Customer feedback acquisition and processing system

• US 20030041056A1
• Filed: 09/12/2002
• Published: 02/27/2003
• Est. Priority Date: 07/19/1999
• Status: Active Grant

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Original Assignee: Ameritech Corp. (AT&T, Inc.)
• Inventors: Bossemeyer, Robert Wesley Jr., Connolly, Dennis
• Granted Patent: US 7,058,625 B2
• Time in Patent Office: Days
• US Class Current: 707/3
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: Y10S 707/917 Text
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

A customer feedback acquisition and processing system is disclosed. Customer feedback, which may optionally include voice signals, is captured and stored in a database. The database can be searched to develop a subset of records pertaining to an area of interest. A data mining tool can then be used on the subset to identify trend(s) in the customer feedback records. The database tool assigns relevance scores to each word (“concept”) in one or more fields of the records in the subset. It then combines the concepts and develops new relevance scores for the combined concepts to identify trend(s) in the customer feedback records.

Claims

1. A customer feedback acquisition and processing system comprising:

a service representative terminal for receiving customer feedback messages;

a data acquisition processor in communication with the service representative terminal for developing electronic records including text representative of the customer feedback messages;

a database in communication with the data acquisition processor for storing the records developed by the data acquisition processor;

a data analyst terminal for receiving query inputs; and

a database processor in communication with the database and the data analyst terminal, the database processor being responsive to a query input received from the data analyst terminal to analyze the text in the records stored in the database to identify trends in the customer feedback messages.

2. A customer feedback acquisition and processing system as defined in claim 1 wherein the customer feedback messages comprise at least one of a voice signal received over a telephone, an audio signal, an email message, and data entered in a form from a web page.

3. A customer feedback acquisition and processing system as defined in claim 1 wherein the data acquisition processor comprises a speech recognizer for translating audio signals to text.

4. A customer feedback acquisition and processing system as defined in claim 1 wherein the data acquisition processor comprises a data formatter for determining whether a received customer feedback message includes at least one of a service representative comment, a customer comment, a product code, a reason code, a date and a time.

5. A customer feedback acquisition and processing system as defined in claim 4 wherein the data acquisition processor stores at least one segment of the received customer feedback message in a corresponding one of a service representative field, a customer comment field, a product code field, a reason code field, a date field, and a time field in a record in the database.

6. A customer feedback acquisition and processing system as defined in claim 5 wherein each of the records in the database corresponds to a customer feedback instance.

7. A customer feedback acquisition and processing system as defined in claim 1 wherein the database processor further comprises a search engine and queue generator which is responsive to the query input to produce a search result identifying a subset of the records in the database, the search engine and queue generator also producing a first search queue from the search result, the first search queue including a plurality of concepts, each concept in the first search queue corresponding to a unique word contained in at least one predetermined portion of at least one of the records in the search result.

8. A customer feedback acquisition and processing system as defined in claim 7 wherein the database processor further comprises a relevance finder that computes a relevance score for a given concept in the first search queue.

9. A customer feedback acquisition and processing system as defined in claim 8 wherein the relevance finder comprises a counter that develops a set count at least approximating a number of records in the database containing the given concept in the at least one predetermined portion of the records and that develops a subset count indicative of a number of records in the search result containing the given concept in the at least one predetermined portion of the records.

10. A customer feedback acquisition and processing system as defined in claim 9 wherein the relevance finder further comprises a frequency generator that generates a global frequency for the given concept by dividing the set count for the given concept by a total number of records in the database and that generates a local frequency for the given concept by dividing the subset count for the given concept by a total number of records identified in the search result.

11. A customer feedback acquisition and processing system as defined in claim 10 wherein the relevance finder further comprises a relevance calculator that calculates the relevance score for the given concept by dividing the local frequency by the global frequency.

12. A customer feedback acquisition and processing system as defined in claim 8 wherein the relevance finder computes a relevance score for each concept in the search queue.

13. A customer feedback acquisition and processing system as defined in claim 8 wherein the database processor further comprises a relevance sorter that sorts the concepts in the first search queue based on the relevance scores.

14. A customer feedback acquisition and processing system as defined in claim 7 wherein the database processor further comprises a merger that creates a compound concept by combining at least two concepts in the first search queue.

15. A customer feedback acquisition and processing system as defined in claim 14 wherein the database processor further comprises a relevance finder that computes a relevance score for at least one concept in the first search queue, and the relevance finder cooperates with the merger to calculate a relevance score for the compound concept.

16. A customer feedback acquisition and processing system as defined in claim 15 wherein the merger further comprises an improvement detector for selectively creating a second search queue by adding the compound concept to the first search queue if the relevance score of the compound concept is greater than the relevance score of each of the at least two concepts.

17. A customer feedback acquisition and processing system as defined in claim 15 wherein the merger further comprises an intersection generator for developing a new set list for the compound concept from at least two set lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two set lists identify each record in the database containing the corresponding concept, and wherein the new set list identifies each record appearing in each of the at least two set lists.

