Computer-implemented system and method for web activity assessment

• US 7,634,423 B2
• Filed: 08/30/2002
• Issued: 12/15/2009
• Est. Priority Date: 03/29/2002
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Brocklebank, John C.
• Primary Examiner(s): Boswell; Beth V
• Assistant Examiner(s): PATS, JUSTIN
• Application Number:
  US10/232,153
• Publication Number:
  US 20030187719A1
• Time in Patent Office: 2,664 Days
• Field of Search: 705/10
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0246 Traffic

Abstract

A computer-implemented system and method for evaluating customer activity. Data about the customer activity is received and is used to generate actual data values associated with preselected business metrics. One or more business metric score cards may be generated to assess how the business metrics are performing as well as what business metrics can be changed to better meet business goals.

Claims

1. A computer-implemented method for evaluating an advertising campaign event, comprising:

executing software instructions on a computer processor to receive a selection of a business metric;

executing software instructions on the computer processor to receive data indicative of customer activity occurring after the advertising campaign event, wherein the customer activity data is actual point of sale or Internet-based customer activity data associated with the selected business metric, and wherein the advertising campaign event involves promotion of a product or service over a communication medium;

executing software instructions on the computer processor to receive promotion exposure data for the advertising campaign event;

executing software instructions on the computer processor to fit a statistical model to the customer activity data and to the promotion exposure data for the advertising campaign event;

executing software instructions on the computer processor to use the fitted statistical model to forecast values of the selected business metric, wherein forecasting is computed from the fitted statistical model, standard errors for the forecast estimates, and corresponding prediction intervals including predictions at different time lags and at a plurality of different desired levels, wherein a time lag is a lag in time between the advertising campaign event and the customer activity;

executing software instructions on the computer processor to generate a goal seeking report that displays the forecasted selected business metric values at the different time lags and at the plurality of different desired levels, the forecasted selected business metric values representing different desired goals for the selected business metric;

executing software instructions on the computer processor to generate a directional indicator based upon the forecasted selected business metric value and metric directional data, wherein the metric directional data is a determination of whether a business metric value is changing in a desired positive or negative direction; and

executing software instructions on the computer processor to calculate an individual score for the selected business metric;

wherein when it is preferred that actual selected business metric values are greater than the forecasted selected business metric values, the individual score is calculated using the following;

2. The method of claim 1, further comprising:

executing software instructions on the computer processor to calculate metadata associated with each business metric type as a function of time and the standardized difference between actual selected business metric values and the forecasted selected business metric values.

3. The method of claim 2, further comprising:

executing software instructions on the computer processor to calculate an upper and a lower confidence limit associated with the forecast for the selected business metric; and

executing software instructions on the computer processor to determine whether the forecasted selected business metric values fall between the upper and lower confidence limits.

4. The method of claim 1, further comprising:

executing software instructions on the computer processor to generate a forecast using a large-scale automatic forecasting module.

5. The method of claim 1, wherein calculating an individual score for the selected business metric is done as a function of actual selected business metric values, the forecasted selected business metric values, and the metadata.

6. The method of claim 1, wherein the generated business metric report indicates actual selected business metric values and the individual score.

7. The method of claim 6, wherein the business metric report indicates whether the selected business metric value is changing in the desired positive or negative direction.

8. The method of claim 7, further comprising:

executing software instructions on the computer processor to calculate a standardized difference as a function of the deviation between actual selected business metric values and the forecasted selected business metric values, wherein the business metric report also indicates the standardized difference for the selected business metric.

9. The method of claim 7, further comprising:

executing software instructions on the computer processor to calculate an upper and a lower confidence limit associated with the forecast for the selected business metric; and

executing software instructions on the computer processor to determine whether the forecasted selected business metric values fall between the upper and lower confidence limits, wherein the business metric report also indicates whether actual selected business metric value falls within the associated upper and lower confidence limits.

10. The method of claim 1, wherein the customer activity is logged from a plurality of data sources, wherein at least one of the data sources is a web site.

11. The method of claim 10, wherein at least one of the data sources is a point-of-sale device.

12. The method of claim 1, further comprising:

executing software instructions on the computer processor to calculate a standardized difference as a function of the deviation between actual selected business metric values and the forecasted selected business metric values.

13. The method of claim 1, further comprising:

executing software instructions on the computer processor to calculate an amount by which actual selected business metric values must change in order to achieve a preferred value; and

executing software instructions on the computer processor to generate a goal seeking report that indicates the amount by which actual selected business metric values may change.

14. The method of claim 13, wherein the amount by which actual selected business metric values may change is calculated using a nonlinear econometric model.

15. The method of claim 1, wherein a point of sale customer activity is a customer visit to a store, a customer viewing a product at a store, or a customer purchase at a store.

16. The method of claim 1, wherein an Internet-based customer activity is a customer visit to a website, a customer viewing a product page on a website, or a customer purchase at a website.

17. The method of claim 1, wherein a communication medium is a medium selected from the group consisting of radio, television, newsprint, magazines, Internet, films, books, video games, CDs, and DVDs.

18. The method of claim 1, wherein the goal seeking report is generated by individually solving for values of the selected business metric at different target values, and

wherein the goal seeking report facilitates access to one or more graphical evaluations of the selected business metric.

19. The method of claim 1, wherein the different desired goals for the selected business metric represent ways to increase the level of the target variable by determining an exposure rating.

20. A computer-implemented system for evaluating an advertising campaign event, comprising:

a data processor; and

a memory storing the following;

software instructions executable on the data processor to receive a selection of a business metric;

software instructions executable on the data processor to receive data indicative of customer activity occurring after the advertising campaign event, wherein the customer activity data is actual point of sale or Internet-based activity data associated with the selected business metric, and wherein the advertising campaign event involves promotion of a product or service over a communication medium;

software instructions executable on the data processor to receive promotion exposure data for the advertising campaign event;

software instructions executable on the data processor to fit a statistical model to the customer activity data and to the promotion exposure data for the advertising campaign event;

software instructions executable on the data processor to use the fitted statistical model to forecast values of the selected business metric, wherein the forecasting is computed from the fitted statistical model, standard errors for the forecast estimates, and corresponding prediction intervals including predictions at different time lags and at a plurality of different desired levels, wherein a time lag is a lag in time between the advertising campaign event and the customer activity;

software instructions executable on the data processor to generate a goal seeking report that displays forecasted selected business metric values at the different time lags and at the plurality of different desired levels, the forecasted selected business metric values representing different desired goals for the selected business metric;

software instructions executable on the data processor to generate a directional indicator based upon the forecasted selected business metric value and metric directional data, wherein the metric directional data is a determination of whether a business metric value is changing in a desired positive or negative direction; and

software instructions executable on the data processor to calculate an individual score for the selected business metric;

wherein when it is preferred that actual selected business metric values are greater than the forecasted selected business metric values, the individual score is calculated using the following;