Method and system for providing electronic marketing communications for a promotion and marketing service

1. An apparatus comprising at least one processor and at least one memory storing instructions that, with the at least one processor, cause the apparatus to:

determine, via the at least one processor, a threshold promotion score for a consumer based on at least a consumer segment associated with consumer profile data of the consumer;

dynamically adjust, via the at least one processor, the threshold promotion score based on a current time data and current consumer activity data associated with a mobile computing device of the consumer;

generate, via the at least one processor, a promotion score for each promotion of a plurality of promotions utilizing a machine learning system configured to:

receive data inputs for the consumer;

generate a multidimensional vector based on the data inputs;

programmatically execute a trained machine learning algorithm based on the multidimensional vector;

generate a probability value; and

determine the promotion score based on the probability value;

generate, via the at least one processor, one or more electronic marketing communications associated with the plurality of promotions based on the promotion score and a suitable time period for providing the one or more electronic marketing communications to a mobile computing device; and

transmit, via the at least one processor and a network during the suitable time period associated with the promotion, an electronic marketing communication to the mobile computing device associated with the consumer, wherein the electronic marketing communication comprises a promotion selected based on an associated promotion score and is transmitted based on at least the associated promotion score and the threshold promotion score associated with the consumer to which the electronic marketing communication is transmitted, during the suitable time period associated with the promotion.

2. The apparatus of claim 1 , wherein the at least one memory storing the instructions that, with the at least one processor, further cause the apparatus to:

determine, via the at least one processor, a plurality of suitable time periods for transmitting electronic marketing communications to the mobile computing device, wherein each suitable time period is associated with a promotion of the plurality of promotions and is selected from one or more candidate time periods based on one or more timing rules.

3. The apparatus of claim 1 , wherein the at least one memory storing the instructions that, with the at least one processor, further cause the apparatus to:

receive, via the at least one processor, promotion data indicative of the plurality of promotions.

4. The apparatus of claim 1 , wherein the at least one memory storing the instructions that, with the at least one processor, further cause the apparatus to:

determine, via the at least one processor, promotion scores for each promotion of the plurality of promotions.

5. The apparatus of claim 1 , wherein the suitable time period for providing the electronic marketing communications to the mobile computing device is determined based on at least one promotion of the plurality of promotions and one or more timing rules.

6. The apparatus of claim 1 , wherein a timing rule of the one or more timing rules is associated with a predictive temporal model that relates a predicted consumer purchase probability at each candidate time period of the one or more candidate time periods to one or more of:

the consumer profile data associated with the consumer;

a stored consumer activity data associated with the consumer;

the current consumer activity data associated with the consumer;

a current local context data associated with the consumer; or

a predicted consumer activity data associated with the consumer.

7. The apparatus of claim 1 , wherein the electronic marketing communication comprising a promotion selected from the plurality of promotions based on an associated promotion score.

8. A non-transitory computer-readable media having encoded thereon computer-executable instructions for performing a method for providing electronic marketing communications to a mobile computing device associated with a consumer, the method comprising:

determining, via at least one processor, a threshold promotion score for a consumer based on at least a consumer segment associated with consumer profile data of the consumer;

dynamically adjusting, via the at least one processor, the threshold promotion score based on a current time data and current consumer activity data associated with a mobile computing device of the consumer;

generating, via the at least one processor, a promotion score for each promotion of a plurality of promotions utilizing a machine learning system configured to:

receive data inputs for the consumer;

generate a multidimensional vector based on the data inputs;

programmatically execute a trained machine learning algorithm based on the multidimensional vector;

generate a probability value; and

determine the promotion score based on the probability value;

generating, via the at least one processor, one or more electronic marketing communications associated with the plurality of promotions based on the promotion score and a suitable time period for providing the one or more electronic marketing communications to a mobile computing device; and

transmitting, via the at least one processor and a network during the suitable time period associated with the promotion, an electronic marketing communication to the mobile computing device associated with the consumer, wherein the electronic marketing communication comprises a promotion selected based on an associated promotion score and is transmitted based on at least the associated promotion score and the threshold promotion score associated with the consumer to which the electronic marketing communication is transmitted, during the suitable time period associated with the promotion.

