GENERATING CUSTOMIZED MARKETING CONTENT TO IMPROVE CROSS SALE OF RELATED ITEMS

• US 20080249864A1
• Filed: 09/26/2007
• 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:
  US11/861,520
• Publication Number:
  US 20080249864A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0226 Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
• CPC Class Codes: G06Q 30/0253 During e-commerce, i.e. online transactions
• CPC Class Codes: G06Q 30/0271 Personalized advertisement
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

A computer implemented method, apparatus, and computer usable program code for generating customized marketing messages for marketing correlated items. In one embodiment, an item selected by a customer is identified to form a selected item. Items in a list of correlated items associated with the selected item are identified to form a set of correlated items. A correlated item in the set of correlated items provides a different basic functionality than the selected item. A set of dynamic data associated with the customer is analyzed using a set of data models to identify personalized marketing message criteria for the customer. The dynamic data associated with the customer is generated in real-time as the customer is shopping. A customized marketing message is generated using the personalized marketing message criteria. The customized marketing message comprises a marketing message for at least one item in the set of correlated items.

Claims

1. A computer implemented method for generating customized marketing messages for correlated items, the computer implemented method comprising:

identifying an item selected by a customer to form a selected item;

identifying each item in a list of correlated items associated with the selected item to form a set of correlated items, wherein a correlated item in the set of correlated items provides a different basic functionality than the selected item;

analyzing a set of dynamic data associated with the customer using a set of data models to identify personalized marketing message criteria for the customer, wherein the dynamic data associated with the customer is generated in real-time as the customer is shopping;

generating a customized marketing message using the personalized marketing message criteria, wherein the customized marketing message comprises a marketing message for at least one item in the set of correlated items.

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

receiving data from a set of detectors located externally to a retail facility to form external data, wherein the external data is data describing the customer while the customer is located outside a retail facility; and

processing the external data to form the dynamic data.

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

selecting the set of correlated items from the list of correlated items using the dynamic data, wherein the dynamic data is used to select at least one correlated item in the list of correlated items that is most likely to be purchased by the customer to form the set of correlated items.

4. The computer implemented method of claim 1 wherein the customer is a customer in a set of customers and further comprising:

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

processing the detection data for the set of customers to form the dynamic data, wherein the dynamic data comprises grouping data for the customer, 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; and

generating the customized marketing message for the customer using the grouping data.

5. The computer implemented method of claim 4 further comprising:

identifying items in the list of correlated items that are frequently purchased by customers in the grouping category for the customer to form the set of correlated items.

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

receiving external marketing data from a set of sources to form current events data;

processing the current events data to form the dynamic data; and

responsive to a determination that the current events data indicates an event of interest to the customer occurs within a predetermined period of time, identifying items in the list of correlated items associated with the event of interest to form the set of correlated items.

7. 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; and

identifying each item in the list of correlated items that the customer has purchased in the past using the customer profile to form the set of correlated items.

8. 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 identify patterns of events to form customer behavior data, wherein customer behavior data comprises data describing events associated with the customer in the retail facility;

processing the customer behavior data to form the dynamic data; and

identifying items in the list of correlated items using the customer behavior data to form the set of correlated items.

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

responsive to a determination that the dynamic data indicates a shopping preference of the customer, identifying items in the list of correlated items associated with the shopping preference to form the set of correlated items.

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

prompting the customer to purchase the at least one correlated item and purchase the selected item using the customized marketing message, wherein a sale of the at least one correlated item and purchase the selected item produces a greater amount of revenue or a greater amount of profit than a sale of the selected item alone.

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

identifying a plurality of items purchased by a set of customers;

analyzing the plurality of items using a set of correlation analysis techniques to identify items that are purchased in correlation with one or more other items to form a list of correlated items.

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

identifying a percentage of the set of customers that purchase the selected item in correlation with a given different item;

identifying the given different item as a correlated item associated with the selected item in response to the percentage of the set of customers exceeding a threshold percentage.

