System and method for modeling shopping behavior

• US 8,140,378 B2
• Filed: 07/11/2005
• Issued: 03/20/2012
• Est. Priority Date: 07/09/2004
• Status: Active Grant

• Current Assignee: Shopper Scientist LLC
• Original Assignee: Shopper Scientist LLC
• Inventors: Sorensen, Herb
• Primary Examiner(s): PATS, JUSTIN
• Application Number:
  US11/179,306
• Publication Number:
  US 20060010030A1
• Time in Patent Office: 2,444 Days
• Field of Search: 705/7.29, 705/7.31
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

A system and method are provided for modeling shopper behavior in a store. The system may include an analysis program executable on a computing device, configured to receive product position data and shopper path data gathered from a plurality of stores, each having a different shape represented in a corresponding store map. The analysis program is typically configured to spatially define behavioral domains, product categories, and geographic locations that are common to each of the store maps, and compute statistics based on the product position data and the shopper path data, which statistics are normalized by behavioral domain, product category, and geographic location, across the plurality of stores.

Claims

1. A shopping modeling system, comprising:

a computing device including a processing unit; and

an analysis program executable via the processing unit of the computing device, the analysis program configured to;

receive product position data and shopper path data of shoppers gathered from a plurality of stores, each of the plurality of stores having a different shape represented in a corresponding store map;

generate a store model for each store, the store model for a corresponding store including;

a traffic points layer comprising traffic points that resolve shopper path data, each point of the traffic points indicating a relative frequency and/or length of shopper visits within a predefined vicinity of a location;

a product points layer comprising product points that resolve product position data, each point of the product points indicating a particular location of one of a plurality of products in the corresponding store;

a geographic sector layer comprising spatially defined geographic locations defined by a geographic sector map, an aspect ratio of the geographic sector map adjusted to fit the corresponding store map, the geographic sector map configured to divide each store map of the plurality of store maps into a same number of geographic sectors;

a product category layer comprising spatially defined product categories; and

a behavioral domain layer comprising spatially defined behavioral domains representing areas of the corresponding store in which the shoppers exhibit common shopper behaviors, the spatially defined behavioral domains selected from a group consisting of bazaar, service area, center of store aisle, end cap, and checkout;

compute using the store model for each store statistics for the shoppers comprising shopper visits, shops, purchases, buy time, and trip decile statistics based on the product position data and the shopper path data;

normalize said statistics by each behavioral domain defined by the behavior domain layer, each product category defined by the product category layer, and each geographic location defined by the geographic sector layer, across the plurality of stores; and

calculate an expected shopper flow and expected product purchases for a selected location within the corresponding store, based on the normalized statistics.

2. The system of claim 1, wherein the geographic locations are computed by geographic sectors normalized to each of the store maps.

3. The system of claim 1, wherein the geographic locations are computed by a coordinate system normalized to each of the store maps.

4. The system of claim 1, wherein center of store aisles are fractionated by segment according to a normalized aisle depth.

5. The system of claim 4, wherein the center of store aisles are numbered with a normalized aisle index number to indicate relative left to right position in the store.

6. The system of claim 5, wherein the analysis program is configured to receive a store number, normalized index aisle number, segment number, product point number, and product category as inputs for each product point;

and

wherein the analysis program is configured to calculate shopper visits, shops, purchases, buy time, and trip decile as outputs for each product point, based on the inputs.

7. The system of claim 1, wherein the product categories are selected from the group consisting of frozen foods, salty snacks, dairy, meat, produce, bakery, deli, health and beauty, pet care, and carbonated beverage.

8. A computer-executed shopping analysis method, comprising:

at an analysis program executed on a processing unit of a computing device;

receiving product position data and shopper path data of shoppers gathered from a plurality of stores, each of the plurality of stores having a different shape represented in a corresponding store map;

generating a store model for each store, the store model for a corresponding store including;

a traffic points layer comprising traffic points that resolve shopper path data, each point of the traffic points indicating a relative frequency and length of shopper visits within a predefined vicinity of a location;

a product points layer comprising product points that resolve product position data, each point of the product points indicating a particular location of one of a plurality of products in the corresponding store;

a geographic sector layer comprising spatially defined geographic locations defined by a geographic sector map, an aspect ratio of the geographic sector map adjusted to fit the corresponding store map, the geographic sector map configured to divide each store map of the plurality of store maps into a same number of geographic sectors;

a product category layer comprising spatially defined product categories; and

a behavioral domain layer comprising spatially defined behavioral domains representing areas of the corresponding store in which the shoppers exhibit common shopper behaviors, the spatially defined behavioral domains selected from a group consisting of bazaar, service area, center of store aisle, end cap, and checkout;

computing using the store model for each store statistics for the shoppers comprising shopper visits, shops, purchases, buy time, and trip decile statistics based on the product position data and the shopper path data;

normalizing said statistics by each behavioral domain defined by the behavior domain layer, each product category defined by the product category layer, and each geographic location defined by the geographic sector layer, across the plurality of stores; and

calculating an expected shopper flow and expected product purchases for a selected location within the corresponding store, based on the normalized statistics.

9. The method of claim 8, wherein the geographic locations are computed by geographic sectors normalized to each of the store maps.

10. The method of claim 8, wherein the geographic locations are computed by a coordinate system normalized to each of the store maps.

11. The system of claim 8, wherein center of store aisles are fractionated by segment according to a normalized aisle depth.

12. The method of claim 11, wherein the center of store aisles are numbered with a normalized aisle index number to indicate relative left to right position in the store.

13. The method of claim 12, further comprising, at the analysis program,

receiving a store number, normalized index aisle number, segment number, product point number, and product category as inputs for each product point;

and

calculating shopper visits, shops, purchases, buy time, and trip decile as outputs for each product point, based on the inputs.

14. The method of claim 8, wherein the product categories are selected from the group consisting of frozen foods, salty snacks, dairy, meat, produce, bakery, deli, health and beauty, pet care, and carbonated beverage.