METHOD AND APPARATUS FOR DECISION TREE BASED MARKETING AND SELLING FOR A RETAIL STORE

• US 20080249870A1
• Filed: 09/27/2007
• Published: 10/09/2008
• Est. Priority Date: 04/03/2007
• Status: Abandoned Application

• Current Assignee: Daedalus Blue LLC
• Original Assignee: Daedalus Blue LLC
• Inventors: Kraemer, James R., Angell, Robert Lee
• Application Number:
  US11/862,374
• Publication Number:
  US 20080249870A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

A computer implemented method, apparatus, and computer usable program product for decision tree based marketing to a customer in a retail facility. In response to identifying a customer associated with the retail facility, a marketing decision tree for the customer is retrieved. The marketing decision tree indicates a set of paths through the retail facility that the customer will most likely follow while shopping. A next probable location of the customer is identified using a current location of the customer and the marketing decision tree. A customized marketing message for an item located in the next probable location is presented to the customer.

Claims

1. A computer implemented method for decision tree based marketing to a customer in a retail facility, the computer implemented method comprising:

responsive to identifying a customer associated with the retail facility, retrieving a marketing decision tree for the customer, wherein the marketing decision tree indicates a set of paths through the retail facility that the customer will most likely follow while shopping;

identifying a next probable location of the customer using a current location of the customer and the marketing decision tree; and

generating a customized marketing message for an item located in the next probable location to the customer.

2. The computer implemented method of claim 1 wherein identifying the next probable location further comprises:

identifying items in a shopping container associated with the customer to form current shopping basket contents;

identifying a past purchase history of the customer, wherein the past purchase history indicates items the customer has purchased in the past;

identifying areas of the retail facility traversed by the customer during a current visit to the retail facility to form currently covered areas; and

comparing the current shopping basket contents, the current location of the customer, the currently covered areas, and a probable path in the set of paths in the marketing decision tree to identify the next probable location, wherein the current shopping basket contents are compared to items purchased in the past purchase history to identify additional items the customer is likely to purchase and locations of the additional items in the retail facility, and wherein the next probable location is a location in the locations of the additional items along the probable path.

3. The computer implemented method of claim 1 further comprising:

receiving images of the customer from a set of three or more cameras associated with the retail facility; and

analyzing the images by a smart detection engine to form dynamic data for the customer, wherein the dynamic data includes the current location of the customer in the retail facility.

4. The computer implemented method of claim 3 wherein analyzing the images further comprises:

receiving a series of images of the customer in the retail facility from a set of cameras to form camera data;

analyzing the camera data to form a three dimensional representation of the retail facility, wherein the camera data is used to identify an x-axis, a y-axis and a z-axis for use in generating the three dimensional representation; and

identifying the current location of the customer using the three dimensional representation of the retail facility.

5. The computer implemented method of claim 1 further comprising:

retrieving a customer behavior profile for the customer, wherein the customer behavior profile comprises metadata describing behavior of the customer while shopping during past visits to the retail facility; and

analyzing the customer behavior profile to identify a most probable path in the marketing decision tree.

6. The computer implemented method of claim 1 further comprising:

responsive to a failure of a prediction of a next probable location of the customer by the marketing decision tree, identifying a new current location of the customer and identifying a next most probable location based on the new current location of the customer and the next most likely path through the retail facility indicated by the marketing decision tree, wherein the failure of the prediction of the marketing decision tree is indicated by the customer failing to move to the next probable location designated on the marketing decision tree.

7. The computer implemented method of claim 1 further comprising:

responsive to the customer concluding a transaction at the retail facility to form a most recent transaction, updating the marketing decision tree using a path through the retail facility taken by the customer during the most recent transaction.

8. The computer implemented method of claim 1 further comprising:

presenting a customized marketing message to the customer for an item, wherein the item is located in the current location of the customer.

