CS teams would like to be more effective and get results faster.
SuccessPlay which drive the customer success workflow needs to be effective and tune according to the results it's achieved.
In order to make SuccessPlays more effective, users must have accurate information about the effectiveness of SuccessPlays.
Using SP Effectiveness Score, Totango users can understand the effectiveness of every SuccessPlays and take action to keep the effective SuccessPlays and to improve the less effective ones.
Use Cases
- As a user, I am able to view the SuccessPlays effectiveness score for every SuccessPlays.
- As a user, I am able to share my feedback on whether it was helpful or not. The system is gathering the feedback and based on it improves the SuccessPlays effectiveness algorithm to be more accurate and more meaningful.
- As a user, I am able to see empty SuccessPlays effectiveness score for "new" SuccessPlays. In this case, the SuccessPlays did not reach the maturity level for the algorithm to conclude its effectiveness.
Behind The Scenes
Totango AI team uses Machine Learning algorithm to calculate the SuccessPlays effectiveness score.
This algorithm is based on two dimensions: Effectiveness and Impact.
Effectiveness indicator calculates the influence of the tasks created by this SuccessPlay on the team productivity.
Productivity is measured based on the work volume and work pace of the team.
Impact indicator calculates the influence of the tasks created by this SuccessPlay on health improvements and revenue increase.
As this feature is based on a Machine Learning algorithm, the algorithm input parameters and the parameters influence are changing all the time.
The Machine Learning algorithm is changing, evaluating results and calculating the data on a weekly basis.
In order for the algorithm to start to produce results, the SuccessPlay should run for at least 60 days to start to provide results. You can expect accuracy improves in the SuccessPlays effectiveness score week by week.