Generating a customer risk assessment using dynamic customer data

• US 8,831,972 B2
• Filed: 09/27/2007
• Issued: 09/09/2014
• Est. Priority Date: 04/03/2007
• Status: Expired due to Fees

• Current Assignee: Daedalus Blue LLC
• Original Assignee: International Business Machines Corporation
• Inventors: Angell, Robert Lee, Kraemer, James R.
• Primary Examiner(s): Brown, Alvin L
• Application Number:
  US11/862,306
• Publication Number:
  US 20080249793A1
• Time in Patent Office: 2,539 Days
• Field of Search: 705/14
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A computer implemented method, apparatus, and computer usable program product for generating a customer risk assessment score. In one embodiment, the process parses dynamic data associated with a customer to identify patterns of events. The dynamic data comprises metadata describing an appearance and behavior of the customer. The patterns of events are analyzed to identify risk assessment factors for the customer. A risk assessment analysis is dynamically performed using the risk assessment factors for the customer to generate a risk assessment score for the customer while the customer is shopping in a retail facility. The risk assessment score indicates a potential risk posed by the customer to the retail facility.

Claims

1. A computer implemented method for generating a customer risk assessment score, the computer implemented method comprising:

parsing dynamic data associated with a customer to identify patterns of events, wherein the dynamic data comprises data elements that are changing in real-time while the customer is shopping in a retail facility, wherein the data elements comprise customer behavior data and responses of the customer to items in the retail facility viewed by the customer;

analyzing, by a processor, the patterns of events to identify risk assessment factors for the customer, wherein the risk assessment factors include a speed at which the potential customer is walking, a determination that the customer is loitering or pacing, and a determination that the potential customer is carrying a bag;

dynamically performing a risk assessment analysis using the risk assessment factors for the customer to generate a risk assessment score for the customer while the customer is shopping in the retail facility, wherein the risk assessment score indicates a potential risk of shoplifting posed by the customer to the retail facility;

ranking the customer based on the risk assessment factors;

responsive to a determination that the risk assessment score is less than an lower threshold score, identifying the customer as a highly desirable customer and initiating aggressive marketing incentives targeted to the customer wherein initiating the aggressive marketing incentives further comprises notifying an employee associated with the retail facility to assist the customer; and

responsive to the risk assessment score indicating that the customer is the undesirable customer, initiating aggressive marketing disincentives targeted to the undesirable customer, wherein the aggressive marketing disincentives comprise creating a negative ambiance in an area of the retail facility associated with the customer.

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

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

analyzing the biometric data with the dynamic data to identify the risk assessment factors for the customer.

3. The computer implemented method of claim 2 wherein analyzing the biometric data further comprising:

detecting a change in a biometric reading associated with the customer that exceeds a threshold change to form the biometric data;

responsive to a determination that the customer was viewing an item or a marketing message when the change in the biometric reading occurred, associating the change in the biometric reading with the item or the marketing message to form the biometric data; and

responsive to a determination that the customer was interacting with another customer, an employee of the retail facility, a child, or an animal, associating the change in the biometric reading with the another customer, the employee, the child, or the animal.

4. The computer implemented method of claim 1 wherein the risk assessment score further indicates a potential risk of theft, violence, failure to pay bills, defaulting on loans, disrupting operations, criminal activities, threatening customers, panhandling, and loitering posed by the customer to the retail facility.

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

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

analyzing the detection data using a set of data models, by a smart detection engine, to form the dynamic data, wherein the dynamic data further comprises metadata describing the customer.

6. The computer implemented method of claim 2 wherein the set of detectors comprises a set of cameras and wherein the detection data is derived from a continuous stream of video data captured by the set of cameras.

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

retrieving a customer profile for the customer, wherein the customer profile comprises static customer data elements describing the customer; and

analyzing the dynamic data with the customer profile to identify the risk assessment factors.

8. The computer implemented method of claim 1 wherein parsing the event data further comprises:

processing the event data using at least one of a statistical method, a data mining method, a causal model, a mathematical model, a marketing model, a behavioral model, a psychological model, a sociological model, or a simulation model.

