Methods and system for allocating shelf space

• US 7,734,495 B2
• Filed: 04/23/2002
• Issued: 06/08/2010
• Est. Priority Date: 04/23/2002
• Status: Expired due to Fees

• Current Assignee: Kimberly-Clark Worldwide Incorporated (Kimberly-Clark Corporation)
• Original Assignee: Kimberly-Clark Worldwide Incorporated (Kimberly-Clark Corporation)
• Inventors: Klaubauf, Christine M., Lindsay, Jeff
• Primary Examiner(s): Boswell; Beth V.
• Assistant Examiner(s): PATS, JUSTIN
• Application Number:
  US10/128,533
• Publication Number:
  US 20030200129A1
• Time in Patent Office: 2,968 Days
• Field of Search: 705/10
• US Class Current: 705/7.25
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

A method and system for the collection of consumer sales data for use in intelligent management of shelf space in a retail environment based on quantitative analysis of consumer sales data in which interactions between two or more products are quantitatively applied in estimating sales for a particular selection of the shelf space location and/or shelf space amount. Such management may be achieved manually or automatically with the invention.

Claims

1. A computer implemented method of allocating retail shelf space for a product segment based on sales data indicating past transactions of consumers of products in the product segment, said method comprising:

defining a product segment of products based on a shared characteristic of the products;

defining a plurality of buyer groups, each group based on consumers who purchase a first product from said defined product segment within a defined time period;

for each buyer group, determining a preference for the consumers in each buyer group to purchase a second product from the defined product segment via a computer processor, said second product being other than the first product and said second product serving a different primary purpose than said first product, said determining comprising calculating a preference index relating the first product and the second product as a function of the sales data; and

allocating the retail shelf space for the defined product segment of products based on the determined preference,

wherein determining the preference comprises calculating the preference index as a function of an expected product segment mix and an expected buyer group mix, wherein the expected product segment mix corresponds to a percentage of the second product purchased with respect to the total number of products purchased within the product segment after excluding the first products, wherein the expected buyer group mix corresponds to the percentage of the second product purchased with respect to the total number of products purchased by the buyer group after excluding the first products.

2. The method of claim 1 wherein said sales data comprises point of sale data, household panel data, commercial consumer sales data, and/or data stored on a computer readable medium.

3. The method of claim 2, wherein said sales data further comprises virtual shopping data.

4. The method of claim 1 wherein said defined time period is one year.

5. The method of claim 1 wherein allocating the retail shelf space comprises assigning shelf space positions which are closer to the position of the first product to one or more of the other products which have a relatively higher preference as compared to the preference of the other remaining products.

6. The method of claim 1 further comprising calculating the preference index for each of the other products in the product segment relative to the first product as a function of the sales data, wherein allocating the retail shelf space comprises, as between two products, assigning an amount of retail shelf space to each of the two products based on a comparison of the preference indices of each of the two products.

7. The method of claim 6 wherein allocating the retail shelf space comprises determining a common characteristic of products with higher preference indices via the computer processor and assigning preferentially shelf space to selected products which have the common characteristic.

8. The method of claim 1 wherein the step of allocating the retail shelf space based on said preference increases sales for retailers.

9. A computer implemented method of allocating retail shelf space for a product segment of products based on sales data indicating past purchases of consumers of products in the product segment, said method comprising:

defining a product segment of products based on a shared characteristic of the products;

defining a plurality of buyer groups, each group based on consumers who purchase a first product in said product segment within a defined time period;

for each buyer group, determining a preference for the consumers in each buyer group to purchase each of the other products within the product segment via a computer processor, said determining comprising calculating a preference index as a function of the sales data for each of the other products relative to the product that defines the buyer group, wherein at least one of each of the other products serves a different primary purpose than the first product;

predicting a sales impact based on the preference for the consumers in each buyer group to purchase each of the other products within the product segment; and

allocating the retail shelf space based on said predicted sales impact,

wherein determining the preference comprises calculating the preference index as a function of an expected product segment mix and an expected buyer group mix, wherein the expected product segment mix corresponds to a percentage of the other products purchased with respect to the total number of products purchased within the product segment after excluding the first products, wherein the expected buyer group mix corresponds to the percentage of the other products purchased with respect to the total number of products purchased by the buyer group after excluding the first products.

10. The method of claim 9 wherein allocating the retail shelf space based on said predicted sales impact achieves a sales objective and/or a profitability objective.

11. The method of claim 9 further comprising modifying a planogram to allocate shelf space by comparing the determined preferences of a plurality of buyer groups.

12. The method of claim 9 wherein said sales data comprises one or more of the following:

point of sale data, household panel data, shopping studies, and commercial consumer sales data.

13. The method of claim 9 wherein said shared characteristic is selected from one or more of the following:

price;

a manufacturer name;

a brand name; and

an identifiable classification.

14. A system for allocating retail shelf space for a product segment of products based on sales data indicating past transactions of consumers of products in the product segment, said system comprising:

data collection instructions for collecting data relating to purchases by a plurality of defined buyer groups from within a defined product segment, wherein the product segment is defined based on a shared characteristic of the products, and wherein each of the groups is defined based on consumers who purchase a first product in said product segment within a defined time period;

a database for collecting sales data; and

a computer system which, for each buyer group, determines a preference for the consumers in each buyer group to purchase a second product within the product segment, said second product being other than the first product of the buyer group and said second product serving a different primary purpose than said first product, wherein the computer system calculates a preference index relating the first product and the second product as a function of the sales data, said computer system further allocating the retail shelf space based on said determined preference,

wherein determining the preference comprises calculating the preference index as a function of an expected product segment mix and an expected buyer group mix, wherein the expected product segment mix corresponds to a percentage of the second product purchased with respect to the total number of products purchased within the product segment after excluding the first products, wherein the expected buyer group mix corresponds to the percentage of the second product purchased with respect to the total number of products purchased by the buyer group after excluding the first products.

15. The system of claim 14, wherein the database is further configured for storing inventory data for a plurality of point of sale retail locations.

16. The system of claim 14, wherein the database is further configured for storing, retrieving, and/or deleting information relating to consumer sales data for one or more products and/or the preference for the consumers in each buyer group to purchase each of the other products within the product segment, other than the first product.

17. The system of claim 14 wherein the computer system is responsive to the data collection instructions and/or the database for determining a preference for the consumers in each buyer group to purchase each of the other products within the product segment, other than the first product.

18. The system of claim 14 further comprising a server connected to the computer system via a data communication network, said server storing and retrieving information relating to consumer sales data for one or more products and/or quantified sales interaction parameters between the first and second products.

19. The system of claim 18 wherein said server retrieves consumer sales data from one or more of the following:

a loyalty card data source;

a commercial consumer sales entity (CCSE);

a shopping study source; and

a computer readable medium.

20. The system of claim 14, wherein the computer system is configured for storing, retrieving, and/or deleting information relating to consumer sales data for one or more products.

21. The system of claim 14, wherein the determined preference is a function of quantified sales interaction parameters further including one or more of the following:

an actual product segment mix and an actual buyer group mix (ABGM).

22. The system of claim 14, wherein the computer system is further configured to present for viewing and/or printing the information relating to consumer sales data for one or more products.