18. A customer feedback acquisition and processing system as defined in claim 15 wherein the merger further comprises an intersection generator for developing a new subset list for the compound concept from at least two subset lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two subset lists identify each record in the search result containing the corresponding concept, and wherein the new subset list identifies each record appearing in each of the at least two subset lists.

19. A customer feedback acquisition and processing system as defined in claim 18 wherein the relevance finder develops a new subset count for the compound concept by counting the records identified in the new subset list.

20. A customer feedback acquisition and processing system as defined in claim 19 wherein the relevance finder further comprises a frequency generator that generates a local frequency for the compound concept by dividing the new subset count for the compound concept by a total number of records identified in the search result.

21. A customer feedback acquisition and processing system as defined in claim 20 wherein the relevance finder further comprises a relevance calculator that calculates the relevance score for the compound concept by dividing the local frequency by a global frequency.

22. A customer feedback acquisition and processing system as defined in claim 21 wherein the relevance finder estimates the global frequency of the compound concept by determining a global frequency for each of the at least two concepts and by multiplying the global frequencies of the at least two concepts.

23. A method of acquiring and analyzing customer feedback comprising the steps of:

receiving customer feedback messages;

developing electronic records including text representative of the customer feedback messages;

storing the developed records in a database;

receiving a query input; and

responding to the query input by analyzing the text in the records to identify trends in the customer feedback messages.

24. A method as defined in claim 23 wherein the step of developing electronic records further comprises determining whether a received customer feedback message includes at least one of a service representative comment, a customer comment, a product code, a reason code, a date and a time.

25. A method as defined in claim 24 wherein the step of storing the records further comprises storing at least one segment of the received customer feedback message in a corresponding one of a service representative field, a customer comment field, a product code field, a reason code field, a date field, and a time field in a record in the database.

26. A method as defined in claim 23 wherein the step of responding to the query input by analyzing the text in the records further comprises the steps of:

producing a search result identifying a subset of the records in the database; and

producing a first search queue from the search result, the first search queue including a plurality of concepts, each concept in the first search queue corresponding to a unique word contained in at least one predetermined portion of at least one of the records in the search result.

27. A method as defined in claim 26 wherein the step of responding to the query input by analyzing the text in the records further comprises the step of computing a relevance score for a given concept in the first search queue.

28. A method as defined in claim 27 wherein the step of computing a relevance score for a given concept in the first search queue further comprises the steps of:

developing a set count indicative of a number of records in the database containing the given concept in the at least one predetermined portion of the records; and

developing a subset count indicative of a number of records in the search result containing the given concept in the at least one predetermined portion of the records.

29. A method as defined in claim 28 wherein the step of computing a relevance score for a given concept in the first search queue further comprises the steps of:

generating a global frequency for the given concept by dividing the set count for the given concept by a total number of records in the database; and

generating a local frequency for the given concept by dividing the subset count for the given concept by a total number of records identified in the search result.

30. A method as defined in claim 29 wherein the step of computing a relevance score for a given concept in the first search queue further comprises the steps of calculating the relevance score for the given concept by dividing the local frequency by the global frequency.

31. A method as defined in claim 27 further comprising the step of sorting the concepts in the first search queue based on the relevance scores.

32. A method as defined in claim 26 further comprising the step of creating a compound concept by combining at least two concepts in the first search queue.

33. A method as defined in claim 32 further comprising the steps of:

computing a relevance score for at least one concept in the first search queue; and

calculating a relevance score for the compound concept.

34. A method as defined in claim 33 further comprising the step of selectively creating a second search queue by adding the compound concept to the first search queue if the relevance score of the compound concept is greater than the relevance score of each of the at least two concepts.

35. A method as defined in claim 34 further comprising the steps of:

developing a new subset list for the compound concept from at least two subset lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two subset lists identify each record in the search result containing the corresponding concept, and wherein the new subset list identifies each record appearing in each of the at least two subset lists;

developing a new subset count for the compound concept by counting the records identified in the new subset list;

generating a global frequency for the compound concept;

generating a local frequency for the compound concept by dividing the subset count for the compound concept by a total number of records identified in the search result; and

calculating the relevance score for the compound concept by dividing the local frequency by the global frequency.

36. A method as defined in claim 35 wherein the step of generating a global frequency for the compound concept further comprises the steps of:

determining a global frequency for each of the at least two concepts comprising the compound concept; and

multiplying the global frequencies of the at least two concepts together to estimate the global frequency of the compound concept.

37. For use with a database comprising a plurality of records containing text data, a database mining tool comprising:

a search engine and queue generator responsive to input search criteria for accessing the database to produce a search result identifying a subset of the records in the database, the search engine and queue generator also producing a first search queue from the search result, the first search queue including a plurality of concepts, each concept in the first search queue corresponding to a unique text element contained in at least one of the records in the search result;

a relevance finder for computing a relevance score for the concepts in the first search queue; and

a merger for creating a compound concept by combining at least two concepts in the first search queue, the merger cooperating with the relevance finder to calculate a relevance score for the compound concept and to create a second search queue by adding the compound concept to the first search queue if the relevance score of the compound concept is greater than the relevance score of each of the at least two concepts.