9. The computer implemented method of claim 1 wherein performing the risk assessment analysis further comprises:

analyzing the risk assessment factors using at least one of a statistical method, a data mining method, and pre-generated manual input to generate weighted risk factors; and

generating the risk assessment score using the weighted risk assessment factors and cohort data for the customer, wherein cohort data comprises data describing the customer.

10. The computer implemented method of claim 1 wherein the dynamic data comprises grouping data for the customer, wherein the grouping data describes a group associated with the customer and further comprising:

generating a risk assessment score for each member of the group associated with the customer.

11. The computer implemented method of claim 10 wherein the group associated with the customer is 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.

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

automatically analyzing the dynamic data to generate customer identification data, wherein the customer identification data identifies the customer without human input, wherein automatically analyzing the dynamic data comprises analyzing the dynamic data using at least one of facial recognition analysis on an image of a face of the customer, license plate recognition analysis on an image of a vehicle license plate, a fingerprint analysis on a fingerprint of the customer, and voice analysis on a sound file.

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

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

processing the current events data to form the dynamic data, wherein analyzing the patterns of events to identify the risk assessment factors for the customer further comprises identifying events in the current events data indicating a potential risk to the retail facility associated with the customer.

14. A computer program product comprising:

a non-transitory computer usable medium including computer usable program code for generating a customer risk assessment score, said computer program product comprising;

computer usable program code for parsing dynamic data associated with a customer to identify patterns of events, wherein the dynamic data comprises data elements that are changing in real-time while the customer is shopping in a retail facility, wherein the data elements comprise customer behavior data and responses of the customer to items in the retail facility viewed by the customer;

computer usable program code for analyzing the patterns of events to identify risk assessment factors for the customer, wherein the risk assessment factors include a speed at which the potential customer is walking, a determination that the customer is loitering or pacing, and a determination that the potential customer is carrying a bag;

computer usable program code for dynamically performing a risk assessment analysis using the risk assessment factors for the customer to generate a risk assessment score for the customer while the customer is shopping in the retail facility, wherein the risk assessment score indicates a potential risk of shoplifting posed by the customer to the retail facility;

computer usable program code for ranking the customer based on the risk assessment factors;

computer usable program code, responsive to a determination that the risk assessment score is less than an lower threshold score, for identifying the customer as a highly desirable customer and initiating aggressive marketing incentives targeted to the customer wherein initiating the aggressive marketing incentives further comprises notifying an employee associated with the retail facility to assist the customer; and

computer usable program code responsive to the risk assessment score indicating that the customer is the undesirable customer, for initiating aggressive marketing disincentives targeted to the undesirable customer, wherein the aggressive marketing disincentives comprise creating a negative ambiance in an area of the retail facility associated with the customer.

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

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

computer usable program code for analyzing the biometric data with the dynamic data to identify the risk assessment factors for the customer, wherein analyzing the biometric data further comprising;

computer usable program code for detecting a change in a biometric reading associated with the customer that exceeds a threshold change to form the biometric data;

computer usable program code for responsive to a determination that the customer was viewing an item or a marketing message when the change in the biometric reading occurred, associating the change in the biometric reading with the item or the marketing message to form the biometric data; and

computer usable program code for responsive to a determination that the customer was interacting with another customer, an employee of the retail facility, a child, or an animal, associating the change in the biometric reading with the another customer, the employee, the child, or the animal.

16. The computer program product of claim 14 wherein the risk assessment score further indicates a potential risk of theft, violence, failure to pay bills, defaulting on loans, disrupting operations, criminal activities, threatening customers, panhandling, and loitering posed by the customer to the retail facility.

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

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

computer usable program code for analyzing the detection data using a set of data models, by a smart detection engine, to form the dynamic data, wherein the dynamic data further comprises metadata describing the customer.

18. The computer program product of claim 17 wherein the set of detectors comprises a set of cameras and wherein the detection data is derived from a continuous stream of video data captured by the set of cameras.

