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

One or more non-transitory computer-readable media for providing electronic marketing communications, the one or more non-transitory computer-readable media having encoded thereon computer-executable instructions for performing:

• programmatically retrieving promotion data indicative of a plurality of promotions from a computer memory;
• determining, a promotion score for each promotion of the plurality of promotions, wherein each promotion score comprises a programmatically generated likelihood that a consumer associated with a mobile computing device will purchase the promotion, wherein each promotion score is determined using a machine learning model and is based on consumer profile data associated with the consumer, stored consumer activity data associated with the consumer, and at least one of:
• current consumer activity data associated with the consumer, current local context data associated with the consumer, or predicted consumer activity data associated with the consumer, wherein the current consumer activity data comprises one or more of a current location of the consumer or a current motion status of the consumer;
• determining a threshold promotion score for the consumer based on at least a consumer segment associated with the consumer profile data of the consumer;
• dynamically adjusting the threshold promotion score based on a current time data and the current consumer activity data associated with the mobile computing device;
• determining 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, and wherein each timing rule is associated with a predictive temporal model that relates a predicted consumer purchase period at each candidate time period of the one or more candidate time periods to 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;
• generating electronic marketing communications associated with the plurality of promotions based on the promotion scores and the suitable time periods for providing electronic marketing communications to the mobile computing device;
• and
• transmitting an electronic marketing communication to the mobile computing device associated with the consumer, wherein the electronic marketing communication comprises a promotion selected based on its 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 a suitable time period associated with the promotion.

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 a promotion score for each promotion of the plurality of promotions, wherein each promotion score comprises a programmatically generated likelihood that a consumer associated with a mobile computing device will purchase the promotion, wherein each promotion score is determined using a machine learning model and is based on consumer profile data associated with the consumer, stored consumer activity data associated with the consumer, and at least one of:
• current consumer activity data associated with the consumer, current local context data associated with the consumer, or predicted consumer activity data associated with the consumer, wherein the current consumer activity data comprises one or more of a current location of the consumer or a current motion status of the consumer;
• determine a threshold promotion score for the consumer based on at least a consumer segment associated with the consumer profile data of the consumer;
• dynamically adjust the threshold promotion score based on a current time data and the current consumer activity data associated with the mobile computing device;
• determine 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 based on one or more timing rules, and wherein each timing rule is associated with a predictive temporal model that relates a predicted consumer purchase period at each candidate time period of the one or more candidate time periods to associated with 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;
• generate electronic marketing communications associated with the plurality of promotions based on the promotion scores and the suitable time periods for providing electronic marketing communications to the mobile computing device;
• and
• transmit an electronic marketing communication to the mobile computing device associated with the consumer, wherein the electronic marketing communication comprises a promotion selected based on its 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 a suitable time period associated with the promotion.