Predictive recommendation system

• US 10,825,046 B2
• Filed: 05/03/2019
• Issued: 11/03/2020
• Est. Priority Date: 06/28/2013
• Status: Active Grant

• Current Assignee: Groupon Incorporated
• Original Assignee: Groupon Incorporated
• Inventors: Wai, Lawrence Lee
• Primary Examiner(s): Cao, Vincent M
• Application Number:
  US16/403,054
• Publication Number:
  US 20200034879A1
• Time in Patent Office: 550 Days
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system based on an analysis of previous consumer behavior. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving data representing a user, the data including user identification and historical data; receiving a set of promotions recommended for the user; assigning the user to a consumer lifecycle model state based in part on the historical data and the user identification; selecting a ranking algorithm associated with the consumer lifecycle model state; and ranking the received set of promotions based on a predicted promotion relevance value associated with each promotion, the predicted promotion value being calculated using the ranking algorithm.

Claims

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

retrieve a set of promotions recommended for a particular consumer;

determine a level of consumer engagement for the particular consumer, the level of consumer engagement representing a measure of time that has elapsed since the particular consumer'"'"'s most recent interaction with content published by a promotion and marketing service, wherein the level of consumer engagement is one of;

a current engagement when no time has elapsed since the particular consumer'"'"'s most recent interaction with the content, a moderate engagement when a first predetermined window has elapsed since the particular consumer'"'"'s most recent interaction with the content, or an inactive engagement when a second predetermined window has elapsed since the particular consumer'"'"'s most recent interaction with the content;

determine a level of activation behavior representing whether or not the particular consumer has made a promotion purchase while interacting with the content published by the promotion and marketing service, wherein the level of activation behavior is one of;

an activated behavior when the particular consumer has made a promotion purchase while interacting with the content published by the promotion and marketing service or a not activated behavior when the particular consumer has not made a promotion purchase while interacting with the content published by the promotion and marketing service;

select a ranking algorithm associated with the level of consumer engagement and level of activation behavior, wherein the ranking provided by the ranking algorithm is based on a predicted probability that the ranking will result in a change in one or more of the level of consumer engagement of level of activation behavior; and

transmit for display, to the client device, a subset of promotions from the set of promotions, the subset of promotions comprising a top N number of promotions based on the ranking algorithm in an instance in which the particular customer accesses the promotion and marketing service via the client device associated with the particular customer.

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

receive data describing the particular consumer, the data comprising previous behavioral data signals collected and recorded each time that the particular consumer interacted with content published by a promotion and marketing service and displayed on at least one client device associated with the particular consumer.

3. The apparatus of claim 2, wherein the at least one memory stores instructions thereon that, with the at least one processor, further configure the apparatus to:

assign, based in part on the data, a consumer lifecycle model state of a plurality of consumer lifecycle model states to the particular consumer, wherein each consumer lifecycle model state represents a different combination of relative levels of consumer engagement and activation behavior.

4. The apparatus of claim 3, wherein the ranking algorithm is based on a ranking target, wherein the ranking target is a desired different consumer lifecycle model state to be assigned to the particular consumer based on an interaction by the particular consumer with the content published by the promotion and marketing service.

5. The apparatus of claim 4, wherein the ranking target is based on one or more of a quantity of orders per impression, a quantity of profit per impression, or a quantity of clicks per impression.

6. The apparatus of claim 1, wherein calculating a predicted promotion relevance value associated with a promotion using the ranking algorithm comprises:

generating a feature vector describing the promotion using feature data representing attributes of the particular consumer and attributes of the promotion; and

calculating the predicted promotion relevance value using the feature vector.

7. The apparatus of claim 6, wherein the feature data representing attributes of the particular consumer includes at least one of the historical data representing the particular consumer and the user identification.

8. The apparatus of claim 7, wherein the set of promotions is ordered according to a ranking based on the predicted promotion relevance value of each promotions of the set of promotions.

9. A computer program product, encoded on a computer-readable medium, operable to cause a data processing apparatus to:

retrieve a set of promotions recommended for a particular consumer;

determine a level of consumer engagement for the particular consumer, the level of consumer engagement representing a measure of time that has elapsed since the particular consumer'"'"'s most recent interaction with content published by a promotion and marketing service, wherein the level of consumer engagement is one of;

a current engagement when no time has elapsed since the particular consumer'"'"'s most recent interaction with the content, a moderate engagement when a first predetermined window has elapsed since the particular consumer'"'"'s most recent interaction with the content, or an inactive engagement when a second predetermined window has elapsed since the particular consumer'"'"'s most recent interaction with the content;

