Predictive recommendation system
First Claim
1. A computer-implemented method for relevance ranking of promotions that are available from a promotion and marketing service and are to be recommended to a particular consumer, the method comprising:
- receiving, using a processor, data describing the particular consumer, the data including user identification data including a bcookie and historical data including previous behavioral data signals collected and recorded each time that the consumer interacted with content published by the promotion and marketing service and displayed on at least one client device associated with the particular consumer, wherein the historical data is retrieved using the bcookie;
receiving, using the processor, a set of promotions recommended for the particular consumer, wherein the set of promotions recommended for the particular consumer is selected by executing a workflow sequence of filtering rules and algorithms that are applied to stored data representing attributes of promotions and the particular consumer, wherein executing the workflow sequence includes ordering the set of promotions according to a ranking of relevance to the consumer;
assigning, using the processor and based in part on the historical data and the user identification 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, the consumer lifecycle model state is assigned by the processor performing;
determining a level of consumer engagement representing a measure of time that has elapsed since the particular consumer'"'"'s most recent interaction with content published by the 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; and
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, using the processor, a ranking algorithm associated with the consumer lifecycle model state assigned to the particular consumer, wherein the ranking provided by the ranking algorithm is based on a predicted probability that the ranking will result in a different lifecycle model state being assigned to the particular consumer; and
transmitting for display, using the processor and 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.
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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.
27 Citations
31 Claims
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1. A computer-implemented method for relevance ranking of promotions that are available from a promotion and marketing service and are to be recommended to a particular consumer, the method comprising:
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receiving, using a processor, data describing the particular consumer, the data including user identification data including a bcookie and historical data including previous behavioral data signals collected and recorded each time that the consumer interacted with content published by the promotion and marketing service and displayed on at least one client device associated with the particular consumer, wherein the historical data is retrieved using the bcookie; receiving, using the processor, a set of promotions recommended for the particular consumer, wherein the set of promotions recommended for the particular consumer is selected by executing a workflow sequence of filtering rules and algorithms that are applied to stored data representing attributes of promotions and the particular consumer, wherein executing the workflow sequence includes ordering the set of promotions according to a ranking of relevance to the consumer; assigning, using the processor and based in part on the historical data and the user identification 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, the consumer lifecycle model state is assigned by the processor performing; determining a level of consumer engagement representing a measure of time that has elapsed since the particular consumer'"'"'s most recent interaction with content published by the 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; anddetermining 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, using the processor, a ranking algorithm associated with the consumer lifecycle model state assigned to the particular consumer, wherein the ranking provided by the ranking algorithm is based on a predicted probability that the ranking will result in a different lifecycle model state being assigned to the particular consumer; and transmitting for display, using the processor and 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system for relevance ranking of promotions that are available from a promotion and marketing service and are to be recommended to a particular consumer by performing operations, comprising:
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one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to; receive data describing the particular consumer, the data including user identification data including a bcookie and historical data including previous behavioral data signals collected and recorded each time that the consumer interacted with content published by the promotion and marketing service and displayed on at least one client device associated with the particular consumer, wherein the historical data is retrieved using the bcookie; receive a set of promotions recommended for the particular consumer, wherein the set of promotions recommended for the particular consumer is selected by executing a workflow sequence of filtering rules and algorithms that are applied to stored data representing attributes of promotions and the particular consumer, wherein executing the workflow sequence includes ordering the set of promotions according to a ranking of relevance to the consumer; assign, based in part on the historical data and the user identification 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, the consumer lifecycle model state is assigned by; determining a level of consumer engagement representing a measure of time that has elapsed since the particular consumer'"'"'s most recent interaction with content published by the 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; anddetermining 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 consumer lifecycle model state assigned to the particular consumer, wherein the ranking provided by the ranking algorithm is based on a predicted probability that the ranking will result in a different lifecycle model state being assigned to the particular consumer; 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. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. A computer program product for relevance ranking of promotions that are available from a promotion and marketing service and are to be recommended to a particular consumer, encoded on a computer-readable medium, operable to cause a data processing apparatus to:
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receive data describing the particular consumer, the data including user identification data including a bcookie and historical data including previous behavioral data signals collected and recorded each time that the consumer interacted with content published by the promotion and marketing service and displayed on at least one client device associated with the particular consumer, wherein the historical data is retrieved using the bcookie; receive a set of promotions recommended for the particular consumer, wherein the set of promotions recommended for the particular consumer is selected by executing a workflow sequence of filtering rules and algorithms that are applied to stored data representing attributes of promotions and the particular consumer, wherein executing the workflow sequence includes ordering the set of promotions according to a ranking of relevance to the consumer; assign, based in part on the historical data and the user identification 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, the consumer lifecycle model state is assigned by; determining a level of consumer engagement representing a measure of time that has elapsed since the particular consumer'"'"'s most recent interaction with content published by the 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; anddetermining 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 consumer lifecycle model state assigned to the particular consumer, wherein the ranking provided by the ranking algorithm is based on a predicted probability that the ranking will result in a different lifecycle model state being assigned to the particular consumer; 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. - View Dependent Claims (23, 24, 25, 26, 27, 28, 29, 30, 31)
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Specification