Systems, methods, and computer program products for optimizing communications with selected product providers and users by identifying trends in transactions between product providers and users

• US 20060265429A1
• Filed: 05/17/2005
• Published: 11/23/2006
• Est. Priority Date: 05/17/2005
• Status: Abandoned Application

• Current Assignee: Travelocity.com LP (Expedia Group, Inc.)
• Original Assignee: Travelocity.com LP (Expedia Group, Inc.)
• Inventors: Slean, Kevin, Pendergast, Richard
• Application Number:
  US11/130,521
• Publication Number:
  US 20060265429A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

A system, method, and computer program product are provided for automatically identifying trends in the number of transactions occuring between selected users and selected product providers in order to determine which product providers and/or users are most productive or least productive over the course of a selected number of time periods such that marketing or communications may be focused on selected product providers and/or users exhibiting the most extreme upward or downward transactional trends. The system of the present invention determines and stores the number of transactions that occur between users (such as individual customers and/or affiliates) and a product provider (such as a hotel) over the course of a selected number of time periods and compares the stored transactional data to the number of transactions determined over the course of a recent number of selected time periods in order to determine a transactional trend.

Claims

1. A system for identifying trends in transactional activity of one or more transaction systems, said system comprising:

at least one transaction system capable of performing transactions; and

a tracking system in communication with said transaction system,

wherein said tracking system;

tracks the number of transactions made by the transaction system for different time periods;

determines an average number of transactions for the transaction system based on the transactions over different time periods; and

compares the average number of transactions to the number of transactions for a selected time period to identify a trend in the number of transactions for the selected time period.

2. A system according to claim 1, wherein said tracking system determines the average number of transactions for an N number of time periods and the number of transactions for an N+1 time period and compares the average number of transactions for the N number of time periods to the number of transactions for the N+1 time period.

3. A system according to claim 1, wherein the time period is one of a day, a week, a month, a quarter of a year, or a year.

4. A system according to claim 1, wherein said tracking system compares the transactions for successive time periods to the average number of transactions, wherein the comparisons define a slope.

5. A system according to claim 4, wherein said tracking system applies a scaling factor to the determined slope.

6. A system according to claim 5, wherein the time periods have a selected duration, and wherein the scaling factor has a value that is dependent on the duration of the time periods.

7. A system according to claim 4, wherein said tracking system applies a scaling factor to the determined slope, the scaling factor having a greater absolute value corresponding to longer selected time periods and a lesser absolute value corresponding to shorter selected time periods.

8. A system according to claim 1, wherein said tracking system calculates a slope F[X] representing a trend in the number of transactions made by a transaction system, said tracking system determining the number of transactions for a current time period N[0] and the number of transaction for a preceeding time period N[−

1], and calculates the slope using the following formula;

F[X]=(N[0]−

N[−

1])/N[−

1].

9. A system according to claim 8, wherein said tracking system applies a scaling factor K[0] to the determined slope F[X], wherein the time periods have a selected duration, and wherein the scaling factor has a value that is dependent on the duration of the time periods.

10. A system according to claim 1, wherein said tracking system calculates a slope F[X] representing a trend in the number of transactions made by a transaction system, wherein said slope F[X] is calculated based on a plurality of data samples (N[0], N[−

1], N[−

2], N[−

3] . . . N[−

n]) each representing the number of number of transactions for a time period N[x], wherein the calculation of the slope for eight samples (N[0], N[−

1], N[−

2], N[−

3], N[−

4], N[−

5], N[−

6], N[−

7]) is;

11. A system according to claim 10, wherein said tracking system determines an average number of transactions A[X] for the eight samples (N[0], N[−

1], N[−

2], N[−

3], N[−

4], N[−

5], N[−

6], N[−

7]), and calculates an averaged slope FA[0] using the following formula;

FA[0]=(F[X]*A[X])/A[X].

12. A system according to claim 11, wherein said tracking system determines a filtered slope using the formula:

FR[X]=F[X]−

FA[0].

13. A system according to claim 1, wherein said tracking system determines a trend value representing the number of transactions for a selected time period for a plurality of transaction systems, and identifies at least one of transaction systems having upward trends and transaction systems having downward trends.

