Automatic analysis of user experience issues to reduce resolution time
Systems and methods include monitoring user experience of one or more users accessing any of the Internet, cloud applications, and private applications; determining a user experience score for the one or more users; responsive to detecting a low user experience score for a user, performing one or more analyses on the user experience of the user; and determining a root cause of the low user experience score based on the one or more analyses. The systems and methods can include determining a remedial action for the user based on the root cause.
1. A method comprising steps of:
monitoring user experience of one or more users accessing any of the Internet, cloud applications, and private applications, wherein the monitoring includes collecting metrics at an application executing on one or more user devices associated with the one or more users and an application connector associated with any of the Internet, the cloud applications, and the private applications;
receiving the metrics from the application executing on the one or more user devices and the application connector;
assigning each of the one or more users a user experience score based on the one or more user's performance;
sending the metrics and the user experience score to a logging and analytics service;
logging the user experience score over time and corresponding metrics, for use in one or more analyses;
normalizing each of the one or more user experience scores of the one or more users, wherein the normalizing includes normalizing each of the one or more user experience scores based on each of the one or more user's peer group;
responsive to detecting a low normalized user experience score for a user, performing one or more analyses on the user experience of the user; and
determining a root cause of the low user experience score based on the one or more analyses.
2. The method of claim 1 , wherein the steps further include
comparing the low user experience score with a known good user experience score in the vicinity to determine the root cause; and
determining a remedial action for the user based on the root cause.
3. The method of claim 1 , wherein the steps further include
labeling low user experience scores with root causes and utilizing the corresponding metrics for training a machine learning model.
4. The method of claim 1 , wherein the steps further include
developing a plurality of baselines from the logged user experience scores based on the one or more users peer groups, for use in the one or more analyses.
5. The method of claim 1 , wherein the one or more analyses performed in response to a low user experience score include any of a single point analysis, a two point analysis, a baseline analysis, a macro clustering analysis, and a comparative analysis.
6. The method of claim 1 , wherein the one or more analyses include a plurality of analyses with results combined and ranked.
7. The method of claim 1 , wherein the one or more analyses include a baseline analysis which looks for changes in corresponding metrics when the user experience score goes lower.
8. The method of claim 1 , wherein the one or more analyses include a macro clustering analysis that takes current metrics when the user experience score is low and looks for similar characteristics.
9. The method of claim 1 , wherein the steps further include
receiving feedback based on the root cause and updating the one or more analyses based thereon.
10. A non-transitory computer-readable medium comprising instructions that, when executed, cause at least one processor to perform steps of:
monitoring user experience of one or more users accessing any of the Internet, cloud applications, and private applications, wherein the monitoring includes collecting metrics at an application executing on one or more user devices associated with the one or more users and an application connector associated with any of the Internet, the cloud applications, and the private applications;
receiving the metrics from the application executing on the one or more user devices and the application connector;
assigning each of the one or more users a user experience score based on the one or more user's performance;
sending the metrics and the user experience score to a logging and analytics service;
logging the user experience score over time and corresponding metrics, for use in one or more analyses;
normalizing each of the one or more user experience scores of the one or more users, wherein the normalizing includes normalizing each of the one or more user experience scores based on each of the one or more user's peer group;
responsive to detecting a low normalized user experience score for a user, performing one or more analyses on the user experience of the user; and
determining a root cause of the low user experience score based on the one or more analyses.
11. The non-transitory computer-readable medium of claim 10 , wherein the steps further include
comparing the low user experience score with a known good user experience score in the vicinity to determine the root cause; and
determining a remedial action for the user based on the root cause.
12. The non-transitory computer-readable medium of claim 10 , wherein the steps further include
labeling low user experience scores with root causes and utilizing the corresponding metrics for training a machine learning model.
13. The non-transitory computer-readable medium of claim 10 , wherein the steps further include
developing a plurality of baselines from the logged user experience scores based on the one or more users peer groups, for use in the one or more analyses.
14. The non-transitory computer-readable medium of claim 10 , wherein the one or more analyses performed in response to a low user experience score include any of a single point analysis, a two point analysis, a baseline analysis, a macro clustering analysis, and a comparative analysis.
15. The non-transitory computer-readable medium of claim 10 , wherein the one or more analyses include a plurality of analyses with results combined and ranked.
16. The non-transitory computer-readable medium of claim 10 , wherein the one or more analyses include a baseline analysis which looks for changes in corresponding metrics when the user experience score goes lower.
17. The non-transitory computer-readable medium of claim 10 , wherein the one or more analyses include a macro clustering analysis that takes current metrics when the user experience score is low and looks for similar characteristics.
18. The non-transitory computer-readable medium of claim 10 , wherein the steps further include
receiving feedback based on the root cause and updating the one or more analyses based thereon.