Computer-implemented system and method for web activity assessment

• US 20030187719A1
• Filed: 08/30/2002
• Published: 10/02/2003
• Est. Priority Date: 03/29/2002
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Brocklebank, John C.
• Granted Patent: US 7,634,423 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• 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 the steps of:

receiving data indicative of customer activity occurring after the advertising campaign event, said customer activity data including actual data values associated with preselected business metrics;

for at least one of the business metrics, determining a time lag between the advertising campaign event and the customer activity that generated the business metric;

based upon the determined time lag, calculating different time lag amounts that seek to achieve different desired levels for the business metric; and

generating a goal seeking report that displays the calculated different lag amounts with the desired levels for the business metric.

2. The method of claim 1, wherein the generating a directional indicator based upon the determined business metric value and metric directional data, said metric directional data determining whether a business metric value is changing in a desired positive or negative direction.

3. The method of claim 1, wherein the metadata associated with each business metric type is calculated as a function of time and the standardized difference between the business metric value and the forecast.

4. The method of claim 1, wherein the forecast is generated using a large-scale automatic forecasting module.

5. The method of claim 1, comprising the further step of:

calculating an individual score for each business metric type as a function of the business metric value, the forecast, and the metadata.

6. The method of claim 5, comprising the further step of:

calculating an overall score as a function of the individual scores.

7. The method of claim 6, comprising the further step of:

generating a business metric report that indicates each business metric value and individual score.

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

9. The method of claim 8, comprising the additional step of:

calculating an overall score as a function of the individual scores, wherein the business metric report also indicates the individual score.

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, comprising the additional step of:

for each business metric type, calculating a standardized difference as a function of the deviation between the business metric value and the associated forecast.

13. The method of claim 8, comprising the additional step of:

for each business metric type, calculating a standardized difference as a function of the deviation between the business metric value and the associated forecast, wherein the business metric report also indicates the standardized difference for each business metric type.

14. The method of claim 1, comprising the additional steps of:

calculating an upper and a lower confidence limit associated with the forecast for each business metric type; and

for each business metric type, determining whether the business metric value falls between the upper and lower confidence limits.

15. The method of claim 8, comprising the additional steps of:

calculating an upper and a lower confidence limit associated with the forecast for each business metric type; and

for each business metric type, determining whether the business metric value falls between the upper and lower confidence limits,

wherein the business metric report also indicates whether each business metric value falls within the associated upper and lower confidence limits.

16. The method of claim 1, comprising the additional step of:

calculating an amount by which at least one of the business metric values must change in order to achieve a desired value in another of the business metric values; and

generating a goal seeking report that indicates the amount by which the business metric values may change.

17. The method of claim 16, wherein the amount by which at least one of the business metric values may change is calculated using a nonlinear econometric model.

18. A computer-implemented method for evaluating customer activity, comprising the steps of:

receiving data indicative of customer activity, said customer activity data including actual data values associated with preselected business metrics;

for each business metric, performing a comparison between the actual values of a business metric and a prediction associated with a business metric value; and

using directional metric metadata and the comparison to generate an indicator as to performance direction for a business metric, said directional metric metadata indicating for a business metric a desired performance direction.

19. The method of claim 18, further comprising the step of:

modifying a web site based upon the generated performance indicator.

20. The method of claim 19, further comprising the steps of:

determining a funnel decay characteristic of the web site; and

modifying the web site based upon the determined funnel decay characteristic.

21. The method of claim 20, further comprising the steps of:

determining a funnel width characteristic of the web site; and

modifying the web site based upon the determined funnel width characteristic.