Event revenue management system

• US 20020120492A1
• Filed: 06/08/2001
• Published: 08/29/2002
• Est. Priority Date: 06/09/2000
• Status: Active Grant

• Current Assignee: JDA Software Group Incorporated (RedPrairie Holding Incorporated)
• Original Assignee: JDA Software Group Incorporated (RedPrairie Holding Incorporated)
• Inventors: Cohen, Jeremy, Holmquist, Kristen, Phillips, Robert L., Mace, Andrew, Eldredge, Michael, Ye, Stanley, Pyron, Nancy, Cao, George, Levett, Dave, Buckalew, Bill
• Granted Patent: US 7,110,960 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

The present invention provides an event revenue management system that calculates event pricing to manage revenue and to maximize profit from the event. In particular, the system uses event parameters, including timing, resource, and discount categories, and initializes forecasting parameters. The system aggregates historical data using the forecasting parameters to generate initial forecast statistics, taking into account the event parameters. It then forecasts demand by updating the initial forecast statistics based on current data and optimizes pricing and availability of the event based on the forecasted demand.

Claims

1. A system for managing revenue for an event, the event having event parameters including timing, resource, and discount categories, the system comprising:

means for initializing forecasting parameters;

means for aggregating historical data using the forecasting parameters to generate initial forecast statistics, taking into account the event parameters;

means for forecasting demand by updating the initial forecast statistics based on current data; and

means for optimizing pricing of the event.

2. The system of claim 1, wherein the aggregating means is capable of selecting a virtual event category that matches the event, and wherein the virtual event category is used to calculate the initial forecast statistics.

3. The system of claim 2, further comprising:

means for calculating seasonal event demand adjustment values,

wherein the aggregating means produces seasonally-adjusted general statistics for the virtual event category for various combinations of the resource and discount categories, and

wherein the demand forecasted using the forecasting means includes demands for days-out bins, a gross event demand, a seasonalized event demand, and an end-point demand.

4. The system of claim 1, wherein the initial forecast statistics include a fractional build curve, forecast, and remaining demand forecast.

5. The system of claim 4, wherein the forecasting means updates the fractional build curve, forecast, and remanding demand forecast.

6. The system of claim 1, wherein the optimizing means adjusts an availability of the discount categories.

7. The system of claim 1, further comprising:

means for calculating seasonal event demand adjustment values, wherein the seasonal event demand adjustment values are used by the aggregating and forecasting means.

8. The system of claim 1, wherein the forecasting means takes into account external factors.

9. The system of claim 8, wherein the external factors include opponent information, win/loss record, promotions, and weather.

10. The system of claim 1, further comprising:

means for allowing a user to adjust calculations performed by the initializing, aggregating, forecasting, and optimizing means.

11. A method for managing revenue for an event, the event having event parameters including timing, resource, and discount categories, the method comprising:

initializing forecasting parameters;

aggregating historical data using the forecasting parameters to generate initial forecast statistics, taking into account the event parameters;

forecasting demand by updating the initial forecast statistics based on current data; and

optimizing pricing of the event.

12. The method of claim 11, wherein the aggregating step is capable of selecting a virtual event category that matches the event, and wherein the virtual event category is used to calculate the initial forecast statistics.

13. The method of claim 12, further comprising:

calculating seasonal event demand adjustment values,

wherein the aggregating step produces seasonally-adjusted general statistics for the virtual event category for various combinations of the resource and discount categories, and

wherein the demand forecasted in the forecasting step includes demands for days-out bins, a gross event demand, a seasonalized event demand, and an end-point demand.

14. The method of claim 11, wherein the initial forecast statistics include a fractional build curve, forecast, and remaining demand forecast.

15. The method of claim 14, wherein the forecasting step updates the fractional build curve, forecast, and remanding demand forecast.

16. The method of claim 11, wherein the optimizing step adjusts an availability of the discount categories.

17. The method of claim 11, further comprising:

calculating seasonal event demand adjustment values,

wherein the seasonal event demand adjustment values are used by the aggregating and forecasting steps.

