Assessing probability of winning an in-flight deal for different price points

• US 10,248,974 B2
• Filed: 06/24/2016
• Issued: 04/02/2019
• 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
• Primary Examiner(s): Sittner, Matthew T
• Application Number:
  US15/192,892
• Publication Number:
  US 20170372378A1
• Time in Patent Office: 1,012 Days
• Field of Search: None
• 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 comprising:

receiving, at a prediction engine operating on a server device, information relating to a service deal that a service provider is bidding on from a database maintained on a storage device, wherein the service deal comprises a hierarchy of services comprising multiple levels of service, the information relating to the service deal comprises a set of price points, metadata, and a set of baseline values for a highest level of service included in the hierarchy of services, each price point is a potential bidding price for the service deal that the service provider may offer during the bidding, the metadata comprises information relating to one or more other service providers bidding on the same service deal, and the set of baseline values comprises, for each service included in the highest level of service, a corresponding amount of the service the service provider will provide; and

for each price point of the set of price points, predicting, via the prediction engine, a probability of the service provider winning the bidding at the price point based on a predictive analytics model trained by the prediction engine and the information relating to the service deal;

wherein the predicting comprises top-down pricing of the service deal, and the top-down pricing comprises determining a cost and price of each service included in the highest level of service, and determining a total cost and total price of the service deal based on each cost and price of each service determined.

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 the metadata further comprises at least one of the following factors in assessing probability of the service provider winning the bidding at a price point of the set of price points:

complexity of the service deal, whether the service deal is global or local, contract length of the service deal, timing of assignment of the service deal to a delivery executive, presence of a third party advisor, or market segment of a client of the service deal.

6. The method of claim 1, wherein the information relating to one or more other service providers bidding on the same service deal comprises data indicative of one or more of the following:

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

7. The method of claim 1, wherein the information relating to one or more other service providers bidding on the same service deal comprises data indicative of one or more of the following:

whether the one or more other service providers 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 comprising:

receiving, at a prediction engine operating on a server device, information relating to a service deal that a service provider is bidding on from a database maintained on a storage device, wherein the service deal comprises a hierarchy of services comprising multiple levels of service, the information relating to the service deal comprises a set of price points, metadata, and a set of baseline values for a highest level of service included in the hierarchy of services, each price point is a potential bidding price for the service deal that the service provider may offer during the bidding, the metadata comprises information relating to one or more other service providers bidding on the same service deal, and the set of baseline values comprises, for each service included in the highest level of service, a corresponding amount of the service the service provider will provide; and

for each price point of the set of price points, predicting, via the prediction engine, a probability of the service provider winning the bidding at the price point based on a predictive analytics model trained by the prediction engine and the information relating to the service deal;

wherein the predicting comprises top-down pricing of the service deal, and the top-down pricing comprises determining a cost and price of each service included in the highest level of service, and determining a total cost and total price of the service deal based on each cost and price of each service determined.

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 the metadata further comprises at least one of the following factors in assessing probability of the service provider winning the bidding at a price point of the set of price points:

complexity of the service deal, whether the service deal is global or local, contract length of the service deal, timing of assignment of the service deal to a delivery executive, presence of a third party advisor, or market segment of a client of the service deal.

13. The system of claim 8, wherein the information relating to one or more other service providers bidding on the same service deal comprises data indicative of one or more of the following:

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

14. The system of claim 8, wherein the information relating to one or more other service providers bidding on the same service deal comprises data indicative of one or more of the following:

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

15. A non-transitory 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 comprising:

receiving, at a prediction engine operating on a server device, information relating to a service deal that a service provider is bidding on from a database maintained on a storage device, wherein the service deal comprises a hierarchy of services comprising multiple levels of service, the information relating to the service deal comprises a set of price points, metadata, and a set of baseline values for a highest level of service included in the hierarchy of services, each price point is a potential bidding price for the service deal that the service provider may offer during the bidding, the metadata comprises information relating to one or more other service providers bidding on the same service deal, and the set of baseline values comprises, for each service included in the highest level of service, a corresponding amount of the service the service provider will provide; and

for each price point of the set of price points, predicting, via the prediction engine, a probability of the service provider winning the bidding at the price point based on a predictive analytics model trained by the prediction engine and the information relating to the service deal;

wherein the predicting comprises top-down pricing of the service deal, and the top-down pricing comprises determining a cost and price of each service included in the highest level of service, and determining a total cost and total price of the service deal based on each cost and price of each service determined.

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 the metadata further comprises at least one of the following factors in assessing probability of the service provider winning the bidding at a price point of the set of price points:

complexity of the service deal, whether the service deal is global or local, contract length of the service deal, timing of assignment of the service deal to a delivery executive, presence of a third party advisor, or market segment of a client of the service deal.

19. The computer program product of claim 15, wherein the information relating to one or more other service providers bidding on the same service deal comprises data indicative of one or more of the following:

whether the one or more other service providers 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 15, wherein the information relating to one or more other service providers bidding on the same service deal comprises data indicative of one or more of the following:

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