SYSTEMS AND METHODS FOR BEHAVIOURAL AND CONTEXTUAL DATA ANALYTICS
System and method for behavioral and contextual data analytics are disclosed. An example computer system to process observational data received from a wireless device includes a memory including machine readable instructions and a processor to execute the instructions to: process the observational data to identify temporally adjacent applications to generate usage metric data, the observational data including application usage data; build a behavior model based on the identified temporally adjacent applications, the behavior model to describe user behavior associated with the wireless device; and apply the behavior model to predict a usage duration of a second application in response to usage of a first application.
1 . A computing system for determining audience segments comprising:
a processor; and
a non-transitory computer readable storage medium, having stored thereon program instructions that, upon execution by the processor, cause performance of a set of operations comprising:
receiving, from a server, first-party data comprising usage metrics for wireless devices of a first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the first set of users;
ingesting the first-party data into a model trained using usage metrics for wireless devices of a second set of users different from the first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the second set of users;
querying the model based on a particular behavioral pattern represented by the usage metrics of the first-party data for wireless devices of a particular segment of the first set of users; and
based on the querying, outputting, by the model, one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern.
2 . The computing system of claim 1 , wherein the model is configured to analyze behavioral patterns of usage of wireless devices represented by usage metrics input into the model to determine one or more characteristics of users of the wireless devices that exhibited the behavioral patterns.
3 . The computing system of claim 1 , wherein the particular behavioral pattern comprises a pattern of exposure of the particular segment to one or more particular types of multimedia content.
4 . The computing system of claim 1 , the set of operations further comprising:
transmitting, to an external computing system, the one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern to facilitate use of the one or more determined characteristics by the external computing system to select targeted content.
5 . The computing system of claim 1 , wherein the usage metrics for the wireless devices of the first set of users comprises one or more of a frequency of usage or a duration of usage of one or more applications installed on the wireless devices.
6 . The computing system of claim 1 , the set of operations further comprising:
receiving additional usage metrics for wireless devices of a third set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the third set of users;
updating the model using the additional usage metrics;
querying the updated model based on the particular behavioral pattern; and
based on the querying of the updated model, outputting, by the model, one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.
7 . The computing system of claim 6 , the set of operations further comprising:
transmitting, to an external computing system, the one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.
8 . The computing system of claim 1 , wherein:
the model comprises a plurality of nodes and a plurality of weights, respective nodes of the plurality of nodes represent one or more of the first set of users or the second set of users, respective weights of the plurality of weights represent respective correlation coefficients between the respective nodes, and
the set of operations further comprises determining the plurality of weights based on the usage metrics.
9 . The computing system of claim 8 , wherein the model is at least one of: a neural network or a Markov model.
10 . A method comprising:
receiving, from a server, first-party data comprising usage metrics for wireless devices of a first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the first set of users;
ingesting the first-party data into a model trained using usage metrics for wireless devices of a second set of users different from the first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the second set of users;
querying the model based on a particular behavioral pattern represented by the usage metrics of the first-party data for wireless devices of a particular segment of the first set of users; and
based on the querying, outputting, by the model, one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern.
11 . The method of claim 10 , wherein the model is configured to analyze behavioral patterns of usage of wireless devices represented by usage metrics input into the model to determine one or more characteristics of users of the wireless devices that exhibited the behavioral patterns.
12 . The method of claim 10 , wherein the particular behavioral pattern comprises a pattern of exposure of the particular segment to one or more particular types of multimedia content.
13 . The method of claim 10 , further comprising:
transmitting, to an external computing system, the one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern to facilitate use of the one or more determined characteristics by the external computing system to select targeted content.
14 . The method of claim 10 , wherein the usage metrics for the wireless devices of the first set of users comprises one or more of a frequency of usage or a duration of usage of one or more applications installed on the wireless devices.
15 . The method of claim 10 , further comprising:
receiving additional usage metrics for wireless devices of a third set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the third set of users;
updating the model using the additional usage metrics;
querying the updated model based on the particular behavioral pattern;
based on the querying of the updated model, outputting, by the model, one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern; and
transmitting, to an external computing system, the one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.
16 . A non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by a processor, cause performance of a set of operations comprising:
receiving, from a server, first-party data comprising usage metrics for wireless devices of a first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the first set of users;
ingesting the first-party data into a model trained using usage metrics for wireless devices of a second set of users different from the first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the second set of users;
querying the model based on a particular behavioral pattern represented by the usage metrics of the first-party data for wireless devices of a particular segment of the first set of users; and
based on the querying, outputting, by the model, one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the particular behavioral pattern comprises a pattern of exposure of the particular segment to one or more particular types of multimedia content.
18 . The non-transitory computer-readable storage medium of claim 16 , the set of operations further comprising:
transmitting, to an external computing system, the one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern to facilitate use of the one or more determined characteristics by the external computing system to select targeted content.
19 . The non-transitory computer-readable storage medium of claim 16 , wherein the usage metrics for the wireless devices of the first set of users comprises one or more of a frequency of usage or a duration of usage of one or more applications installed on the wireless devices.
20 . The non-transitory computer-readable storage medium of claim 16 , the set of operations further comprising:
receiving additional usage metrics for wireless devices of a third set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the third set of users;
updating the model using the additional usage metrics;
querying the updated model based on the particular behavioral pattern;
based on the querying of the updated model, outputting, by the model, one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern; and
transmitting, to an external computing system, the one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.