ASSESSING PROBABILITY OF WINNING AN IN-FLIGHT DEAL FOR DIFFERENT PRICE POINTS

• US 20170372378A1
• Filed: 06/24/2016
• Published: 12/28/2017
• Est. Priority Date: 06/24/2016
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Firth, Michael K., Megahed, Aly, Ren, Guangjie
• Granted Patent: US 10,248,974 B2
• Time in Patent Office: Days
• 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 30/0275 Auctions

Abstract

One embodiment provides a method for assessing probability of winning an in-flight deal. The method comprises receiving information for the in-flight deal. The information for the in-flight deal comprises a set of price points for the in-flight deal and metadata relating to the in-flight deal. The method further comprises, for each price point of the set of price points, predicting a probability of winning the in-flight deal at the price point based on a predictive analytics model.

Claims

1. A method for assessing probability of winning an in-flight deal, comprising:

receiving information for the in-flight deal, wherein the information for the in-flight deal comprises a set of price points for the in-flight deal and metadata relating to the in-flight deal; and

for each price point of the set of price points, predicting a probability of winning the in-flight deal at the price point based on a predictive analytics model.

2. The method of claim 1, further comprising:

in a training stage;

receiving information for a set of deals, wherein the information for the set of deals comprises, for each deal of the set of deals, metadata relating to the deal and a deal outcome of the deal; and

training the predictive analytics model based on the information for the set of deals.

3. The method of claim 2, wherein the set of deals comprises one or more of the following:

a historical deal, and a market deal.

4. The method of claim 1, wherein the set of price points comprises one or more of the following:

a price point based on historical pricing, a price point based on market pricing, a price point based on user-specified pricing, and a price point based on any other user input.

5. The method of claim 1, wherein a probability of winning the in-flight deal is based on at least one of the following factors:

price point, complexity of the in-flight deal, whether the in-flight deal is global or local, contract length of the in-flight deal, timing of assignment of the in-flight deal to a delivery executive, presence of a third party advisor, market segment of a client of the in-flight deal, and information relating to one or more competitors bidding on the in-flight deal.

6. The method of claim 5, wherein the information relating to one or more competitors bidding on the in-flight deal comprises data indicative of one or more of the following:

whether the one or more competitors are multi-national service providers, local service providers, low-cost service providers, or another category of service providers.

7. The method of claim 5, wherein the information relating to one or more competitors bidding on the in-flight deal comprises data indicative of one or more of the following:

whether the one or more competitors are niche, consultant, cloud service providers, software service providers, network service providers, or another classification of service providers.

8. A system comprising a computer processor, a computer-readable hardware storage device, and program code embodied with the computer-readable hardware storage device for execution by the computer processor to implement a method for assessing probability of winning an in-flight deal, the method comprising:

receiving information for the in-flight deal, wherein the information for the in-flight deal comprises a set of price points for the in-flight deal and metadata relating to the in-flight deal; and

for each price point of the set of price points, predicting a probability of winning the in-flight deal at the price point based on a predictive analytics model.

9. The system of claim 8, further comprising:

in a training stage;

receiving information for a set of deals, wherein the information for the set of deals comprises, for each deal of the set of deals, metadata relating to the deal and a deal outcome of the deal; and

training the predictive analytics model based on the information for the set of deals.

10. The system of claim 9, wherein the set of deals comprises one or more of the following:

a historical deal, and a market deal.

11. The system of claim 8, wherein the set of price points comprises one or more of the following:

a price point based on historical pricing, a price point based on market pricing, a price point based on user-specified pricing, and a price point based on any other user input.

12. The system of claim 8, wherein a probability of winning the in-flight deal is based on at least one of the following factors:

price point, complexity of the in-flight deal, whether the in-flight deal is global or local, contract length of the in-flight deal, timing of assignment of the in-flight deal to a delivery executive, presence of a third party advisor, market segment of a client of the in-flight deal, and information relating to one or more competitors bidding on the in-flight deal.

13. The system of claim 12, wherein the information relating to one or more competitors bidding on the in-flight deal comprises data indicative of one or more of the following:

whether the one or more competitors are multi-national service providers, local service providers, low-cost service providers, or another category of service providers.

14. The system of claim 12, wherein the information relating to one or more competitors bidding on the in-flight deal comprises data indicative of one or more of the following:

whether the one or more competitors are niche, consultant, cloud service providers, software service providers, network service providers, or another classification of service providers.

15. A computer program product comprising a computer-readable hardware storage device having program code embodied therewith, the program code being executable by a computer to implement a method for assessing probability of winning an in-flight deal, the method comprising:

receiving information for the in-flight deal, wherein the information for the in-flight deal comprises a set of price points for the in-flight deal and metadata relating to the in-flight deal; and

for each price point of the set of price points, predicting a probability of winning the in-flight deal at the price point based on a predictive analytics model.

16. The computer program product of claim 15, further comprising:

in a training stage;

receiving information for a set of deals, wherein the information for the set of deals comprises, for each deal of the set of deals, metadata relating to the deal and a deal outcome of the deal; and

training the predictive analytics model based on the information for the set of deals;

wherein the set of deals comprises one or more of the following;

a historical deal, and a market deal.

17. The computer program product of claim 15, wherein the set of price points comprises one or more of the following:

a price point based on historical pricing, a price point based on market pricing, a price point based on user-specified pricing, and a price point based on any other user input.

18. The computer program product of claim 15, wherein a probability of winning the in-flight deal is based on at least one of the following factors:

price point, complexity of the in-flight deal, whether the in-flight deal is global or local, contract length of the in-flight deal, timing of assignment of the in-flight deal to a delivery executive, presence of a third party advisor, market segment of a client of the in-flight deal, and information relating to one or more competitors bidding on the in-flight deal.

19. The computer program product of claim 18, wherein the information relating to one or more competitors bidding on the in-flight deal comprises data indicative of one or more of the following:

whether the one or more competitors are multi-national service providers, local service providers, low-cost service providers, or another category of service providers.

20. The computer program product of claim 18, wherein the information relating to one or more competitors bidding on the in-flight deal comprises data indicative of one or more of the following:

whether the one or more competitors are niche, consultant, cloud service providers, software service providers, network service providers, or another classification of service providers.