14. A system according to claim 13, wherein said tracking system compares the trend value for each transaction system to a first threshold value, and identifies transaction systems having an associated trend value at least as great as the first threshold.

15. A system according to claim 13, wherein said tracking system compares the trend value for each transaction system to a second threshold value, and identifies transaction systems having an associated trend value less that the second threshold.

16. A system according to claim 1, wherein the transactions tracked by said tracking system are at least one of:

a number of website hits associated with the transaction system;

a purchases on the transaction system; and

inquiries concerning products offered by the transaction system.

17. A system according to claim 1, wherein the transactions tracked by said tracking system are instances when the transaction system performs a transaction on a selected system.

18. A system according to claim 1, wherein said transaction system is a computer reservation system.

19. A method for identifying trends in transactional activity of one or more transaction systems, said method comprising:

providing at least one transaction system capable of performing transactions;

tracking the number of transactions made by the transaction system for different time periods;

determining an average number of transactions for the transaction system based on the transactions over different time periods; and

comparing the average number of transactions to the number of transactions for a selected time period to identify a trend in the number of transactions for the selected time period.

20. A method according to claim 19, wherein said determining step determines the average number of transactions for an N number of time periods and the number of transactions for an N+1 time period and said comparing step compares the average number of transactions for the N number of time periods to the number of transactions for the N+1 time period.

21. A method according to claim 19, wherein the time period is one of a day, a week, a month, a quarter of a year, or a year.

22. A method according to claim 19, wherein said said comparing step compares the transactions for successive time periods to the average number of transactions, wherein the comparisons define a slope.

23. A method according to claim 22 further comprising applying a scaling factor to the determined slope.

24. A method according to claim 23, wherein the time periods have a selected duration, and wherein the scaling factor has a value that is dependent on the duration of the time periods.

25. A method according to claim 23, wherein said applying step applies a scaling factor to the determined slope, the scaling factor having a greater absolute value corresponding to longer selected time periods and a lesser absolute value corresponding to shorter selected time periods.

26. A method according to claim 19 further comprising calculating a slope F[X] representing a trend in the number of transactions made by a transaction system, said determining step determining the number of transactions for a current time period N[0] and the number of transaction for a preceeding time period N[−

1], and said calculating step calculating the slope using the following formula;

F[X]=(N[0]−

N[−

1])/N[−

1].

27. A method according to claim 26 further comprising applying a scaling factor K[0] to the determined slope F[X], wherein the time periods have a selected duration, and wherein the scaling factor has a value that is dependent on the duration of the time periods.

28. A method according to claim 19 further comprising calculating a slope F[X] representing a trend in the number of transactions made by a transaction system, wherein said slope F[X] is calculated based on a plurality of data samples (N[0], N[−

1], N[−

2], N[−

3] . . . N[−

n]) each representing the number of number of transactions for a time period N[x], wherein the calculation of the slope for eight samples (N[0], N[−

1], N[−

2], N[−

3], N[−

4], N[−

5], N[−

6], N[−

7]) is;

29. A method according to claim 28 further comprising determining an average number of transactions A[X] for the eight samples (N[0], N[−

1], N[−

2], N[−

3], N[−

4], N[−

5], N[−

6], N[−

7]), and said calculating step calculating an averaged slope FA[0] using the following formula;

FA[0]=(F[X]*A[X])/A[X].

30. A method according to claim 29 further comprising determining a filtered slope using the formula:

FR[X]=F[X]−

FA[0].

31. A method according to claim 19 further comprising determining a trend value representing the number of transactions for a selected time period for a plurality of transaction systems, and at least one of identifying transaction systems having upward trends and transaction systems having downward trends.

32. A method according to claim 31, wherein said comparing step compares the trend value for each transaction system to a first threshold value, and identifies transaction systems having an associated trend value at least as great as the first threshold.

33. A method according to claim 31, wherein said comparing step compares the trend value for each transaction system to a second threshold value, and identifies transaction systems having an associated trend value less that the second threshold.

34. A method according to claim 19, wherein the transactions tracked by said tracking step are at least one of:

a number of website hits associated with the transaction system;

a purchases on the transaction system; and

inquiries concerning products offered by the transaction system.

35. A method according to claim 19, wherein the transactions tracked by said tracking step are instances when the transaction system performs a transaction on a selected system.

