Smarter Task Assignment
Introducing User Recommendation - the first feature of 'Smart Redbooth' - a collection of tools to simplify task and project management.
The concept behind User Recommendation is to make applying key features of a task (user, due date) both easier and faster. In order to improve this experience, we added a 'smart' layer that is both lightweight and convenient.
This version shows both the User and Due date(coming soon!) recommendations. They are highlighted purple for effect only.
How it works
User Recommendation contains two basic elements. First is the location of the user recommendation feature. It lives right next to your existing user assignment settings, and is activated simply by hovering over the field. To assign users to a task, click the users in 'Assign to'. This is both time saving (fewer clicks) and ensures you are assigning the task to the most appropriate users.
The current drop down of users is still available if you wish to assign to someone else, or use the search feature.
We will also introduce user recommendation in the task card popovers at a later date.
The second element includes the recommendations themselves.
- The first recommended user is you, the 'task assigner', which is existing behavior in Redbooth.
- The next user is the one most likely to be associated with similar tasks, based on history of that user and task names. We are calling this the smart recommendation because this is the one that the recommendation engine is providing. If you click a smart user, one more recommended user will appear.
- The third user is the most popular assigned user in the workspace.
In total there will be four recommendations in this hover area.
User Recommendation as it currently appears. A user is highlighted purple on mouse-hover.
User Recommendation is currently available for all Business plan and Business preview customers.
User Recommendation is followed by Due-date recommendation and additional tools to help teams track the progress of their tasks and projects.
Don't see the recommendation? If the user recommendation engine does not find a match, it will not always show a recommendation.