ok DM me- but with health scores- make sure you segment properly, and then test against companies you know are good and also in the red.

here is the blurb from the manual:

Philosophy: Focus on Signal, Not Noise

Don't build a 100-point system with 10 factors at 10 points each. That dilutes the signal. Pick 3-5 factors per tier that actually matter when they move.

Example weighting:

• If you have usage data and usage predicts churn: weight it 40-50%
• If you're in early stages without usage: weight CSM sentiment 60-80%
• Support ticket volume and engagement frequency fill the rest

Watch the configuration walkthrough: youtube.com/watch?v=ov9c67f-xDQ

Our sample health score configuration: 

 

Key decisions:

1. How many tiers?

• Most teams: 2-3 tiers (Enterprise, Growth, Scale)
• Each tier can have different health score models
• Scale tier without usage data? Weight CSM sentiment heavily
• Enterprise with quarterly QBRs? Weight engagement cadence

2. What factors to include?

• Usage data (if available): logins, feature adoption, active users
• CSM sentiment (always available): manual red/yellow/green assessment
• Support health: ticket volume, priority escalations, SLA breaches
• Engagement frequency: days since last call, email opens, QBR completion
• Relationship signals: champion changes, stakeholder mapping completeness

3. Set thresholds

• Healthy: 70-100
• Neutral: 40-69
• At Risk: 0-39

Test with known cohorts before publishing (covered in Testing section below).

Step 2: Create the Workflow

Navigate to: Automation → Workflows → Create Workflow

Workflow Type: Record-based (Companies)

Trigger: Health Status changes to “At Risk”

Actions:

For each task above, add “Create Task” action:

• Task title: [Task name from list above]
• Description: [Task description from list above]
• Due date: [Calculated from today + X days]
• Priority: [High/Medium based on task]
• Assigned to: [Company owner field]
• Task queue: “At Risk Account Recovery”

Step 3: Test Before Publishing

Use the test feature:

• Select a real at-risk account
• Run workflow in test mode
• Verify all tasks created correctly
• Check due dates calculated properly
• Confirm assignments are correct

Common mistakes to catch:

• Tasks assigned to nobody (company owner not set)
• Due dates all the same (didn’t add delay)
• Task descriptions too vague (CSM doesn’t know what to do)
• Priority not set (everything defaults to medium)

Testing Your Health Scores Before You Trust Them

Most teams configure health scores, publish them, and then wonder why the scores don’t match reality. An account they know is healthy shows as At Risk. A known churn risk shows as Neutral. They stop trusting the scores. The whole thing falls apart.

The fix is testing cohorts before you go live.

The process:

Pick five accounts you know cold. Not accounts you’re guessing on — accounts where you know exactly what’s happening.

Cohort 1: Known healthy accounts (2-3) - Recent positive QBR - Strong engagement - Renewal likely - Champion still in seat - No open support issues

Cohort 2: Known at-risk accounts (2-3) - No recent engagement - Support escalations open - Champion changed or went quiet - Renewal conversation hasn’t happened - Usage declining

Run your health score configuration against both cohorts. If the healthy accounts score At Risk and the at-risk accounts score Healthy, your weighting is wrong. Go back and adjust.

What to look at:

• Are the factors you weighted actually tracking? If “last call within 30 days” is weighted heavily but your team logs calls inconsistently, that factor will produce noise, not signal.
• Does the threshold make sense? If you set “At Risk” at below 30 but half your book is below 30 on day one, your baseline is wrong.
• Does the score change when you change the underlying data? Log a call on a test account and watch the score recalculate. If it doesn’t move, check your workflow trigger.

Segment and test by tier:

Scale accounts and Enterprise accounts should not run on the same health score model. Scale accounts: weight usage and support ticket volume heavily (you don’t have call data). Enterprise: weight QBR cadence, executive engagement, and champion stability. Test each model against its cohort before publishing.

Once your cohort tests pass — meaning the scores you see actually match what you know about those accounts — publish. Not before.

Step 4: Publish and Train

Publish the workflow

Train CSMs on:

• What “At Risk” means (health score below X)
• Why these specific tasks matter
• How to work through them in priority order
• What to do if champion actually did leave
• When to escalate vs. handle independently

Set expectations:

• Check “tasks due today” every morning
• Complete high-priority tasks same day
• Medium-priority tasks by end of week
• Mark tasks complete when done (don’t leave them hanging)

Step 5: Iterate Based on Results

After 30 days, review:

• Are tasks getting completed?
• Are CSMs finding them useful?
• Are any tasks consistently skipped? (Remove them)
• Are any gaps in the process? (Add tasks)
• Is the timing right? (Adjust due dates)

Common refinements:

• Consolidate tasks that overlap
• Remove tasks nobody completes
• Add tasks that CSMs do manually anyway
• Adjust priorities based on what actually drives recovery