Automated behavioral economics patterns in promotion testing and methods therefor

1. A computer-implemented method for performing promotion optimization, comprising:

generating a plurality of test promotions;

tagging each of the test promotions with a behavioral economics driver using machine learning by comparing the test promotion format, text, channel and graphics to a knowledge database of a training data set comprised of pre-tagged promotions, wherein the behavioral economics driver is selected from a set including at least one of anchoring, scarcity, affinity affect, hyperbolic discounting, warm glow effect, loss aversion, numerosity effect, naive diversification, priming, framing, extreme aversion, contrast effect, impact bias, mere exposure effect, fluency heuristic, interloper effect, bandwagon effect, vonrestorff effect, authority bias, domination effect, take the best heuristic, social proof heuristic, simulation heuristic, recognition heuristic, ingroup bias and halo effect;

administering the plurality of test promotions to a plurality of segmented subpopulations of consumers in real-time, wherein the real-time administration of test promotions includes concurrent testing of the plurality of test promotions;

tagging each segmented subpopulations of consumers with a behavioral economics driver affinity, wherein each behavioral economics driver affinity corresponds to one of the behavioral economics driver of the set;

obtaining responses from said segmented subpopulations of consumers;

quantifying performance effects of each behavioral economics driver by comparing success of the plurality of test promotions including baseline test promotions;

generating a targeted population promotion responsive to analysis of said responses wherein the targeted population promotion includes targeted behavioral economics drivers selected for a target population;

selecting the target population from the plurality of segmented subpopulations of consumers by matching behavioral economics driver affinity of the target population to the targeted behavioral economics drivers found in the targeted population promotion to improve the response; and

administering the targeted population promotion to the target population.

2. The method of claim 1 , wherein the tagging include assigning an intensity value.

3. The method of claim 1 , wherein each behavioral economic driver includes an intensity level.

4. The method of claim 3 , wherein the quantifying includes determining a contribution to promotion results of each behavioral economic driver at differing intensity levels.

5. The method of claim 1 , further comprising grouping consumers into the plurality of segmented subpopulations of consumers using a segmentation criteria of economic behaviors determined using profiles.

6. The method of claim 5 , wherein the profiles are derived from a social network identity.

7. The method of claim 5 , wherein the profiles are derived from a loyalty program identity.

8. The method of claim 5 , wherein the profiles are derived from browsing habits on a mobile browser.

9. The method of claim 1 , wherein the plurality of test promotions includes different variable values.

10. The method of claim 9 , wherein the behavioral economic drivers are variable values.

11. The method of claim 10 , wherein the graphics, colors, text, and promotion type are variable values.

12. The method of claim 9 , wherein the plurality of test promotions have variable value combinations that are historically successful.

13. The method of claim 12 , wherein the plurality of test promotions have variable value combinations that are variations of each other.