determine a level of activation behavior representing whether or not the particular consumer has made a promotion purchase while interacting with the content published by the promotion and marketing service, wherein the level of activation behavior is one of;

an activated behavior when the particular consumer has made a promotion purchase while interacting with the content published by the promotion and marketing service or a not activated behavior when the particular consumer has not made a promotion purchase while interacting with the content published by the promotion and marketing service;

select a ranking algorithm associated with the level of consumer engagement and level of activation behavior, wherein the ranking provided by the ranking algorithm is based on a predicted probability that the ranking will result in a change in one or more of the level of consumer engagement of level of activation behavior; and

transmit for display, to the client device, a subset of promotions from the set of promotions, the subset of promotions comprising a top N number of promotions based on the ranking algorithm in an instance in which the particular customer accesses the promotion and marketing service via the client device associated with the particular customer.

10. The computer program product of claim 9, wherein the at least one memory stores instructions thereon that, with the at least one processor, further configure the apparatus to:

receive data describing the particular consumer, the data comprising previous behavioral data signals collected and recorded each time that the particular consumer interacted with content published by a promotion and marketing service and displayed on at least one client device associated with the particular consumer.

11. The computer program product of claim 10, wherein the at least one memory stores instructions thereon that, with the at least one processor, further configure the apparatus to:

assign, based in part on the data, a consumer lifecycle model state of a plurality of consumer lifecycle model states to the particular consumer, wherein each consumer lifecycle model state represents a different combination of relative levels of consumer engagement and activation behavior.

12. The computer program product of claim 11, wherein the ranking algorithm is based on a ranking target, wherein the ranking target is a desired different consumer lifecycle model state to be assigned to the particular consumer based on an interaction by the particular consumer with the content published by the promotion and marketing service.

13. The computer program product of claim 12, wherein the ranking target is based on one or more of a quantity of orders per impression, a quantity of profit per impression, or a quantity of clicks per impression.

14. The computer program product of claim 9, wherein calculating a predicted promotion relevance value associated with a promotion using the ranking algorithm comprises:

generating a feature vector describing the promotion using feature data representing attributes of the particular consumer and attributes of the promotion; and

calculating the predicted promotion relevance value using the feature vector.

15. The computer program product of claim 14, wherein the feature data representing attributes of the particular consumer includes at least one of the historical data representing the particular consumer and the user identification.

16. The computer program product of claim 15, wherein the set of promotions is ordered according to a ranking based on the predicted promotion relevance value of each promotions of the set of promotions.

17. A computer-implemented method, comprising:

retrieving a set of promotions recommended for a particular consumer;

determining a level of consumer engagement for the particular consumer, the level of consumer engagement representing a measure of time that has elapsed since the particular consumer'"'"'s most recent interaction with content published by a promotion and marketing service, wherein the level of consumer engagement is one of;

a current engagement when no time has elapsed since the particular consumer'"'"'s most recent interaction with the content, a moderate engagement when a first predetermined window has elapsed since the particular consumer'"'"'s most recent interaction with the content, or an inactive engagement when a second predetermined window has elapsed since the particular consumer'"'"'s most recent interaction with the content;

determining a level of activation behavior representing whether or not the particular consumer has made a promotion purchase while interacting with the content published by the promotion and marketing service, wherein the level of activation behavior is one of;

an activated behavior when the particular consumer has made a promotion purchase while interacting with the content published by the promotion and marketing service or a not activated behavior when the particular consumer has not made a promotion purchase while interacting with the content published by the promotion and marketing service;

selecting a ranking algorithm associated with the level of consumer engagement and level of activation behavior, wherein the ranking provided by the ranking algorithm is based on a predicted probability that the ranking will result in a change in one or more of the level of consumer engagement of level of activation behavior; and

transmitting for display, to the client device, a subset of promotions from the set of promotions, the subset of promotions comprising a top N number of promotions based on the ranking algorithm in an instance in which the particular customer accesses the promotion and marketing service via the client device associated with the particular customer.

18. The method of claim 17, further comprising:

receiving data describing the particular consumer, the data comprising previous behavioral data signals collected and recorded each time that the particular consumer interacted with content published by a promotion and marketing service and displayed on at least one client device associated with the particular consumer.

19. The method of claim 18, further comprising:

assigning, based in part on the data, a consumer lifecycle model state of a plurality of consumer lifecycle model states to the particular consumer, wherein each consumer lifecycle model state represents a different combination of relative levels of consumer engagement and activation behavior.

20. The method of claim 19, wherein the ranking algorithm is based on a ranking target, wherein the ranking target is a desired different consumer lifecycle model state to be assigned to the particular consumer based on an interaction by the particular consumer with the content published by the promotion and marketing service.