18. The method of claim 11, wherein the forecasting step takes into account external factors.

19. The method of claim 18, wherein the external factors include opponent information, win/loss record, promotions, and weather.

20. The method of claim 11, further comprising:

allowing a user to adjust calculations performed in the initializing, aggregating, forecasting, and optimizing steps.

21. A method for managing revenue for a plurality of events, comprising:

loading historical data and event information regarding each of the plurality of events, wherein the event information includes venue configuration, event, date and time, resource category, and discount category information;

initializing forecasting parameters for each event in the plurality of events;

generating initial forecast statistics for each event in the plurality of events by aggregating appropriate historical data using corresponding forecasting parameters, taking into account corresponding event information;

updating the initial forecast statistics for each event in the plurality of events based on appropriate current data; and

optimizing pricing for each event in the plurality of events.

22. The method of claim 21, wherein the generating step is capable of selecting a virtual event category for each event in the plurality of events, wherein the virtual event category matches a corresponding event and is used to calculate the initial forecast statistics for the corresponding event.

23. The method of claim 22, further comprising:

calculating seasonal event demand adjustment values for each of the plurality of events,

wherein the generating step produces seasonally-adjusted general statistics for the virtual event category for various combinations of the resource and discount categories for each of the plurality of events, and

wherein the demand forecasted in the forecasting step includes demands for days-out bins, a gross event demand, a seasonalized event demand, and an end-point demand.

24. The method of claim 21, wherein the initial forecast statistics include a fractional build curve, forecast, and remaining demand forecast.

25. The method of claim 24, wherein the forecasting step updates the fractional build curve, forecast, and remanding demand forecast.

26. The method of claim 21, wherein the optimizing step adjusts an availability of the discount categories for each event in the plurality of events.

27. The method of claim 21, further comprising:

calculating seasonal event demand adjustment values for each of the plurality of events,

wherein the seasonal event demand adjustment values are used in the generating and forecasting steps.

28. The method of claim 21, wherein the forecasting step takes into account external factors.

29. The method of claim 28, wherein the external factors include opponent information, win/loss record, promotions, and weather.

30. The method of claim 21, further comprising:

allowing a user to adjust calculations performed in the loading initializing, generating, forecasting, and optimizing steps.

31. An event revenue management network for managing revenue for an event, the event having event parameters including timing, resource, and discount categories, the network comprising:

an initialization module that initializes forecasting parameters;

an aggregation module that aggregates historical data using the forecasting parameters to generate initial forecast statistics, taking into account the event parameters;

a forecast module that forecasts demand by updating the initial forecast statistics based on current data; and

an optimization module that optimizes pricing of the event.

32. A computer program product for managing event revenue, the event having event parameters including timing, resource, and discount categories, the computer program product comprising:

computer readable program code configured to initialize forecasting parameters;

computer readable program code configured to aggregate historical data using the forecasting parameters to generate initial forecast statistics, taking into account the event parameters;

computer readable program code configured to forecast demand by updating the initial forecast statistics based on current data;

computer readable program code configured to optimize pricing of the event; and

a computer readable medium having the computer readable program codes embodied therein.

33. A mechanism configured to manage revenue of an event, the event having event parameters including timing, resource and discount categories, the mechanism comprising:

a mechanism configured to initialize forecasting parameters;

a mechanism configured to aggregate historical data using the forecasting parameters to generate initial forecast statistics, taking into account the event parameters;

a mechanism configured to forecast demand by updating the initial forecast statistics based on current data; and

a mechanism configured to optimize pricing of the event.

34. A program storage device readable by a machine, tangibly embodying a program of instructions executable by a machine to perform method steps of managing event revenue, the event having event parameters including timing, resource and discount categories, the method steps comprising:

initializing forecasting parameters;

aggregating historical data using the forecasting parameters to generate initial forecast statistics, taking into account the event parameters;

forecasting demand by updating the initial forecast statistics based on current data; and

optimizing pricing of the event.