and effort spent to translate and store this data into relational databases for later use by business intelligence to generate reports. However, the business intelligence fails to provide insight for accurate decision-making based on this data.

The disclosed invention is a method to capture new insights from the readily- available POS data from controllers. This provides retailers the ability to:


• Increase the sales and profitability


• Identify the popular items and then place them in an easily accessible area or arrange the items identified as impulse purchases in an attractive/ irresistible manner

The invention addresses methods of affinity analysis and describes a method of weighting and combining multiple methods of affinity analysis. The invention is a mechanism to group the items into a larger number of saleable products within the particular demography. It is an automated method to determine which items should be grouped together to form larger saleable units.

The invention is implemented as part of an automated application running on each controller as a background service or as an enterprise application running on a host. In the case of application running on an enterprise host, the Data Integration Facility trickles the POS data transactions to the host, thereby providing POS data to the scanner. Note that the scanner program provides a mechanism to perform affinity analysis and identify the items that should be grouped together as a saleable unit.

The affinity analysis identifies which items can be grouped together by looking at several criteria:

1. Affinity based on ontology alignment metrics (dictionary-based or structural-based). The following steps are performed in this process:


A. Collect the most used keys (Item Code) utilized by each transaction

B. Build semantic information based on each transaction (i.e., extracted from the most used keys) and structure (i.e., extracted from the dependency information retrieved). This information is the item ontology.

C. Perform an automatic alignment of each item's ontology in order to identify which items have ontologies that are closer together. There are two main mechanisms to perform ontology alignment:

i. Dictionary-based, where each keyword is c...