13. A computer program product comprising:

a computer usable medium including computer usable program code for generating customized marketing messages for correlated items, said computer program product comprising;

computer usable program code for identifying an item selected by a customer to form a selected item;

computer usable program code for identifying each item in a list of correlated items associated with the selected item to form a set of correlated items, wherein a correlated item in the set of correlated items provides a different basic functionality than the selected item;

computer usable program code for analyzing a set of dynamic data associated with the customer using a set of data models to identify personalized marketing message criteria for the customer, wherein the dynamic data associated with the customer is generated in real-time as the customer is shopping;

computer usable program code for generating a customized marketing message using the personalized marketing message criteria, wherein the customized marketing message comprises a marketing message for at least one item in the set of correlated items.

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

computer usable program code for receiving data from a set of detectors located externally to a retail facility to form external data, wherein the external data is data describing the customer while the customer is located outside a retail facility; and

computer usable program code for processing the external data to form the dynamic data.

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

computer usable program code for selecting the set of correlated items from the list of correlated items using the dynamic data, wherein the dynamic data is used to select at least one correlated item in the list of correlated items that is likely to be purchased by the customer to form the set of correlated items.

16. The computer program product of claim 13 wherein the customer is a customer in a set of customers and further comprising:

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

computer usable program code for processing the detection data for the set of customers to form the dynamic data, wherein the dynamic data comprises grouping data for the customer, 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, and a customer shopping alone; and

computer usable program code for identifying each item in the list of correlated items that are frequently purchased by customers in the grouping category for the customer to form the set of correlated items.

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

computer usable program code for receiving external marketing data from a set of sources to form current events data;

computer usable program code for processing the current events data to form the dynamic data; and

computer usable program code for identifying items in the list of correlated items associated with the event of interest to form the set of correlated items in response to a determination that the current events data indicates an event of interest to the customer occurs within a predetermined period of time.

18. 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; and

computer usable program code for identifying items in the list of correlated items that the customer has purchased in the past using the customer profile to form the set of correlated items.

19. 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 identify patterns of events to form customer behavior data, wherein customer behavior data comprises data describing events associated with the customer in the retail facility;

computer usable program code for processing the customer behavior data to form the dynamic data; and

computer usable program code for identifying items in the list of correlated items using the customer behavior data to form the set of correlated items.

20. A data processing system for generating customized marketing messages for a customer, 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 identify an item selected by a customer to form a selected item;

identify each item in a list of correlated items associated with the selected item to form a set of correlated items, wherein a correlated item in the set of correlated items provides a different basic functionality than the selected item;

analyze a set of dynamic data associated with the customer using a set of data models to identify personalized marketing message criteria for the customer, wherein the dynamic data associated with the customer is generated in real-time as the customer is shopping;

generate a customized marketing message using the personalized marketing message criteria, wherein the customized marketing message comprises a marketing message for at least one item in the set of correlated items.

21. The data processing system of claim 20 wherein the processor unit further executes the computer usable program code to select the set of correlated items from the list of correlated items using the dynamic data, wherein the dynamic data is used to select at least one correlated item in the list of correlated items that is likely to be purchased by the customer to form the set of correlated items.

22. A system for generating customized marketing messages for correlated items, the system comprising:

an analysis server, wherein the analysis server identifies an item selected by a customer to form a selected item;

identifies each item in a list of correlated items associated with the selected item to form a set of correlated items, wherein a correlated item in the set of correlated items provides a different basic functionality than the selected item;

analyzes a set of dynamic data associated with the customer using a set of data models to identify personalized marketing message criteria for the customer, wherein the dynamic data associated with the customer is generated in real-time as the customer is shopping; and

a dynamic marketing message assembly, wherein the dynamic marketing message assembly generates a customized marketing message using the personalized marketing message criteria, wherein the customized marketing message comprises a marketing message for at least one correlated item in the set of correlated items.

23. The system of claim 22 further comprising:

a display device, wherein the display device displays the customized marketing message, wherein the customized marketing message prompts the customer to purchase the at least one correlated item in addition to a purchase of the selected item using the customized marketing message.

24. The system of claim 22 further comprising:

a set of detectors located externally to a retail facility, wherein the set of detectors gathers data associated with the customer to form external data, wherein the external data is data describing the customer in real time while the customer is located outside a retail facility; and

processing the external data to form the dynamic data.

25. The system of claim 22 further comprising:

a set of detectors located internally to a retail facility, wherein the set of detectors gathers data associated with the customer to form detection data;

analyzing the detection data using a set of data models to form the dynamic data; and

processing the detection data to form the dynamic data.