38. A database mining tool as defined in claim 37 wherein the relevance finder comprises a counter that develops a set count indicative of a number of records in the database containing a given concept and that develops a subset count indicative of a number of records in the search result containing the given concept.

39. A database mining tool as defined in claim 38 wherein the relevance finder further comprises a frequency generator that generates a global frequency for the given concept by dividing the set count for the given concept by a total number of records in the database and that generates a local frequency for the given concept by dividing the subset count for the given concept by a total number of records identified in the search result.

40. A database mining tool as defined in claim 39 wherein the relevance finder further comprises a relevance calculator that calculates the relevance score for the given concept by dividing the local frequency by the global frequency.

41. A database mining tool as defined in claim 37 further comprising a relevance sorter that sorts the concepts in the first search queue based on the relevance scores.

42. A database mining tool as defined in claim 37 wherein the merger further comprises an intersection generator for developing a new set list for the compound concept from at least two set lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two set lists identify each record in the database containing the corresponding concept, and wherein the new set list identifies each record appearing in each of the at least two set lists.

43. A database mining tool as defined in claim 37 wherein the merger further comprises an intersection generator for developing a new subset list for the compound concept from at least two subset lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two subset lists identify each record in the search result containing the corresponding concept, and wherein the new subset list identifies each record appearing in each of the at least two subset lists.

44. A database mining tool as defined in claim 43 wherein the relevance finder develops a new subset count for the compound concept by counting the records identified in the new subset list.

45. A database mining tool as defined in claim 44 wherein the relevance finder further comprises a frequency generator that generates a local frequency for the compound concept by dividing the new subset count for the compound concept by a total number of records identified in the search result.

46. A database mining tool as defined in claim 45 wherein the relevance finder further comprises a relevance calculator that calculates the relevance score for the compound concept by dividing the local frequency by a global frequency.

47. A database mining tool as defined in claim 46 wherein the relevance generator generates the global frequency for the compound concept by determining a global frequency for each of the at least two concepts comprising the compound concept, and by multiplying the global frequencies of the at least two concepts together.

48. A method of mining a database including a plurality of records containing text data, the method comprising the steps of:

responding to input search criteria by accessing the database to produce a search result identifying a subset of the records in the database;

producing a first search queue from the search result, the first search queue including a plurality of concepts, each concept in the first search queue corresponding to a unique text element contained in at least one of the records in the search result;

computing a relevance score for the concepts in the first search queue;

creating a compound concept by combining at least two concepts in the first search queue;

computing a relevance score for the compound concept; and

creating a second search queue by adding the compound concept to the first search queue if the relevance score of the compound concept is greater than the relevance score of each of the at least two concepts.

49. A method as defined in claim 48 wherein the step of computing a relevance score for the concepts in the first search queue further comprise the steps of:

developing a set count indicative of a number of records in the database containing a given concept;

developing a subset count indicative of a number of records in the search result containing the given concept;

generating a global frequency for the given concept by dividing the set count for the given concept by a total number of records in the database;

generating a local frequency for the given concept by dividing the subset count for the given concept by a total number of records identified in the search result; and

calculating the relevance score for the given concept by dividing the local frequency by the global frequency.

50. A method as defined in claim 49 further comprising the step of sorting the concepts in the first search queue based on the relevance scores.

51. A method as defined in claim 48 wherein the step of computing a relevance score for the compound concept further comprises the steps of:

developing a new set list for the compound concept from at least two set lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two set lists identify each record in the database containing the corresponding concept, and wherein the new set list identifies each record appearing in each of the at least two set lists;

developing a new subset list for the compound concept from at least two subset lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two subset lists identify each record in the search result containing the corresponding concept, and wherein the new subset list identifies each record appearing in each of the at least two subset lists;

developing a new set count for the compound concept by counting the records identified in the new set list;

developing a new subset count for the compound concept by counting the records identified in the new subset list;

generating a global frequency for the compound concept by dividing the new set count for the compound concept by a total number of records in the database;

generating a local frequency for the compound concept by dividing the new subset count for the compound concept by a total number of records identified in the search result; and

calculating the relevance score for the compound concept by dividing the local frequency by the global frequency.

52. A method as defined in claim 48 wherein the step of computing a relevance score for the compound concept further comprises the steps of:

developing a new subset list for the compound concept from at least two subset lists corresponding to the at least two concepts in the first search queue, wherein each of the at least two subset lists identify each record in the search result containing the corresponding concept, and wherein the new subset list identifies each record appearing in each of the at least two subset lists;

developing a new subset count for the compound concept by counting the records identified in the new subset list;

generating a global frequency for the compound concept;

generating a local frequency for the compound concept by dividing the new subset count for the compound concept by a total number of records identified in the search result; and

calculating the relevance score for the compound concept by dividing the local frequency by the global frequency.

53. A database mining tool as defined in claim 52 wherein the relevance generator generates the global frequency for the compound concept by determining a global frequency for each of the at least two concepts comprising the compound concept, and by multiplying the global frequencies of the at least two concepts together.