SYSTEM AND METHOD FOR PROVIDING CROWD SOURCED METRICS FOR NETWORK CONTENT BROADCASTERS
A system and method for providing crowd sourced metrics for broadcast content providers. For determinations of such metrics, user consumption of broadcast content in multiple streams by multiple content providers may be monitored and user consumption information regarding the broadcast content may be obtained. One or more content consumption metrics may be determined to quantify individual user consumption of the broadcast content. Audience metrics may be determined, for a content provider, to inform about users that are available to consume broadcast content provided by the content provider. Events within the broadcast content may be determined and event information regarding individual user consumption of the broadcast content at the event may be obtained. Event metrics may be determined based on the obtained event information to inform about consumption of the broadcast content at the event by users.
1 . A computer-implemented method, comprising:
receiving content consumption information associated with one or more broadcast streams;
dynamically determining content consumption metrics for a set of users, wherein the content consumption metrics correspond to historical broadcast content accessed by the set of users;
executing a trained machine-learning model using the content consumption metrics, wherein the trained machine-learning model generates a prediction of future audience metrics associated with the set of users, wherein the future audience metrics predict a likelihood of consumption of particular broadcast media over one or more future time intervals; and
facilitating a transmission that includes the future audience metrics, wherein when the future audience metrics are received at a content provider, the future audience metrics are used to continually optimize quantified consumption of additional broadcast content by any of the set of users.
2 . The method of claim 1 , wherein the content consumption metrics include time-shift information indicating time-shifted presentation of the historical broadcast content accessed by a user of the set of users.
3 . The method of claim 1 , further comprising:
detecting one or more events using the content consumption metrics; and
generating event content consumption information for the one or more events, wherein generating the event content consumption information includes the use of metadata detection, ID tag detection, header detection, voice recognition, image analysis, motion detection, or signal detection.
4 . The method of claim 1 , wherein the set of users are determined to be available to connect to the media stream based on a duration of time in which a display of a device of the user is on or off.
5 . The method of claim 1 , wherein the content consumption information includes indications of consumption of on demand content.
6 . The method of claim 1 , wherein the future audience metrics include an indication of an amount of available users of the set of users that will switch from a first broadcast stream to a second broadcast stream associated with the content provider within a future time interval.
7 . The method of claim 1 , wherein the future audience metrics include an indication of an amount of available users of the set of users that will switch off a first broadcast stream associated with the content provider within a future time interval.
8 . A system comprising:
one or more processors; and
a non-transitory computer-readable medium storing instructions that when executed by the one or more processors cause the one or more processors to perform operations including:
receiving content consumption information associated with one or more broadcast streams;
dynamically determining content consumption metrics for a set of users, wherein the content consumption metrics correspond to historical broadcast content accessed by the set of users;
executing a trained machine-learning model using the content consumption metrics, wherein the trained machine-learning model generates a prediction of future audience metrics associated with the set of users, wherein the audience metrics predict a likelihood of consumption of particular broadcast media over one or more future time intervals; and
facilitating a transmission that includes the future audience metrics, wherein when the future audience metrics are received at a content provider, the future audience metrics are used by the content provider to continually optimize quantified consumption of additional broadcast content by any of the set of users.
9 . The system of claim 8 , wherein the content consumption metrics include time-shift information indicating time-shifted presentation of the historical broadcast content accessed by a user of the set of users.
10 . The system of claim 8 , wherein the operations further include:
detecting one or more events using the content consumption metrics; and
generating event content consumption information for the one or more events, wherein generating the event content consumption information includes the use of metadata detection, ID tag detection, header detection, voice recognition, image analysis, motion detection, or signal detection.
11 . The system of claim 8 , wherein the set of users are determined to be available to connect to the media stream based on a duration of time in which a display of a device of the user is on or off.
12 . The system of claim 8 , wherein the content consumption information includes indications of consumption of on demand content.
13 . The system of claim 8 , wherein the future audience metrics include an indication of an amount of available users of the set of users that will switch from a first broadcast stream to a second broadcast stream associated with the content provider within a future time interval.
14 . The system of claim 8 , wherein the future audience metrics include an indication of an amount of available users of the set of users that will switch off a first broadcast stream associated with the content provider within a future time interval.
15 . A non-transitory computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to perform operations including:
receiving content consumption information associated with one or more broadcast streams;
dynamically determining content consumption metrics for a set of users, wherein the content consumption metrics correspond to historical broadcast content accessed by the set of users;
executing a trained machine-learning model using the content consumption metrics, wherein the trained machine-learning model generates a prediction of future audience metrics associated with the set of users, wherein the audience metrics predict a likelihood of consumption of particular broadcast media over one or more future time intervals; and
facilitating a transmission that includes the future audience metrics, wherein when the future audience metrics are received at a content provider, the future audience metrics are used by the content provider to continually optimize quantified consumption of additional broadcast content by any of the set of users.
16 . The non-transitory computer-readable medium of claim 15 , wherein the content consumption metrics include time-shift information indicating time-shifted presentation of the historical broadcast content accessed by a user of the set of users.
17 . The non-transitory computer-readable medium of claim 15 , wherein the operations further include:
detecting one or more events using the content consumption metrics; and
generating event content consumption information for the one or more events, wherein generating the event content consumption information includes the use of metadata detection, ID tag detection, header detection, voice recognition, image analysis, motion detection, or signal detection.
18 . The non-transitory computer-readable medium of claim 15 , wherein the set of users are determined to be available to connect to the media stream based on a duration of time in which a display of a device of the user is on or off.
19 . The non-transitory computer-readable medium of claim 15 , wherein the content consumption information includes indications of consumption of on demand content.
20 . The non-transitory computer-readable medium of claim 15 , wherein the future audience metrics include an indication of an amount of available users of the set of users that will switch from a first broadcast stream to a second broadcast stream associated with the content provider within a future time interval.