9. The computer implemented method of claim 1 further comprising:

retrieving a customer behavior profile for the customer, wherein the customer behavior profile comprises grouping data for the customer while shopping in past transactions at the retail facility, wherein the grouping data identifies a grouping category for the customer, and wherein the grouping category is selected from a group consisting of parents with children, teenagers, children, minors unaccompanied by adults, minors accompanied by adults, grandparents with grandchildren, senior citizens, couples, friends, coworkers, a customer shopping with a pet, and a customer shopping alone;

identifying a current grouping category for the customer based on current companions of the customer; and

analyzing the customer behavior profile and current grouping category to generate the marketing decision tree, wherein the marketing decision tree comprises a path through the retail facility that the customer typically follows while shopping with the current grouping category.

10. The computer implemented method of claim 1 further comprising:

receiving data associated with the customer from a set of cameras associated with a retail facility to form detection data for the customer;

processing the detection data, by a smart detection engine, to generate identification data for the customer, wherein the identification data identifies the customer;

retrieving a customer profile for the customer using the customer identification data, wherein the customer profile comprises items purchased during past transactions, previous purchasing patterns, and customer behavior while shopping; and

analyzing the customer profile to generate the marketing decision tree.

11. The computer implemented method of claim 1 further comprising:

retrieving a customer behavior profile for the customer wherein the customer behavior profile indicates customer behavior while shopping in past transactions, and wherein the customer behavior includes at least one of an average speed of walking through the retail facility, a typical time of day for shopping, a typical day of the week for shopping, a frequency of visits to the retail facility over a given time period, an average amount of time spent selecting each item that is purchased, an average number of items purchased during each transaction, and an average number of shopping companions accompanying the customer; and

analyzing the customer behavior profile to generate the marketing decision tree.

12. The computer implemented method of claim 1 further comprising:

receiving data associated with a customer from a set of detectors associated with the retail facility to form detection data;

processing the detection data to form dynamic data for the customer;

analyzing the dynamic data using a set of data models to identify personalized marketing message criteria for the customer;

generating the customized marketing message using the personalized marketing message criteria, wherein the customized marketing message comprises a marketing offer associated with an item in the next probable location; and

delivering the customized marketing message to a display device associated with the customer for display of the customized marketing message to the customer.

13. A computer program product comprising:

a computer usable medium including computer usable program code for decision tree based marketing to a customer in a retail facility, said computer program product comprising;

computer usable program code for retrieving a marketing decision tree for the customer, wherein the marketing decision tree indicates a set of paths through the retail facility that the customer will most likely follow while shopping in response to identifying a customer associated with the retail facility;

computer usable program code for identifying a next probable location of the customer using a current location of the customer and the marketing decision tree; and

computer usable program code for generating presenting a customized marketing message for an item located in the next probable location to the customer.

14. The computer program product of claim 13 wherein identifying a next probable location further comprises:

computer usable program code for identifying items in a shopping container associated with the customer to form current shopping basket contents;

computer usable program code for identifying a past purchase history of the customer, wherein the past purchase history indicates items the customer has purchased in the past;

computer usable program code for identifying areas of the retail facility traversed by the customer during a current visit to the retail facility to form currently covered areas; and

computer usable program code for comparing the current shopping basket contents, the current location of the customer, the currently covered areas, and a probable path indicated in the marketing decision tree to identify the next probable location, wherein the current shopping basket contents are compared to items purchased in the past purchase history to identify additional items the customer is likely to purchase and locations of the additional items in the retail facility, and wherein the next probable location is a location in the locations of the additional items along the probable path.

15. The computer program product of claim 13 further comprising:

computer usable program code for retrieving a customer behavior profile for the customer, wherein the customer behavior profile comprises metadata describing behavior of the customer while shopping during past visits to the retail facility; and

computer usable program code for analyzing the customer behavior profile to generate the marketing decision tree.

16. The computer program product of claim 13 further comprising:

computer usable program code for identifying a new current location of the customer and identifying a next most probable location based on the new current location of the customer and the set of paths in the marketing decision tree in response to a failure of a prediction of the decision tree, wherein the failure of the prediction of the decision tree is indicated by the customer failing to move to the next probable location designated by the marketing decision tree.

