SYSTEM AND METHOD FOR RECOMMENDING A GRAMMAR FOR A MESSAGE CAMPAIGN USED BY A MESSAGE OPTIMIZATION SYSTEM

• US 20130311269A1
• Filed: 05/17/2012
• Published: 11/21/2013
• Est. Priority Date: 05/17/2012
• Status: Active Grant

• Current Assignee: Persado Intellectual Property Limited
• Original Assignee: Upstream Mobile Marketing Limited
• Inventors: Baciu, Assaf, Forte, Rui Miguel, Shalit, Avishalom, Krief, Guy Stephane
• Granted Patent: US 10,395,270 B2
• Time in Patent Office: Days
• US Class Current: 705/14.43
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0276 Advertisement creation

Abstract

A system and method is provided for recommending a grammar for a message campaign used by a message optimization system. A user specifies parameters for a new campaign, from which a set of statistical design budgets is calculated. The user selects a grammar structure, recommended based on the statistical design budgets, for the campaign. The n-most relevant past campaigns are identified. Semantic tags, associated with each previously used value from the n-most relevant past campaigns and each of a plurality of untested values, are identified and ranked based on past performance. The previously used values are ordered by ranked tag group and then within each tag group, while the untested values are ordered by ranked tag group and then randomly within the tag group. Recommended values are selected from the ranked list of previously used values and untested values depending on the degree of exploration/conservatism indicated by the user.

Claims

1. A method for recommending a grammar for a message campaign used by a message optimization system, the method comprising:

enabling a campaign manager to specify one or more parameters for a new campaign;

calculating a set of statistical design budgets based on one or more of the specified parameters;

recommending at least one grammar structure based on the set of statistical design budgets, wherein the grammar structure specifies a plurality of message component types;

enabling a campaign manager to select one of the recommended grammar structures for the new campaign;

identifying the n-most relevant past campaigns based on the specified parameters;

for each message component type in the selected grammar structure, generating a ranked list of previously-used values for the component type in the n-most relevant past campaigns, wherein the values are ranked at least in part based on performance in the n-most relevant past campaigns;

for each message component type, selecting a plurality of values to recommend based at least in part on the ranked list; and

enabling the campaign manager to reject one or more of the recommended values; and

in response to the campaign manager rejecting one or more of the recommended values, providing alternate recommended values for the rejected values.

2. The method of claim 1, wherein the previously-used values are each associated with a semantic tag and generating the ranked list comprises:

identifying the tags associated with the previously-used values in the n-most relevant campaigns;

ranking the tags based on performance of tagged values in the n-most relevant campaigns; and

ordering the previously-used values first by ranked tag group and second, within each tag group, by the number of time an individual value has been identified as the winning value in the n-most relevant campaigns.

3. The method of claim 1, wherein the recommended values are selected in part from the ranked list of previously-used values and in part from a ranked list of untested values.

4. The method of claim 3, wherein a plurality of untested values are grouped by semantic tag and ranked according to their semantic tag, wherein within a tag group an untested value is randomly ranked.

5. The method of claim 3, wherein the percentage of recommended untested values depends on a degree of exploration/conservatism indicated by the campaign manager.

6. The method of claim 4, wherein the recommended values are also selected in part from a group of untested values having an associated untested/unranked semantic tag.

7. The method of claim 6, wherein each untested value in the group of untested values is associated with a confidence level based on the global ranking of the untested value across all campaigns.

8. The method of claim 1, wherein enabling a campaign manager to specify parameters for a new campaign comprises providing a user interface wherein the campaign manager is prompted to enter parameters for the campaign.

9. The method of claim 8, wherein one or more parameters in the user interface is associated with a drop down menu that lists the options available to the campaign manager for the parameter.

10. The method of claim 8, wherein the parameters comprise campaign duration, audience size and characteristics, expected response rate, effect size, constraints, and objectives of the campaign.

11. The method of claim 10, wherein the campaign manager selects the objectives of the campaign via a drop down menu, from a sliding scale, or by inputting a value.

12. The method of claim 8, wherein if the campaign manager does not specify certain parameters, default values, empirically determined based on past campaigns, are used.

13. The method of claim 1, wherein the statistical design budgets are calculated based on audience size, effect size, and expected response rate.

