Automated event correlation to improve promotional testing

1. A computer implemented promotion-optimization method that is performed by a promotion optimization system, containing a correlation engine, a promotion module, an administration module, a monitoring module, the method comprising:

extracting a plurality of covariants from data feeds, wherein the covariants include referring source, demographic information, search keywords, geographic information, and temporal information;

generating a plurality of test promotions;

administering the plurality of test promotions to a plurality of segmented subpopulations of consumers in a testing time period, wherein the testing time period includes concurrent testing of the plurality of test promotions:

obtaining responses from said segmented subpopulations of consumers via a point of sales terminal, a website and a program;

correlating the extracted covariants to inputs for the test promotions, wherein the correlating is a degree of attribution of experiment performance to the presence of a covariant, and wherein the correlating identifies a range of values for a particular covariant that correlates with experiment performance, and wherein the correlation derives covariant-experiment pairings;

monitoring the covariant-experiment pairings over time for changes in test promotion results from extraneous events, wherein the extraneous events that impact the test promotions is defined as noise;

attributing the noise to covariants based upon covariant-experiment pairing correlations; improving a signal-to-noise ratio for the responses by factoring out the impact of covariants associated with the extraneous events to generate corrected promotional results; and

generating a general population promotion using the improved signal-to-noise ratio of the test promotion responses responsive to analysis of said corrected promotional results.

2. The method of claim 1 , wherein the referring source includes a search engine, a social network, an email, a personal loyalty account and a mobile browser.

3. The method of claim 1 , further comprising filtering out covariants that do not have an effect on the test promotions.

4. The method of claim 1 , wherein the extracting the plurality of covariants includes identifying the covariants using techniques including k-means clustering, machine learned classification, pre-defined covariants based on domain specific clusters, pre-defined temporal blocks, and hierarchical clustering.

5. The method of claim 4 , wherein the technique used for identification of covariants is determined by heuristics based on the data feed type and number of data feeds.

6. The method of claim 1 , wherein the correlating is performed by one of general linear models, chi-square analysis and Pearson's coefficient.

7. The method of claim 1 , wherein the correlating identifies a range of values for a particular covariant that correlate with experiment performance.

8. The method of claim 1 , further comprising monitoring covariant-experiment correlation pairs for changes in the degree of attribution of experiment performance.

9. A system for performing promotion optimization comprising:

a correlation engine on a server for extracting a plurality of covariants from data feeds, wherein the covariants include referring source, demographic information, search keywords, geographic information, and temporal information;

a promotion module for generating a plurality of test promotions;

an administration module for administering the plurality of test promotions to a plurality of segmented subpopulations of consumers in a testing time period, wherein the testing time period includes concurrent testing of the plurality of test promotions, and

obtaining responses from said segmented subpopulations of consumers via a point of sales terminal, a website and a program;

a correlation engine for correlating the extracted covariants to inputs for the test promotions, wherein the correlating is a degree of attribution of experiment performance to the presence of a covariant, and wherein the correlating identifies a range of values for a particular covariant that correlates with experiment performance, and wherein the correlation derives covariant-experiment pairings;

a monitoring module for monitoring the covariant-experiment pairings over time for changes in test promotion results from extraneous events, wherein the extraneous events that impact the test promotions is defined as noise;

the correlation engine further for attributing the noise to covariants based upon covariant-experiment pairing correlations, and improving a signal-to-noise ratio for the responses by factoring out the impact of covariants associated with the extraneous events to generate corrected promotional results; and

a general population module for generating a general population promotion using the improved signal-to-noise ratio of the test promotion responses responsive to analysis of said corrected promotional results.

10. The system of claim 9 , wherein the referring source includes a search engine, a social network, an email, a personal loyalty account and a mobile browser.

11. The system of claim 9 , further comprising a covariant ID kernel for filtering out covariants that do not have an effect on the test promotions.

12. The system of claim 9 , wherein the extracting the plurality of covariants includes identifying the covariants using techniques including k-means clustering, machine learned classification, pre-defined covariants based on domain specific clusters, pre-defined temporal blocks, and hierarchical clustering.

13. The system of claim 12 , wherein the technique used for identification of covariants is determined by heuristics based on the data feed type and number of data feeds.

14. The system of claim 9 , wherein the correlating is performed by one of general linear models, chi-square analysis and Pearson's coefficient.

15. The system of claim 9 , wherein the correlating identifies a range of values for a particular covariant that correlate with experiment performance.

16. The system of claim 9 , further comprising a covariant monitor for monitoring covariant-experiment correlation pairs for changes in the degree of attribution of experiment performance.