17. The computer program product of claim 13 further comprising:

computer usable program code for updating the marketing decision tree using a path through the retail facility taken by the customer during a most recent transaction in response to the customer concluding a transaction at the retail facility to form the most recent transaction.

18. The computer program product of claim 13 further comprising:

computer usable program code for presenting a customized marketing message to the customer for an item, wherein the item is located in the current location of the customer.

19. The computer program product of claim 13 further comprising:

computer usable program code for retrieving a customer behavior profile for the customer, wherein the customer behavior profile comprises grouping data for the customer while shopping in past transactions at the retail facility, wherein the grouping data identifies a grouping category for the customer, and wherein the grouping category is selected from a group consisting of parents with children, teenagers, children, minors unaccompanied by adults, minors accompanied by adults, grandparents with grandchildren, senior citizens, couples, friends, coworkers, a customer shopping with a pet, and a customer shopping alone;

computer usable program code for identifying a current grouping category for the customer based on current companions of the customer; and

computer usable program code for analyzing the customer behavior profile and current grouping category to generate the marketing decision tree, wherein the decision tree comprises a path through the retail facility that the customer typically follows while shopping with the current grouping category.

20. The computer program product of claim 13 further comprising:

computer usable program code for receiving data associated with the customer from a set of cameras associated with a retail facility to form detection data for the customer;

computer usable program code for processing the detection data, by a smart detection engine, to generate identification data for the customer, wherein the identification data identifies the customer;

computer usable program code for retrieving a customer profile for the customer using the customer identification data, wherein the customer profile comprises items purchased during past transactions, previous purchasing patterns, and customer behavior while shopping; and

computer usable program code for analyzing the customer profile to generate the marketing decision tree.

21. A data processing system for decision tree based marketing to a customer in a retail facility, the data processing system comprising:

a bus system;

a communications system connected to the bus system;

a memory connected to the bus system, wherein the memory includes computer usable program code; and

a processing unit connected to the bus system, wherein the processing unit executes the computer usable program code to retrieve a marketing decision tree for the customer in response to identifying a customer associated with the retail facility, the marketing decision tree indicates a set of paths through the retail facility that the customer will most likely follow while shopping;

identify a next probable location of the customer using a current location of the customer and the marketing decision tree; and

generate presenting a customized marketing message for an item located in the next probable location to the customer.

22. The data processing system of claim 21 wherein the processor unit further executes the computer usable program code to identify items in a shopping container associated with the customer to form current shopping basket contents;

identify a past purchase history of the customer, wherein the past purchase history indicates items the customer has purchased in the past;

identify areas of the retail facility traversed by the customer during a current visit to the retail facility to form currently covered areas; and

compare the current shopping basket contents, the current location of the customer, the currently covered areas, and a probable path indicated in the marketing decision tree to identify the next probable location, wherein the current shopping basket contents are compared to items purchased in the past purchase history to identify additional items the customer is likely to purchase and locations of the additional items in the retail facility, and wherein the next probable location is a location in the locations of the additional items along the probable path.

24. The system of claim 22 further comprising:

a set of cameras associated with the retail facility, wherein the set of cameras captures images of the customer in the retail facility; and

a smart detection engine, wherein the smart detection engine analyzes the images to form dynamic data for the customer, wherein the dynamic data includes a current location of the customer in the retail facility.

23. A system for decision tree based marketing to a customer in a retail facility, the system comprising:

an analysis server, wherein the analysis server identifies a customer associated with the retail facility;

retrieves a marketing decision tree for the customer, the marketing decision tree indicates a set of paths through the retail facility that the customer will most likely follow while shopping; and

identifies a next probable location of the customer using a current location of the customer and the marketing decision tree; and

a dynamic marketing message assembly, wherein the dynamic marketing message assembly generates a customized marketing message for an item located in the next probable location to the customer.

25. The system of claim 23 further comprising:

a decision tree generator, wherein the decision tree generator generates the marketing decision tree for the customer using current shopping basket contents, a past purchase history of the customer, wherein the past purchase history indicates items the customer has purchased in the past, and a current location of the customer.