Real-time event analysis utilizing relevance and sequencing
A computer system operates to detect a series of activities performed by a user, where the activities include interactions as between the user and one or more user interface components. The computer system recognizes the of activities as a sequence of events, where each event of the sequence corresponds to one more activities of the series. In response to the computer system detecting a current user activity, the computer system determines at least one of a user intent or interest based on an analysis of a relevant portion of the sequence of events.
1. A computing system comprising:
one or more processors;
a memory storing instructions that, when executed by the one or more processors, cause the computing system to:
detect real-time activity data from a computing device of an unknown user, the real-time activity data corresponding to a series of activities performed by the unknown user during a current application session using one or more user interface components of a user interface presented on the computing device of the unknown user, wherein the current application session corresponds to the unknown user interacting with an enterprise resource;
train a machine learning predictive model, based at least in part on other users engaging with the enterprise resource, as to whether an outcome predicted in accordance with a sequence of events came to pass, wherein each event of the sequence of events is associated with a corresponding set of parameters, and wherein the one or more processors access the instructions to determine a value for at least one parameter of the corresponding set of parameters for the associated event;
wherein the corresponding set of parameters for at least a first event of the sequence of events includes a set of related activity parameters, the set of related activity parameters including a first related activity parameter that identifies information about one or more activities of a particular type that preceded or followed the first event;
execute the machine learning predictive model to determine a user intent probability that the unknown user will engage with selected content during the current application session; and
based on the user intent probability, (i) determine whether the unknown user is indecisive towards performing an action to engage with the selected content on the computing device of the unknown user during the current session; and (ii) implement a trigger for causing the user to perform the action during the current session.
2. The computing system of claim 1 , wherein the real-time activity data corresponds to a number of page views in the current application session.
3. The computing system of claim 1 , wherein the real-time activity data indicates interest in a product, and the trigger comprises at least one of a particular type of communication and a type of promotion.
4. The computing system of claim 3 , wherein the selected content corresponds to a user interface element that enables the unknown user to purchase the product.
5. The computing system of claim 1 , wherein the enterprise resource comprises an e-commerce resource.
6. A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to:
detect real-time activity data from a computing device of an unknown user, the real-time activity data corresponding to a series of activities performed by the unknown user during a current application session using one or more user interface components of a user interface presented on the computing device of the unknown user, wherein the current application session corresponds to the unknown user interacting with an enterprise resource;
train a machine learning predictive model, based at least in part on other users engaging with the enterprise resource, as to whether an outcome predicted in accordance with a sequence of events came to pass, wherein each event of the sequence of events is associated with a corresponding set of parameters, and wherein the one or more processors determine a value for at least one parameter of the corresponding set of parameters for the associated event;
wherein the corresponding set of parameters for at least a first event of the sequence of events includes a set of related activity parameters, the set of related activity parameters including a first related activity parameter that identifies information about one or more activities of a particular type that preceded or followed the first event;
execute the machine learning predictive model to determine a user intent probability that the unknown user will engage with selected content during the current application session; and
based on the user intent probability, (i) determine whether the unknown user is indecisive towards performing an action to engage with the selected content on the computing device of the unknown user during the current session; and (ii) implement a trigger for causing the user to perform the action during the current session.
7. The non-transitory computer readable medium of claim 6 , wherein the real-time activity data corresponds to a number of page views in the current application session.
8. The non-transitory computer readable medium of claim 6 , wherein the real-time activity data indicates interest in a product, and the trigger comprises at least one of a particular type of communication and a type of promotion.
9. The non-transitory computer readable medium of claim 8 , wherein the selected content corresponds to a user interface element that enables the unknown user to purchase the product.
10. The non-transitory computer readable medium of claim 6 , wherein the enterprise resource comprises an e-commerce resource.
11. A computer-implemented method of reducing network latency, the method being performed by one or more processors and comprising:
detecting real-time activity data from a computing device of a unknown user, the real-time activity data corresponding to a series of activities performed by the unknown user during a current application session using one or more user interface components of a user interface presented on the computing device of the unknown user, wherein the current application session corresponds to the unknown user interacting with an enterprise resource;
training a machine learning predictive model, based at least in part on other users engaging with the enterprise resource, as to whether an outcome predicted in accordance with a sequence of events came to pass, wherein each event of the sequence of events is associated with a corresponding set of parameters, and wherein the one or more processors determine a value for at least one parameter of the corresponding set of parameters for the associated event;
wherein the corresponding set of parameters for at least a first event of the sequence of events includes a set of related activity parameters, the set of related activity parameters including a first related activity parameter that identifies information about one or more activities of a particular type that preceded or followed the first event;
executing the machine learning predictive model to determine a user intent probability that the unknown user will engage with selected content during the current application session; and
based on the user intent probability, (i) determine whether the unknown user is indecisive towards performing an action to engage with the selected content on the computing device of the unknown user during the current session; and (ii) implement a trigger for causing the user to perform the action during the current session.
12. The method of claim 11 , wherein the real-time activity data corresponds to a number of page views in the current application session.
13. The method of claim 12 , wherein the real-time activity data indicates interest in a product, and the trigger comprises at least one of a particular type of communication and a type of promotion.
14. The method of claim 13 , wherein the selected content corresponds to a user interface element that enables the unknown user to purchase the product.