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A system and method to optimize category layout based on moving path and categories correlation

IP.com Disclosure Number: IPCOM000248692D
Publication Date: 2016-Dec-27

Publishing Venue

The IP.com Prior Art Database

Abstract

This article proposes a method using shopping path and category correlation to optimize marketplace layout for retailers to maximize their profit and improve shopping environment. The profit of the marketplace would be impacted by its layout can be consider in the following factors Bean et al.(1988): first, the attractiveness of the location in which the category is placed in; Second, possible synergy effects with neighboring product category. Based on this fact, this invention introduces CAF (category attractiveness), PLAF (pure location attractiveness), ISF (Inner stimulation factor between categories) and PLMP (pure location moving probability between locations) based consumers shopping path and history sales. Finally, CLBS (Category layout benefit score) can be calculated for one category layout strategy using above 4 introduced factors and the higher value of CLBS is the better the category layout strategy is.

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A system and method to optimize category layout based on moving path and categories correlation

In a marketplace such as a supermarket, category is the collection of complementary products like fresh category, beverage category and so on. The structure of placing

categories in a marketplace is called category layout. The category layout problem arises both in the planning phase of a new marketplace and on operating phase when

need to adjust layout for some factors, like holidays, season changing, new products introduced and so on. So, category layout is a vital business strategy for retailers,

which directly impacts the consumer purchasing behavior and retailer earnings.

In current retail layout management, lots of researches are dedicated to the shelf allocation, which designs the product mix layout per shelf. It tends to put the whole

category together in one shelf, so what needs to be predetermined is how to allocate categories in the marketplace. However, retailers’ designs of category layout always

rely on experience and conventional rule. It is objective and lack of scientific data support. Without analysis on the historical data of customer purchasing behavior, it is

hard to truly reflect consumers’ requirement on the category layout. In view of the foregoing, it would be beneficial to provide a systematized method of category layout

optimization based on history data to improve shopping environment, moreover, to increase profit for the retail store.

In retail shopping layout, many researches show the customer’s shopping patterns can be analyzed for determining the category in-store layout. The famous study goes to

Why We Buy by(2000) Paco Underhill , who tracks the shoppers in different types of retail stores and uncovers the shoppers’ behavioral patterns, but it only gives some

proposals to enhance consumer’s convenience not considering the merchandising. Jeffrey (2005) collects the complete shopper path in the store and identifies the path

types using a multi variate clustering algorithm; however it is still not applied in shopping layout or category in-store management.

This article proposes a method using shopping path and category correlation to optimize marketplace layout for retailers to maximize their profit and improve shopping

environment. The profit of the marketplace would be impacted by its layout can be consider in the following factors Bean et al .(1988): first, the attractiveness of the

location in which the category is placed in; Second, possible synergy effects with neighboring product category. Based on this fact, this invention introduces CAF (category

attractiveness), PLAF (pure location attractiveness), ISF (Inner stimulation factor between categories) and PLMP (pure location moving probability between locations) based

consumers shopping path and history sales. Finally, CLBS (Category layout benefit score) can be calculated for one category layout strategy using above 4 introduced factors

and the higher value o...