Identifying patterns of learning content consumption across multiple entities and automatically determining a customized learning plan based on the patterns

1. A system of identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns, comprising:

one or more hardware processors;

an engagement engine operable to execute on one or more of the hardware processors, the engagement engine further operable to receive information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content,

the engagement engine further operable to generate a first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations,

wherein a consumption profile vector is determined as a function of a fraction of content interactions in a content catalog for a plurality of content catalogs during a time window, the first set of consumption profile vectors comprising a plurality of the consumption profile vector computed across multiple time windows and computed based on the given organization user data associated with consumption of the content, and the one or more second set of consumption profile vectors comprising a plurality of the consumption profile vector computed across the multiple time windows and computed based on cross-industry user data associated with consumption of the content for respective one or more cross-industry organizations, the consumption profile vector detecting trends in learning and a shift in strategic direction of the given organization and the one or more cross-industry organizations,

the engagement engine further operable to receive information associated with a target user in the given organization,

the engagement engine further operable to determine a customized learning plan for the target user in the given organization based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user,

the customized learning plan specifying the target user, one or more courses for learning, estimated time for consuming the one or more courses, and modality of consuming the one or more courses,

wherein a learning system automatically runs the one or more courses according to the modality at the estimated time by at least automatically opening a graphical user interface on a device associated with the user.

2. The system of claim 1 , further comprising a key performance indicator (KPI) generator operable to determine a set of complementary KPIs for selecting the target user.

3. The system of claim 2 , wherein to determine said complementary KPIs, the KPI generator is operable to identify distributional relationships between candidate metrics and existing metrics for the given organization, extract parameters characterizing a relationship of a candidate metric and an existing metric for each of the candidate metrics, and identify a representative subset of metrics using a list of the parameters.

4. The system of claim 2 , wherein the target user meeting the KPI criteria is selected.

5. The system of claim 1 , wherein the engagement engine determines said one or more courses based on generating a list of virtual folders that comprise virtual combinations of commonly consumed assets and a probability profile of the target user's interest in the virtual folders.

6. The system of claim 1 , wherein the information associated with content comprises hierarchy of content folders.

7. A computer-implemented method of identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns, the method performed by one or more processors, comprising:

receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content;

generating a first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations,

wherein a consumption profile vector is determined as a function of a fraction of content interactions in a content catalog for a plurality of content catalogs during a time window, the first set of consumption profile vectors comprising a plurality of the consumption profile vector computed across multiple time windows and computed based on the given organization user data associated with consumption of the content, and the one or more second set of consumption profile vectors comprising a plurality of the consumption profile vector computed across the multiple time windows and computed based on cross-industry user data associated with consumption of the content for respective one or more cross-industry organizations, the consumption profile vector detecting trends in learning and a shift in strategic direction of the given organization and the one or more cross-industry organizations;

receiving information associated with a target user in the given organization;

determining a customized learning plan for the target user in the given organization based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user,

the customized learning plan specifying the target user, one or more courses for learning, estimated time for consuming the one or more courses, and modality of consuming the one or more courses,

wherein a learning system automatically runs the one or more courses according to the modality at the estimated time by at least automatically opening a graphical user interface on a device associated with the user.

8. The method of claim 7 , further comprising determining a set of complementary key performance indicators (KPIs) for selecting the target user.

9. The method of claim 8 , wherein the determining a set of complementary key performance indicators (KPIs) comprises:

identifying distributional relationships between candidate metrics and existing metrics for the given organization;

for each of the candidate metrics, extracting parameters characterizing a relationship of a candidate metric and an existing metric; and

identifying a representative subset of metrics using a list of the parameters.

10. The method of claim 9 , wherein the identifying a representative subset of metrics is performed for each of the cross-industry organizations.

11. The method of claim 9 , wherein the target user meeting criteria of the complementary KPIs is selected.

12. The method of claim 7 , further comprising generating a list of virtual folders that comprise virtual combinations of commonly consumed assets and generating a probability profile of the target user's interest in the virtual folders, wherein the one or more courses are identified based on the list of virtual folders and the probability profile.

13. The method of claim 7 , wherein the information associated with content comprises hierarchy of content folders.

14. A computer readable storage medium storing a program of instructions executable by a machine to perform a method of identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns, the method comprising:

receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content;

generating a first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations,

wherein a consumption profile vector is determined as a function of a fraction of content interactions in a content catalog for a plurality of content catalogs during a time window, the first set of consumption profile vectors comprising a plurality of the consumption profile vector computed across multiple time windows and computed based on the given organization user data associated with consumption of the content, and the one or more second set of consumption profile vectors comprising a plurality of the consumption profile vector computed across the multiple time windows and computed based on cross-industry user data associated with consumption of the content for respective one or more cross-industry organizations, the consumption profile vector detecting trends in learning and a shift in strategic direction of the given organization and the one or more cross-industry organizations;

receiving information associated with a target user in the given organization;

determining a customized learning plan for the target user in the given organization based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user,

the customized learning plan specifying the target user, one or more courses for learning, estimated time for consuming the one or more courses, and modality of consuming the one or more courses,

wherein a learning system automatically runs the one or more courses according to the modality at the estimated time by at least automatically opening a graphical user interface on a device associated with the user.

15. The computer readable storage medium of claim 14 , further comprising determining a set of complementary key performance indicators (KPIs) for selecting the target user.

16. The computer readable storage medium of claim 15 , wherein the determining a set of complementary key performance indicators (KPIs) comprises:

identifying distributional relationships between candidate metrics and existing metrics for the given organization;

for each of the candidate metrics, extracting parameters characterizing a relationship of a candidate metric and an existing metric; and

identifying a representative subset of metrics using a list of the parameters.

17. The computer readable storage medium of claim 16 , wherein the identifying a representative subset of metrics is performed for each of the cross-industry organizations.

18. The computer readable storage medium of claim 16 , wherein the target user meeting criteria of the complementary KPIs is selected.

19. The computer readable storage medium of claim 14 , further comprising generating a list of virtual folders that comprise virtual combinations of commonly consumed assets and generating a probability profile of the target user's interest in the virtual folders, wherein the one or more courses are identified based on the list of virtual folders and the probability profile.

20. The computer readable storage medium of claim 14 , wherein the information associated with content comprises hierarchy of content folders.