36. A method according to claim 19, wherein said transaction system is a computer reservation system.

37. A computer program product for identifying trends in transactional activity of one or more transaction systems, said computer program product comprising a computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

first computer instruction means providing at least one transaction system capable of performing transactions;

second computer instruction means tracking the number of transactions made by the transaction system for different time periods;

third computer instruction means determining an average number of transactions for the transaction system based on the transactions over different time periods; and

fourth computer instruction means comparing the average number of transactions to the number of transactions for a selected time period to identify a trend in the number of transactions for the selected time period.

38. A computer program product according to claim 37, wherein said third computer instruction means determines the avearage number of transactions for an N number of time periods and the number of transactions for an N+1 time period and said fourth computer instruction means compares the average number of transaction for the N number of time periods to the number of transactions for the N+1 time period.

39. A computer program code according to claim 37, wherein the time period is one of a day, a week, a month, a quarter of a year, or a year.

40. A computer program product according to claim 37, wherein said said fourth computer instruction means compares the transactions for successive time periods to the average number of transactions, wherein the comparisons define a slope.

41. A computer program product according to claim 40 further comprising fifth computer instruction means for applying a scaling factor to the determined slope.

42. A computer program product according to claim 41, wherein the time periods have a selected duration, and wherein the scaling factor has a value that is dependent on the duration of the time periods.

43. A computer program product according to claim 41, wherein said fifth computer instruction means for applying a scaling factor to the determined slope, the scaling factor having a greater absolute value corresponding to longer selected time periods and a lesser absolute value corresponding to shorter selected time periods.

44. A computer program product according to claim 37 further comprising fifth computer instruction means calculating a slope F[X] representing a trend in the number of transactions made by a transaction system, said third computer instruction means determines the number of transactions for a current time period N[0] and the number of transaction for a preceeding time period N[−

1], and said fifth computer instruction means calculating the slope using the following formula;

F[X]=(N[0]−

N[−

1])/N[−

1].

45. A computer program product according to claim 44 further comprising sixth computer instruction means for applying a scaling factor K[0] to the determined slope F[X], wherein the time periods have a selected duration, and wherein the scaling factor has a value that is dependent on the duration of the time periods.

46. A computer program product according to claim 37 further comprising fifth computer instruction means for calculating a slope F[X] representing a trend in the number of transactions made by a transaction system, wherein said slope F[X] is calculated based on a plurality of data samples (N[0], N[−

1], N[−

2], N[−

3] . . . N[−

n]) each representing the number of number of transactions for a time period N[x], wherein the calculation of the slope for eight samples (N[0], N[−

1], N[−

2], N[−

3], N[−

4], N[−

5], N[−

6], N[−

7]) is;

47. A computer program product according to claim 46 further comprising sixth computer instruction means for determining an average number of transactions A[X] for the eight samples (N[0], N[−

1], N[−

2], N[−

3], N[−

4], N[−

5], N[−

6], N[−

7]), and said calculating step calculating an averaged slope FA[0] using the following formula;

FA[0]=(F[X]*A[X])/A[X].

48. A computer program product according to claim 47 further comprising seventh computer instruction means for determining a filtered slope using the formula:

FR[X]=F[X]−

FA[0].

49. A computer program product according to claim 37 further comprising fifth computer instruction means for determining a trend value representing the number of transactions for a selected time period for a plurality of transaction systems, and at least one of identifying transaction systems having upward trends and transaction systems having downward trends.

50. A computer program product according to claim 49, wherein said fourth computer instruction means compares the trend value for each transaction system to a first threshold value, and identifies transaction systems having an associated trend value at least as great as the first threshold.

51. A computer program product according to claim 50, wherein said fourth computer instruction means compares the trend value for each transaction system to a second threshold value, and identifies transaction systems having an associated trend value less that the second threshold.

52. A computer program product according to claim 37, wherein the transactions tracked by said second computer instruction means are at least one of:

a number of website hits associated with the transaction system;

a purchases on the transaction system; and

inquiries concerning products offered by the transaction system.

53. A computer program product according to claim 37, wherein the transactions tracked by said second computer instruction means are instances when the transaction system performs a transaction on a selected system.

54. A computer program product according to claim 37, wherein said transaction system is a computer reservation system.