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GROW - Your daily guide to using

Your organization rolled out -- now what? While every team has unique data-driven strategies, starting each day with this four-step approach can help all users succeed.

Goals & Grouping

What is your highest priority for the day? An Account Manager might be most motivated to check on accounts with upcoming renewal dates, whereas a CSM may be most eager to help struggling accounts. Depending on your goal, filter or sort your dashboard to prioritize accounts.

Example of a dashboard filtered by status and revenue and sorted by churn-risk.

Review the data

Skim through the Health Scores for the remaining visible accounts. Scan the dynamic data columns (the green, red and gold fields) to review the behaviors impacting health scores and, if needed, turn on the detailed view to see specific data sources and metrics.

Open additional details

Slide open additional account details (click > at the end of an account row) to review sentiment and trends for specific customers, provide feedback on health score accuracy, and see suggested actions for that customer. If the suggestions seem right to you, convert them to tasks

The additional details slide-out shows trends, sentiment history, and suggested actions you can convert to tasks for each specific account.

Work your plan, collaboratively

Once you’ve completed your dashboard review, move into Workspaces (or, if you use a different system of action, to wherever you track tasks). Review the project board for in-progress tasks or to-dos and label, add notes, assign to teammates, and / or move to different columns depending on status. Make sure to review your tasks regularly as a team to stay aligned.

Create and assign to do cards in your Workspaces board based on the data you reviewed in your Customer Health Data dashboard.

Use data to guide your actions, and watch your customers’ health and success GROW!

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How we test accuracy and performance

Our AI predicts customer behavior with greater than 90% accuracy. How do we know? We test and measure the performance of our models regularly, in a variety of ways. Model training test accuracy - 94


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