Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Discount or incentive
Reexamination Certificate
1998-02-27
2002-04-02
Millin, Vincent (Department: 2165)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Discount or incentive
C705S028000, C705S029000
Reexamination Certificate
active
06366890
ABSTRACT:
BACKGROUND OF THE INVENTION
Technical Field of the Invention
In the field of retail product sales, a method and system for categorizing products and optimizing the variety of products carried at a retail store based upon an analysis of retail sales data for a geographic region and a chain of stores.
DESCRIPTION OF RELATED ART INCLUDING INFORMATION DISCLOSED UNDER 37 CFR 1.97 AND 1.98
Retail sales of packaged food items is a very competitive business offering a wide variety of products. For example, a single food manufacturer may produce food items based upon different food types (e.g. potato chip, corn chip, cheese puff), flavoring (e.g.. salted, Bar-B-Que, ranch dressing, Mexican spicy seasoning, etc.), package sizes (e.g.. 32 oz., 24 oz., 12 oz), and package types (e.g., bag, tin, tub, canister). A retail store must select its assortment from the wide variety of products available in the market. Preferably, the retail store would like to select the most popular goods for its assortment.
Many forces contribute to the popularity of a particular packaged food item such as marketing campaigns, the seasons of the year, and the consumer appeal in a specific geographic area. For instance, a marketing campaign directed toward publicizing a certain packaged food item could increase the demand for that food item significantly over competing goods and the prior sales performance for that food item. Further, the summer months may make one packaged food item more appealing, while colder weather may have a reverse impact on the sales of that same product. People living in different geographic areas may find certain packaged food items more appealing than others. For instance, people in the southern United States may find corn-based snack food items more appealing than the northern United States, while those people in the midwest United States may find potato-based packaged snack food items more appetizing.
The packaged food items with greater popularity will inevitably sell much faster than packaged food items of less popularity. Greater sales usually translates into greater profits. Of all products in the market, the most popular products only comprise a small percentage of the total number of items available. In fact, it has been estimated that twenty percent (20%) of the most popular food items comprise approximately eighty percent (80%) of all sales in that category of food products. Moreover, fifty to sixty percent (50%-60%) of the least popular packaged food items comprise approximately ten percent (10%) of total sales in that category of food product. Accordingly, there is a great disparity between the total sales percentages for the most popular twenty percent (20%) of the packaged food products and the least popular fifty to sixty percent (50%-60%)of the packaged food products.
A representative sales curve can be seen in
FIG. 32
which shows the cumulative percent of total supermarket chain sales volume for a geographic area for the cumulative percent of total items available in that supermarket chain. In
FIG. 32
, the top twenty percent (20%) of products available in the supermarket comprise approximately seventy-five percent (75%) of the total supermarket sales volume. Further, the lower sixty percent (60%) of available products comprise approximately ten percent (10%) of the total sales volume. Because the top twenty percent (20%) of products will generate more profits than the lower (60%) of products, a retailer wants to focus upon a product assortment which emphasizes the top twenty percent (20%) of popular products while de-emphasizing (or eliminating) the lower sixty percent (60%) of the less popular products. In order to optimize a product selection, there is a need to identify the more popular top percentage of products and the lower less popular percentage of products.
Optimizing the profitability of your product assortment is even more important considering the limited shelf space available at grocery stores and supermarkets for each type of food product. As discussed in U. S. Pat. No. 4,112,598 to Maass and U.S. Pat. No. 4,947,322 to Temma, the layout of shelf space at retail stores can be modeled to optimize the shelf space as a limited resource. For the same amount of shelf space, the top percentage of popular food products will return greater profits than the least popular food products. Accordingly, grocery stores and supermarkets have a desire to maximize their profits from their finite amount of shelf space by allocating the most amount of space for the most popular food products and the least amount of space (or no space) for the least popular food products.
Computerized inventory systems are discussed in U. S. Patent No. 4,737,910 to Kimbrow, U.S. Pat. No. 4,797,819 to Dechirot, U.S. Pat. No. 4,972,318 to Brown, U.S. Pat. No. 4,783,740 to Ishizawa, U.S. Pat. No. 4,654,800 to Hayashi and U.S. Pat. No. 4,639,875 to Abraham. While these inventory systems track inventory levels and determine replenishment requirements for a product based upon sales data, these systems do not assist with the determination of the popular variety of goods or the optimization of product selection to maximize profits.
U.S. Pat. No. 5,237,496 to Kagimi, U.S. Pat. No. 5,128,861 to Kagimi, and U.S. Pat. No. 5,313,392 to Temma attempt to forecast future replenishment needs based upon planned sales and/or actual sales of products in inventory. While the products in inventory can be analyzed vis-a-vis their predicted sales or the sales of other goods in inventory, these patents only focus on the products already in inventory. Other popular products may exist in the market which are not carried in inventory, and if added to the store's assortment, would increase the profitability for that store. By looking beyond an actual store's current inventory to other stores in a chain or all stores in a geographic area, a greater number of the popular products and the less popular products can be identified for the geographic area or a chain of stores. A significant need exists with determining the popular products for all stores in a geographic region or for a chain of stores.
Retail sales data is available for most stores in a geographic region and for specific chains of stores in a geographic region, but the volume of this data makes it difficult to analyze in a quick and comprehensible manner. As such, a further problem exists with the ability to flexibly categorize products based on retail sales data and manipulate this large amount of retail sales data in a rapid manner to produce easy to understand tables and graphs which demonstrate the popularity of a variety of a food item and the optimal assortment of food items for a store. Accordingly, there is a significant need to analyze large amounts of retail sales data in a rapid manner, focus upon certain flexibly-defined categories of products in an assortment analysis, accurately identify the success or failure of a product assortment to carry the most popular revenue generating products, accurately identify the popularity of products (from most to least popular) for the geographic market or a chain of stores, and predict how changes in the assortment of products carried by the store would affect the revenues of a store.
SUMMARY OF THE INVENTION
It is therefore an object of the present invention to provide retail stores in general, and grocery and supermarket stores in particular, with a system capable of analyzing large amounts of retail sales data for all products and all manufacturers in a given product category sold in a geographic market and/or a chain of stores in a quick manner. Types of data analyzed by the present system include:
Chain Data—for a given product category, this data would reflect sales of items from all producers for a store or chain of stores.
Market Data—for a given product category, this data would reflect sales of items from all producers for a given market (e.g. geographical). This data can be combined to show how a chain in a specific market is performing relative to the entire market.
Another object of th
Hemingway D. Scott
Millin Vincent
Nguyen Cuong H.
The Law Offices of D. Scott Hemingway
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