Computer-implemented system and method for web activity assessment

• US 8,000,994 B2
• Filed: 11/05/2009
• Issued: 08/16/2011
• Est. Priority Date: 03/29/2002
• Status: Expired due to Term

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Brocklebank, John C.
• Primary Examiner(s): Pats; Justin
• Application Number:
  US12/613,216
• Publication Number:
  US 20100257026A1
• Time in Patent Office: 649 Days
• Field of Search: 705/7.29, 705/7.31, 705/7.39
• US Class Current: 705/7.31
• 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:

receiving a selection of a business metric, wherein the business metric is a single business metric among a plurality of business metrics;

receiving 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 over a communication medium;

receiving promotion exposure data for the advertising campaign event;

fitting a statistical model to the customer activity data and to the promotion exposure data for the advertising campaign event;

forecasting, using an automated forecasting module in a computer-implemented system, one or more expected values for the business metric, wherein the forecasting is computed from the fitted statistical model, standard errors for forecast estimates, and corresponding prediction intervals including predictions at different time lags and at a plurality of different desired levels, and wherein a time lag is a lag in time between the advertising campaign event and the customer activity;

generating a directional indicator based upon the one or more forecasted expected values for the business metric and metric directional data, wherein the metric directional data is a determination of whether a business metric value is changing in a positive or negative direction;

determining standardized differences between the one or more expected values for the business metric and one or more observed values for the business metric;

associating a statistical significance with the business metric;

associating a statistical significance with one or more other business metrics in the plurality of business metrics, wherein at least one business metric is statistically significant and at least one business metric is statistically insignificant;

generating a weighted value for each business metric having an associated statistical significance, wherein a weighted value is generated by mathematically relating a weight to a business metric based upon the statistical significance associated with that business metric;

calculating an overall score by aggregating the weighted values; and

using the standardized differences and the overall score to generate one or more visual indicators for the business metric, wherein the one or more visual indicators and the directional indicator are used to generate a score card for the business metric.

2. The method of claim 1, wherein each business metric is a type of business metric, and wherein each business metric type has associated metadata calculated as a function of time and the standardized difference between the one or more expected business metric values and the one or more observed business metric values.

3. The method of claim 1, further comprising:

generating a business metric report including the one or more observed business metric values and the overall score for the business metric.

4. The method of claim 3, wherein the business metric report indicates whether the business metric value is changing in a positive or negative direction.

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

6. The method of claim 5, wherein at least one of the data sources is a point-of-sale device.

7. The method of claim 3, wherein the business metric report indicates the standardized differences for the selected business metric.

8. The method of claim 1, further comprising:

calculating an upper and a lower confidence limit corresponding to the one or more expected values for the business metric; and

determining whether the one or more expected values for the business metric fall between the upper and lower confidence limits.

9. The method of claim 8, wherein the business metric report indicates whether the one or more observed values for the business metric fall within the upper and lower confidence limits.

10. The method of claim 1, further comprising:

calculating an amount by which the one or more observed values for the business metric must change in order to achieve a preferred value; and

generating a goal seeking report that indicates the amount by which the one or more values for the business metric may change when the preferred value is achieved.

11. The method of claim 10, wherein the amount by which the one or more observed values for the business metric must change is calculated using a nonlinear econometric model.

12. 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.

13. 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.

14. The method of claim 1, wherein the communication medium is a medium selected from the group comprising radio, television, newsprint, magazines, Internet, and books.

15. The method of claim 1, further comprising:

generating a goal seeking report by individually solving for values of the selected business metric at different target values, wherein the goal seeking report facilitates access to one or more graphical evaluations of the selected business metric.

16. The method of claim 15, wherein different desired goals for the selected business metric represent ways to increase the level of a target value by determining an exposure rating.

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

a computer comprising a processor; and

a plurality of modules configured to;

receive a selection of a business metric, wherein the business metric is a single business metric among a plurality of business metrics;

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 over a communication medium;

receive promotion exposure data for the advertising campaign event;

fit a statistical model to the customer activity data and to the promotion exposure data for the advertising campaign event;

forecast one or more expected values for the business metric, wherein the forecasting is computed from the fitted statistical model, standard errors for forecast estimates, and corresponding prediction intervals including predictions at different time lags and at a plurality of different desired levels, and wherein a time lag is a lag in time between the advertising campaign event and the customer activity;

generate a directional indicator based upon the one or more forecasted expected values for the business metric and metric directional data, wherein the metric directional data is a determination of whether a business metric value is changing in a positive or negative direction;

determine standardized differences between the one or more expected values for the business metric and one or more observed values for the business metric;

associate a statistical significance with the business metric;

associate a statistical significance with one or more other business metrics in the plurality of business metrics, wherein at least one business metric is statistically significant and at least one business metric is statistically insignificant;

generate a weighted value for each business metric having an associated statistical significance, wherein a weighted value is generated by mathematically relating a weight to a business metric based upon the statistical significance associated with that business metric;

calculate an overall score by aggregating the weighted values; and

use the standardized differences and the overall score to generate one or more visual indicators for the business metric, wherein the one or more visual indicators and the directional indicator are used to generate a score card for the business metric.