14. The method of claim 1, further comprising evaluating the recommended grammar based on various computed metrics.

15. The method of claim 1, wherein providing alternate recommended values for the rejected values comprises choosing values from the next best performing tag or another candidate value associated with the same tag as the previously rejected value.

16. A computer program embodied on a non-transitory computer-readable medium and comprising code, that, when executed by a computer system, enables the computer system to perform the following method for recommending a grammar for a message campaign used by a message optimization system, the method comprising:

enabling a campaign manager to specify one or more parameters for a new campaign;

calculating a set of statistical design budgets based on one or more of the specified parameters;

recommending at least one grammar structure based on the set of statistical design budgets, wherein the grammar structure specifies a plurality of message component types;

enabling a campaign manager to select one of the recommended grammar structures for the new campaign;

identifying the n-most relevant past campaigns based on the specified parameters;

for each message component type in the selected grammar structure, generating a ranked list of previously-used values for the component type in the n-most relevant past campaigns, wherein the values are ranked at least in part based on performance in the n-most relevant past campaigns;

for each message component type, selecting a plurality of values to recommend based at least in part on the ranked list; and

enabling the campaign manager to reject one or more of the recommended values; and

in response to the campaign manager rejecting one or more of the recommended values, providing alternate recommended values for the rejected values.

17. The method of claim 16, wherein the previously-used values are each associated with a semantic tag and generating the ranked list comprises:

identifying the tags associated with the previously-used values in the n-most relevant campaigns;

ranking the tags based on performance of tagged values in the n-most relevant campaigns; and

ordering the previously-used values first by ranked tag group and second, within each tag group, by the number of time an individual value has been identified as the winning value in the n-most relevant campaigns.

18. The method of claim 16, wherein the recommended values are selected in part from the ranked list of previously-used values and in part from a ranked list of untested values.

19. The method of claim 18, wherein a plurality of untested values are grouped by semantic tag and ranked according to their semantic tag, wherein within a tag group an untested value is randomly ranked.

20. The method of claim 18, wherein the percentage of recommended untested values depends on a degree of exploration/conservatism indicated by the campaign manager.

21. The method of claim 19, wherein the recommended values are also selected in part from a group of untested values having an associated untested/unranked semantic tag.

22. The method of claim 21, wherein each untested value in the group of untested values is associated with a confidence level based on the global ranking of the untested value across all campaigns.

23. The method of claim 16, wherein enabling a campaign manager to specify parameters for a new campaign comprises providing a user interface wherein the campaign manager is prompted to enter parameters for the campaign.

24. The method of claim 23, wherein one or more parameters in the user interface is associated with a drop down menu that lists the options available to the campaign manager for the parameter.

25. The method of claim 23, wherein the parameters comprise campaign duration, audience size and characteristics, expected response rate, effect size, constraints, and objectives of the campaign.

26. The method of claim 25, wherein the campaign manager selects the objectives of the campaign via a drop down menu, from a sliding scale, or by inputting a value.

27. The method of claim 23, wherein if the campaign manager does not specify certain parameters, default values, empirically determined based on past campaigns, are used.

28. The method of claim 16, wherein the statistical design budgets are calculated based on audience size, effect size, and expected response rate.

29. The method of claim 16, further comprising evaluating the recommended grammar based on various computed metrics.

30. The method of claim 16, wherein providing alternate recommended values for the rejected values comprises choosing values from the next best performing tag or another candidate value associated with the same tag as the previously rejected value.

31. A computer system for recommending a grammar for a message campaign used by a message optimization system, the system comprising:

a processor;

a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, causes the system to perform the operations of;

enabling a campaign manager to specify one or more parameters for a new campaign;

calculating a set of statistical design budgets based on one or more of the specified parameters;

recommending at least one grammar structure based on the set of statistical design budgets, wherein the grammar structure specifies a plurality of message component types;

enabling a campaign manager to select one of the recommended grammar structures for the new campaign;

identifying the n-most relevant past campaigns based on the specified parameters;

for each message component type in the selected grammar structure, generating a ranked list of previously-used values for the component type in the n-most relevant past campaigns, wherein the values are ranked at least in part based on performance in the n-most relevant past campaigns;

for each message component type, selecting a plurality of values to recommend based at least in part on the ranked list; and

enabling the campaign manager to reject one or more of the recommended values; and

in response to the campaign manager rejecting one or more of the recommended values, providing alternate recommended values for the rejected values.