9. The non-transitory computer-readable media of claim 8 , wherein the computer-executable instructions for performing the method for providing electronic marketing communications to a mobile computing device associated with a consumer, the method further comprising:

determining, via the at least one processor, a plurality of suitable time periods for transmitting electronic marketing communications to the mobile computing device, wherein each suitable time period is associated with a promotion of the plurality of promotions and is selected from one or more candidate time periods based on one or more timing rules.

10. The non-transitory computer-readable media of claim 8 , wherein the computer-executable instructions for performing the method for providing electronic marketing communications to a mobile computing device associated with a consumer, the method further comprising:

receiving, via the at least one processor, promotion data indicative of the plurality of promotions.

11. The non-transitory computer-readable media of claim 8 , wherein the computer-executable instructions for performing the method for providing electronic marketing communications to a mobile computing device associated with a consumer, the method further comprising:

determining, via the at least one processor, promotion scores for each promotion of the plurality of promotions.

12. The non-transitory computer-readable media of claim 8 , wherein the suitable time period for providing the electronic marketing communication to the mobile computing device is determined based on at least one promotion of the plurality of promotions and one or more timing rules.

13. The non-transitory computer-readable media of claim 8 , wherein a timing rule of the one or more timing rules is associated with a predictive temporal model that relates a predicted consumer purchase probability at each candidate time period of the one or more candidate time periods to one or more of:

the consumer profile data associated with the consumer;

a stored consumer activity data associated with the consumer;

the current consumer activity data associated with the consumer;

a current local context data associated with the consumer; or

a predicted consumer activity data associated with the consumer.

14. The non-transitory computer-readable media of claim 8 , wherein the electronic marketing communication comprising a promotion selected from the plurality of promotions based on an associated promotion score.

15. A computer system, comprising:

a storage device configured to store:

promotion data indicative of a plurality of promotions,

consumer profile data,

stored consumer activity data, and at least one of:

current consumer activity data,

current local context data, or

predicted consumer activity data; and

processing circuitry comprising at least one processor configured to:

determine, via the at least one processor, a threshold promotion score for a consumer based on at least a consumer segment associated with the consumer profile data of the consumer;

dynamically adjust, via the at least one processor, the threshold promotion score based on a current time data and the current consumer activity data associated with a mobile computing device of the consumer;

generate, via the at least one processor, a promotion score for each promotion of a plurality of promotions utilizing a machine learning system configured to:

receive data inputs for the consumer;

generate a multidimensional vector based on the data inputs;

programmatically execute a trained machine learning algorithm based on the multidimensional vector;

generate a probability value; and

determine the promotion score based on the probability value;

generate, via the at least one processor, one or more electronic marketing communications associated with the plurality of promotions based on the promotion score and a suitable time period for providing the one or more electronic marketing communications to a mobile computing device; and

transmit, via the at least one processor and a network during the suitable time period associated with the promotion, an electronic marketing communication to the mobile computing device associated with the consumer, wherein the electronic marketing communication comprises a promotion selected based on an associated promotion score and is transmitted based on at least the associated promotion score and the threshold promotion score associated with the consumer to which the electronic marketing communication is transmitted, during the suitable time period associated with the promotion.

16. The computer system of claim 15 , wherein the processing circuitry comprising the at least one processor is further configured to:

determine, via the at least one processor, a plurality of suitable time periods for transmitting electronic marketing communications to the mobile computing device, wherein each suitable time period is associated with a promotion of the plurality of promotions and is selected from one or more candidate time periods based on one or more timing rules.

17. The computer system of claim 15 , wherein the processing circuitry comprising the at least one processor is further configured to:

receive, via the at least one processor, promotion data indicative of the plurality of promotions.

18. The computer system of claim 15 , wherein the processing circuitry comprising the at least one processor is further configured to:

determine, via the at least one processor, promotion scores for each promotion of the plurality of promotions.

19. The computer system of claim 15 , wherein the suitable time period for providing the electronic marketing communications to the mobile computing device is determined based on at least one promotion of the plurality of promotions and one or more timing rules.

20. The computer system of claim 15 , wherein a timing rule of the one or more timing rules is associated with a predictive temporal model that relates a predicted consumer purchase probability at each candidate time period of the one or more candidate time periods to one or more of:

the consumer profile data associated with the consumer;

the stored consumer activity data associated with the consumer;

the current consumer activity data associated with the consumer;

the current local context data associated with the consumer; or

the predicted consumer activity data associated with the consumer.