Analytically determining revenue of internet companies using internet metrics

• US 8,423,398 B2
• Filed: 10/19/2007
• Issued: 04/16/2013
• Est. Priority Date: 05/04/2001
• Status: Expired due to Term

• Current Assignee: Accessify, LLC (Ascend Innovation Management, LLC)
• Original Assignee: TTI Inventions C LLC (Intellectual Ventures LLC)
• Inventors: Dao, Fu-Tak, Martija, Ricardo, Spacek, Thomas, Weerahandi, Samaradasa
• Primary Examiner(s): Loftis, Johnna
• Assistant Examiner(s): Miller, Alan S
• Application Number:
  US11/975,532
• Publication Number:
  US 20080059262A1
• Time in Patent Office: 2,006 Days
• Field of Search: 705 711- 742
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company'"'"'s revenue for the current day, week, month, or quarter. These estimates are also used to project the company'"'"'s revenue for future days, weeks, months, and quarters.

Claims

1. A method for estimating current revenue of a company, said method comprising:

a computing device obtaining from network resources data points associated with the company for at least two Internet metrics over at least first and second past time periods;

the computing device creating past Internet metric data points for the at least first and second past time periods by combining the data points from the at least two Internet metrics for respective periods of the at least first and second past time periods;

the computing device generating one or more revenue models for the company by correlating at least the created past Internet metric data points for the first past time period to past revenue data points of the company for the first past time period and the created past Internet metric data points for the second past time period to past revenue data points of the company for the second past time period;

the computing device determining statistical characteristics of the one or more revenue models respectively;

the computing device selecting a chosen revenue model based on the statistical characteristics of the chosen revenue model;

the computing device creating one or more new Internet metric data points for a current time period by combining one or more current data points obtained for the at least two Internet metrics from the current time period; and

the computing device estimating the current revenue of the company by applying the created new Internet metric data points to the chosen revenue model.

2. The method of claim 1 wherein creating one or more new Internet metric data points includes creating the new Internet metric data points through standard sums.

3. The method of claim 2 further comprising using standard deviations as a weighting factor in the standard sums.

4. The method of claim 1 wherein generating one or more revenue models includes generating one or more quarterly revenue models, the method further comprising the computing device scaling the one or more quarterly revenue models to weekly revenue models, and wherein creating one or more new Internet metric data points includes creating one week of data points, and wherein estimating the company'"'"'s current revenue includes estimating revenue for the one week.

5. The method of claim 1 wherein combining the data points from the at least two Internet metrics comprises combining at least two of a page-hits metric, a visitors metric, a transactions metric, or a hosts metric.

6. The method of claim 1 wherein obtaining data points from network resources includes obtaining data points from at least one of domain name servers, web activity metrics at a customer premises level, or web activity metrics at a web site.

7. The method of claim 1 wherein the computing device generating the one or more revenue models for the company comprises the computing device performing a regression analysis on the created past internet metric data points and past revenue data points of the company for the first past time period and the created past Internet metric data points and past revenue data points of the company for the second past time period.

8. A tangible computer readable medium, having instructions stored thereon, that in response to execution by a device, cause the device to perform operations comprising:

obtaining data points associated with a company from network resources for at least two Internet metrics over at least first and second past time periods,

creating past Internet metric data points for the at least first and second past time periods by combining the data points from the at least two Internet metrics for respective periods of the at least first and second past time periods;

generating one or more revenue models for the company by correlating at least the created past Internet metric data points for the first past time period to past revenue data points of the company for the first past time period and the created past Internet metric data points for the second past time period to past revenue data points of the company for the second past time period;

determining statistical characteristics of the one or more revenue models respectively;

selecting a chosen revenue model based on the statistical characteristics of the chosen revenue model;

creating one or more new Internet metric data points for a current time period by combining one or more current data points obtained for the at least two Internet metrics from the current time period; and

estimating the current revenue of the company by applying the created new Internet metric data points to the chosen revenue model.

9. The tangible computer readable medium of claim 8 wherein the new Internet metric data points are created through standard sums.

10. The tangible computer readable medium of claim 9 wherein standard deviations are used as a weighting factor in the standard sums.

11. The tangible computer readable medium of claim 8 wherein the chosen revenue model is a quarterly revenue model, the operations further comprising scaling the chosen revenue model to a weekly revenue model, and wherein the created new Internet metric data points comprise one week of data points, and wherein the estimated company'"'"'s current revenue is revenue for the one week.

12. The tangible computer readable medium of claim 8 wherein the at least two Internet metrics comprise at least two of a page-hits metric, a visitors metric, a transactions metric, or a hosts metric.

13. The tangible computer readable medium of claim 8 wherein the network resources comprise at least one of domain name servers, web activity metrics at a customer premises level, or web activity metrics at a web site.

14. The tangible computer readable medium of claim 8 wherein generating one or more revenue models comprises performing a regression analysis on the created past Internet metric data points and past revenue data points of the company for the first past time period and the created past Internet metric data points and past revenue data points of the company for the second past time period.

15. An apparatus for estimating the current revenue of a company, the apparatus comprising:

a computing device configured to;

receive past data points of at least two internet metrics associated with the company for at least first and second past time periods and current data points of the at least two internet metrics for a current time period from network resources;

create a combination of the past data points for the at least first and second past time periods respectively;

generate one or more revenue models for the company by correlating at least the combination of the past data points for the first past time period to past revenue data points of the company for the first past time period and the combination of the past data points for the second past time period to past revenue data points of the company for the second past time period;

determine statistical characteristics of the one or more revenue models respectively;

select a chosen revenue model based on the statistical characteristics of the chosen revenue model; and

estimate a current revenue for the company by applying a combination of the current data points to the chosen revenue model.

16. The apparatus of claim 15 wherein the computing device is configured to receive past and current data points from network resources that comprise at least one of domain name servers, web activity metrics at a customer premises level, or web activity metrics at a web site.

17. The apparatus of claim 15 wherein the computing device is further configured to apply weighting factors to create the combination of the past data points, the at least two Internet metrics comprising at least two of a page-hits metric, a visitors metric, a transactions metric, or a hosts metric.

18. The apparatus of claim 15 wherein the computing device is further configured to create the combination of the current data points using standard sums.

19. The apparatus of claim 18 wherein the computing device is further configured to use standard deviations as a weighting factor in the standard sums.

20. The apparatus of claim 15 wherein the computing device configured to generate one or more revenue models for the company comprises the computing device configured to perform a regression analysis on the combination of the past data points and the past revenue data points of the company for the first past time period and the second past time period respectively.