How to use the Customer Health Scorecard Generator.
Health scores should use defined signals and explain uncertainty. A low usage count can mean many things without customer context.
Make the workflow fit your task.
Choose observable health signals and define their windows, thresholds and weights. Mark missing data separately from a poor result, then show how each signal contributes. Review the score with account context before using it to prioritize follow-up.
- What you provide
- Available account signals and scoring policy.
- What you get
- Transparent health rubric without invented predictive accuracy.
See the input and the result.
Illustrative input and output · a teaching example, not a live WebAct run
Example input
Illustrative rubric supplied: recent use 0–2 points, unresolved issues 0–2, renewal readiness 0–2; equal weights. Account: use=2, issues=0, renewal readiness unknown. Show missingness.
Completed example
Known subtotal: 2 of 4 possible points across two measured signals. Missing: renewal readiness, maximum 2 points. Possible complete score: 2–4 out of 6. Do not report a final percentage until the missing signal is resolved. The zero issue score merits direct review even if another signal is strong.
Load this input into the prompt, then copy it to WebAct to try the task. Your result may differ from the illustration.
Decisions and troubleshooting.
Should missing usage data automatically produce a red health score?
No. Missing evidence and negative evidence are different. Flag the data gap and decide how it affects confidence.
Why does a high health score coexist with a serious unresolved issue?
A weighted average may hide a critical condition. Consider explicit escalation rules for severe issues and explain them beside the score.
Try it with your own source.
Replace the example with your material in the task prompt. Keep the requirements you need, then copy the task into WebAct.
Customize and copy the task ↑