Predicting application conversion using eye tracking

1. A computer-implemented method for determining an application experience of a user, comprising:

determining, by a computing device, baseline eye tracking data of a user interacting with an application, the baseline eye tracking data comprising a baseline frequency of pupil dilations of the user;

receiving, at the computing device, real-time eye tracking data of the user interacting with at least a first page of the application, the real-time eye tracking data comprising a real-time frequency of pupil dilations of the user;

determining, by the computing device, based at least on the real-time eye tracking data and the baseline eye tracking data, at least a current user experience regarding the first page, wherein the current user experience comprises a level of interest with respect to at least a subset of the first page, and wherein the level of interest is determined based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;

predicting, by the computing device, based on evaluating the current user experience, that the user is likely to discontinue use of the application;

determining, by the computing device, based at least on the prediction, an intervention that reduces a likelihood of the user discontinuing use of the application; and

interacting, by the computing device, with the user according to the intervention.

2. The computer-implemented method of claim 1 , wherein the baseline eye tracking data further comprises one or more of: point of gaze; saccadic eye movement duration; or saccadic eye movement patterns.

3. The computer-implemented method of claim 1 , wherein the intervention is determined by using a model to evaluate the current user experience for the first page and the likelihood of the user discontinuing use of the application.

4. The computer-implemented method of claim 1 , wherein the current user experience comprises one or more of: interest, excitement, fixation, and fatigue.

5. The computer-implemented method of claim 1 , wherein the intervention comprises at least one of: offering a discount, offering assisted support, offering self-support content, and providing a list of content items.

6. The computer-implemented method of claim 1 , wherein interacting with the user according to the intervention comprises at least one of: a real-time intervention, an off-line intervention, presenting content items on an interface of the user, and altering at least one content item of the interface of the user.

7. The computer-implemented method of claim 1 , wherein the current user experience comprises a user experience regarding at least one item on the first page.

8. The computer-implemented method of claim 1 , further comprising:

determining at least one metric selected from a list comprising:

a count of user clicks for the first page;

a total amount of time spent by the user on the first page;

an age of the user;

a gender of the user;

an occupation of the user; and

a location of the user; and

evaluating the at least one metric in addition to the current user experience, using a model, to determine the likelihood of the user discontinuing use of the application.

9. A computing device for determining an application experience of a user, the computing device comprising:

a memory; and

a processor configured to perform a method for determining an application experience of a user, the method comprising:

determining baseline eye tracking data of a user interacting with an application, the baseline eye tracking data comprising a baseline frequency of pupil dilations of the user;

receiving real-time eye tracking data of the user interacting with at least a first page of the application, the real-time eye tracking data comprising a real-time frequency of pupil dilations of the user;

determining, based at least on the real-time eye tracking data and the baseline eye tracking data, at least a current user experience regarding the first page, wherein the current user experience comprises a level of interest with respect to at least a subset of the first page, and wherein the level of interest is determined based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;

predicting, based on evaluating the current user experience, that the user is likely to discontinue use of the application;

determining, based at least on the prediction, an intervention that reduces a likelihood of the user discontinuing use of the application; and

interacting with the user according to the intervention.

10. The computing device of claim 9 , wherein the baseline eye tracking data further comprises one or more of: point of gaze; saccadic eye movement duration; or saccadic eye movement patterns.

11. The computing device of claim 9 , wherein the intervention is determined by using a model to evaluate the current user experience for the first page and the likelihood of the user discontinuing use of the application.

12. The computing device of claim 9 , wherein the current user experience comprises one or more of: interest, excitement, fixation, and fatigue.

13. The computing device of claim 9 , wherein the intervention comprises at least one of: offering a discount, offering assisted support, offering self-support content, and providing a list of content items.

14. The computing device of claim 9 , wherein interacting with the user according to the intervention comprises at least one of: a real-time intervention, an off-line intervention, presenting content items on an interface of the user, and altering at least one content item of the interface of the user.

15. The computing device of claim 9 , wherein the current user experience comprises a user experience regarding at least one item on the first page.

16. The computing device of claim 9 , wherein the method further comprises:

determining at least one metric selected from a list comprising:

a count of user clicks for the first page;

a total amount of time spent by the user on the first page;

an age of the user;

a gender of the user;

an occupation of the user; and

a location of the user; and

evaluating the at least one metric in addition to the current user experience, using a model, to determine the likelihood of the user discontinuing use of the application.

17. A computer-readable medium comprising instructions that when executed by a computing device cause the computing device to perform a method for determining an application experience of a user, the method comprising:

determining baseline eye tracking data of a user interacting with an application, the baseline eye tracking data comprising a baseline frequency of pupil dilations of the user;

receiving real-time eye tracking data of the user interacting with at least a first page of the application, the real-time eye tracking data comprising a real-time frequency of pupil dilations of the user;

determining, based at least on the real-time eye tracking data and the baseline eye tracking data, at least a current user experience regarding the first page, wherein the current user experience comprises a level of interest with respect to at least a subset of the first page, and wherein the level of interest is determined based on a comparison between the real-time frequency of pupil dilations and the baseline frequency of pupil dilations;

predicting, based on evaluating the current user experience, that the user is likely to discontinue use of the application;

determining, based at least on the prediction, an intervention that reduces a likelihood of the user discontinuing use of the application; and

interacting with the user according to the intervention.

18. The computer-readable medium of claim 17 , wherein the baseline eye tracking data further comprises one or more of: point of gaze; saccadic eye movement duration; or saccadic eye movement patterns.

19. The computer-readable medium of claim 17 , wherein the intervention is determined by using a model to evaluate the current user experience for the first page and the likelihood of the user discontinuing use of the application.

20. The computer-readable medium of claim 17 , wherein the current user experience comprises one or more of: interest, excitement, fixation, and fatigue.

21. The computer-readable medium of claim 17 , wherein the intervention comprises at least one of: offering a discount, offering assisted support, offering self-support content, and providing a list of content items.

22. The computer-readable medium of claim 17 , wherein interacting with the user according to the intervention comprises at least one of: a real-time intervention, an off-line intervention, presenting content items on an interface of the user, and altering at least one content item of the interface of the user.

23. The computer-readable medium of claim 17 , wherein the current user experience comprises a user experience regarding at least one item on the first page.

24. The computer-readable medium of claim 17 , wherein the method further comprises:

determining at least one metric selected from a list comprising:

a count of user clicks for the first page;

a total amount of time spent by the user on the first page;

an age of the user;

a gender of the user;

an occupation of the user; and

a location of the user; and

evaluating the at least one metric in addition to the current user experience, using a model, to determine the likelihood of the user discontinuing use of the application.