Abstract: Provided herein are systems and methods for hyper-personalizing digital guidance for improved user adoption of an underlying computer application. In one exemplary implementation, a method includes identifying an underlying application, gathering usage data at a user level for n days, choosing at least two methods from the group consisting of sequence analysis, top popular analysis, repeat usage analysis, similar users analysis and popular analysis, combining results from the at least two chosen methods, and recommending content to a recommendation user based upon the combined results.

Abstract: Provided herein are systems and methods for personalizing digital guidance for improved user adoption of an underlying computer application. In one exemplary implementation, a method includes identifying an underlying application, identifying different pages of the application, gathering usage data at a user level for n days, creating a user behavior matrix from the gathered data, performing a user similarity calculation for each pair of the users, tabulating a consumption count for each of the users, performing a series of score calculations for a recommendation user, calculating an intermediate score for each piece of content, counting a number of users who clicked on each piece of content, obtaining a final score for each piece of content, deciding on a ranking order of the content based on the final scores, and recommending content to the recommendation user from the ranking step.

Abstract: Provided herein are systems and methods for personalizing digital guidance for improved user adoption of an underlying computer application. In one exemplary implementation, a method includes identifying an underlying application, identifying different pages of the application, gathering usage data at a user level for n days, creating a user behavior matrix from the gathered data, performing a user similarity calculation for each pair of the users, tabulating a consumption count for each of the users, performing a series of score calculations for a recommendation user, calculating an intermediate score for each piece of content, counting a number of users who clicked on each piece of content, obtaining a final score for each piece of content, deciding on a ranking order of the content based on the final scores, and recommending content to the recommendation user from the ranking step.