> ## Documentation Index
> Fetch the complete documentation index at: https://docs.quivly.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Interpreting Scores

> How to read and act on customer health scores

## Overview

Health scores provide a composite view of customer health, but the category breakdown is where actionable insight lives. This page covers how to read scores effectively.

***

## Reading the Score

The overall score (0-100) maps to a risk level based on your configured score buckets. By default:

| Risk Level   | Range  | Suggested Action                             |
| ------------ | ------ | -------------------------------------------- |
| **Healthy**  | 75-100 | Maintain cadence, explore expansion          |
| **Medium**   | 50-74  | Monitor trends, standard touchpoints         |
| **At Risk**  | 25-49  | Increase engagement, address specific issues |
| **Critical** | 0-24   | Urgent intervention needed                   |

***

## Using the Category Breakdown

The overall score is a starting point. Click into the Health Score tab on any customer to see the category breakdown and identify which area is driving the score up or down.

**What to look for:**

* **Lowest category score** - This is where to focus intervention
* **Score vs weight mismatch** - A low score in a heavily weighted category has outsized impact
* **Category with the most change** - Use the trend indicator to spot which areas are shifting

***

## Understanding Trends

On the Health Score tab, the history chart shows score changes over time:

* **Gradual decline** over weeks suggests a real trend that needs attention
* **Sudden drop** may indicate a specific event (e.g., spike in support tickets, missed renewal)
* **Score change at a version marker** indicates a configuration change, not a change in customer behavior

***

## When Scores Don't Match Expectations

If a score doesn't align with your knowledge of the customer:

* **Check for missing data** - A disconnected integration or missing usage data can skew scores
* **Review individual metrics** - Use the Test tab or the score breakdown to see which metrics are contributing unexpected values
* **Consider timing** - New customers in onboarding may have low scores that improve as they ramp up