Method and system for load balancing a sales forecast by selecting a synchronous or asynchronous process based on a type of event affecting the sales forecast

• US 8,812,346 B2
• Filed: 10/17/2012
• Issued: 08/19/2014
• Est. Priority Date: 10/04/2006
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Doshi, Kedar, Fischer, Mark, Chou, Evan
• Primary Examiner(s): Jarrett, Scott L
• Application Number:
  US13/654,348
• Publication Number:
  US 20130041718A1
• Time in Patent Office: 671 Days
• Field of Search: 705/7.31
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

In accordance with embodiments, there are provided mechanisms and methods for selecting a synchronous or asynchronous process to determine a forecast. These mechanisms and methods for such synchronous/asynchronous process selection can enable embodiments to determine forecasts for multiple users (e.g. with hierarchical relationships, etc.) over an arbitrary time interval. The ability of embodiments to provide forecasts that involve such a large amount of data in an effective way can enable forecasting that was otherwise infeasible due to resource limitations.

Claims

1. A method, comprising:

identifying an entity associated with at least one deal for which a first sales forecast is determined;

identifying data of the entity utilized to determine the first sales forecast;

identifying a type of at least one event associated with a modification to the identified data and affecting the first sales forecast;

managing load on a forecast system utilized to determine the first sales forecast, by automatically selecting between a synchronous process and an asynchronous process for determining a second sales forecast, by a processor, based on the identified type of the at least one event, including;

automatically selecting the synchronous process in response to a determination that the type of the at least one event is a data event that affects a first portion of the data of the entity which is a subject of the determination of the first sales forecast, the data event including a modification to the first portion of the data of the entity, such that the first sales forecast is adjusted based on current data resulting from the modification to the first portion of the data of the entity, and automatically selecting the asynchronous process in response to a determination that the type of the at least one event is a set-up function event that changes data associated with a reporting structure for reporting the second sales forecast;

determining the second sales forecast from the identified data of the entity, utilizing the selected one of the synchronous process and the asynchronous process;

wherein the synchronous process is performed in less time than the asynchronous process, and wherein the synchronous process is performed in real-time or near-real time and the asynchronous process includes batch processing that is performed in a greater amount of time with respect to the synchronous process, such that the synchronous process is selected to determine the second sales forecast faster than would the asynchronous process.

2. The method of claim 1, and further comprising identifying a hierarchy associated with the entity.

3. The method of claim 2, wherein the second sales forecast is determined, based on the hierarchy.

4. The method of claim 2, wherein the hierarchy is user-defined.

5. The method of claim 1, and further comprising identifying at least one user associated with the entity.

6. The method of claim 5, wherein the second sales forecast is determined, based on the at least one user.

7. The method of claim 6, wherein a plurality of users are included in a list associated with the entity.

8. The method of claim 7, wherein the list is user-defined.

9. The method of claim 1, and further comprising identifying an arbitrary time period.

10. The method of claim 9, wherein the second sales forecast is determined, based on the arbitrary time period.

11. The method of claim 1, and further comprising identifying a level of commitment.

12. The method of claim 11, wherein the second sales forecast is determined, based on the level of commitment.

13. The method of claim 11, wherein the level of commitment includes at least one of a committed level, a best case level, an omitted level, a closed level, and a pipeline level.

14. The method of claim 1, and further comprising identifying the at least one event.

15. The method of claim 14, wherein the second sales forecast is determined, based on the at least one event.

16. The method of claim 14, wherein the second sales forecast is determined by only recalculating the first sales forecast and one or more other previous sales forecasts that are affected by the at least one event.

17. The method of claim 14, and further comprising identifying at least one additional event.

18. The method of claim 17, and further comprising determining whether the at least one event is subsumed by the at least one additional event.

19. The method of claim 18, wherein the second sales forecast is determined based on only the at least one additional event, if it is determined that the at least one event is subsumed by the at least one additional event, and the second sales forecast is determined based on the at least one event and the at least one additional event if it is determined that the at least one event is not subsumed by the at least one additional event.

20. A non-transitory machine-readable medium carrying one or more sequences of computer instructions causing a computer to implement a method, the method comprising:

identifying an entity associated with at least one deal for which a first sales forecast is determined;

identifying data of the entity utilized to determine the first sales forecast;

identifying a type of at least one event associated with a modification to the identified data and affecting the first sales forecast;

managing load on a forecast system utilized to determine the first sales forecast, by automatically selecting between a synchronous process and an asynchronous process for determining a second sales forecast based on the identified type of the at least one event, including;

automatically selecting the synchronous process in response to a determination that the type of the at least one event is a data event that affects a first portion of the data of the entity which is a subject of the determination of the first sales forecast, the data event including a modification to the first portion of the data of the entity, such that the first sales forecast is adjusted based on current data resulting from the modification to the first portion of the data of the entity, and automatically selecting the asynchronous process in response to a determination that the type of the at least one event is a set-up function event that changes data associated with a reporting structure for reporting the second sales forecast;

determining the second sales forecast from the identified data of the entity, utilizing the selected one of the synchronous process and the asynchronous process;

wherein the synchronous process is performed in less time than the asynchronous process, and wherein the synchronous process is performed in real-time or near-real time and the asynchronous process includes batch processing that is performed in a greater amount of time with respect to the synchronous process, such that the synchronous process is selected to determine the second sales forecast faster than would the asynchronous process.

21. An apparatus, comprising:

a processor; and

one or more stored sequences of instructions which, when executed by the processor, cause the processor to carry out the steps of;

identifying an entity associated with at least one deal for which a first sales forecast is determined;

identifying data of the entity utilized to determine the first sales forecast;

identifying a type of at least one event associated with a modification to the identified data and affecting the first sales forecast;

managing load on a forecast system utilized to determine the first sales forecast, by automatically selecting between a synchronous process and an asynchronous process for determining a second sales forecast, by a processor, based on the identified type of the at least one event, including;

automatically selecting the synchronous process in response to a determination that the type of the at least one event is a data event that affects a first portion of the data of the entity which is a subject of the determination of the first sales forecast, the data event including a modification to the first portion of the data of the entity, such that the first sales forecast is adjusted based on current data resulting from the modification to the first portion of the data of the entity, and automatically selecting the asynchronous process in response to a determination that the type of the at least one event is a set-up function event that changes data associated with a reporting structure for reporting the second sales forecast;

determining the second sales forecast from the identified data of the entity, utilizing the selected one of the synchronous process and the asynchronous process;

wherein the synchronous process is performed in less time than the asynchronous process, and wherein the synchronous process is performed in real-time or near-real time and the asynchronous process includes batch processing that is performed in a greater amount of time with respect to the synchronous process, such that the synchronous process is selected to determine the second sales forecast faster than would the asynchronous process.