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

computer usable program code for retrieving a customer profile for the customer, wherein the customer profile comprises static customer data elements describing the customer; and

computer usable program code for analyzing the dynamic data with the customer profile to identify the risk assessment factors.

20. The computer program product of claim 14 wherein performing the risk assessment analysis further comprises:

computer usable program code for analyzing the risk assessment factors using at least one of a statistical method, a data mining method, and pre-generated manual input to generate weighted risk factors; and

computer usable program code for generating the risk assessment score using the weighted risk assessment factors and cohort data for the customer, wherein cohort data comprises data describing the customer.

21. A data processing system for generating a customer risk assessment score, 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 parse dynamic data associated with a customer to identify patterns of events, wherein the dynamic data comprises data elements that are changing in realtime while the customer is shopping in a retail facility, wherein the data elements comprise customer behavior data and responses of the customer to items in the retail facility viewed by the customer;

analyze the patterns of events to identify risk assessment factors for the customer, wherein the risk assessment factors include a speed at which the potential customer is walking, a determination that the customer is loitering or pacing, and a determination that the potential customer is carrying a bag;

dynamically perform a risk assessment analysis using the risk assessment factors for the customer to generate a risk assessment score for the customer while the customer is shopping in the retail facility, wherein the risk assessment score indicates a potential risk of shoplifting posed by the customer to the retail facility;

rank the customer based on the risk assessment factors;

in response to a determination that the risk assessment score is less than an lower threshold score, identify the customer as a highly desirable customer and initiate aggressive marketing incentives targeted to the customer wherein initiating the aggressive marketing incentives further comprises notifying an employee associated with the retail facility to assist the customer; and

in response to the risk assessment score indicating that the customer is the undesirable customer, initiate aggressive marketing disincentives targeted to the undesirable customer, wherein the aggressive marketing disincentives comprise creating a negative ambiance in an area of the retail facility associated with the customer.

22. The data processing system of claim 21 wherein the risk assessment score further indicates a potential risk of theft, violence, failure to pay bills, defaulting on loans, disrupting operations, criminal activities, threatening customers, panhandling, and loitering posed by the customer to the retail facility.

23. A system for generating a customer risk assessment score, the system comprising:

an analysis server, wherein the analysis server parses dynamic data associated with a customer to identify patterns of events and analyze the patterns of events to identify risk assessment factors for the customer, wherein the dynamic data comprises data elements that are changing in real-time while the customer is shopping in a retail facility, wherein the data elements comprise customer behavior data and responses of the customer to items in the retail facility viewed by the customer;

a risk assessment engine, wherein the risk assessment engine dynamically performs a risk assessment analysis using the risk assessment factors for the customer to generate a risk assessment score for the customer while the customer is shopping in the retail facility, wherein the risk assessment score indicates a potential risk of shoplifting posed by the customer to the retail facility;

a disincentives generating engine, wherein the disincentives generating engine initiates actions to deter the potential customer from entering the retail facility in response to the ranking indicating that the potential customer poses the possible risk, and initiate aggressive marketing disincentives targeted to the undesirable customer responsive to the risk assessment score indicating that the customer is the undesirable customer, wherein the aggressive marketing disincentives comprise creating a negative ambiance in an area of the retail facility associated with the customer; and

an incentives generating engine, wherein the incentives generating engine initiates aggressive marketing incentives targeted to the customer responsive to a determination that the risk assessment score is less than an lower threshold score, identify the customer as a highly desirable customer, wherein initiating the aggressive marketing incentives further comprises notifying an employee associated with the retail facility to assist the customer.

24. The system of claim 23 further comprising:

a set of biometric sensors associated with the retail facility, wherein the set of biometric sensors captures biometric data associated with the customer, and wherein the risk assessment engine analyzes the biometric data with the dynamic data to identify the risk assessment factors for the customer.

25. The system of claim 23 further comprising:

a set of detectors associated with the retail facility, wherein the set of detectors captures data associated with the customer to form detection data; and

a smart detection engine, wherein the smart detection engine analyzes the detection data using a set of data models to form the dynamic data, wherein the dynamic data further comprises metadata describing the customer.