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

# Extracting Numeric Values From Customer Feedback

> Turning customer text into measurable numeric insights.

Most customer and employee feedback is unstructured, people express emotions, describe problems, share expectations, compare experiences, and add lots of subtle detail.\
Numeric Smart Columns help you convert this free-form text into **clear, measurable values** that can be used in charts, filters, reports, and driver analyses.

This turns qualitative text into **quantitative insights** your team can act on.

Some examples of numeric Smart Columns are:

* **Severity Score**: how painful or serious an issue is
* **Satisfaction Score**: how satisfied the user feels based on their message
* **Engagement Score:** how engaged employees are

All these examples show how numeric extraction unlocks deeper analysis inside Caplena.

Numeric values allow you to:

* **quantify** text-based feedback
* **prioritize** what needs attention
* identify **key drivers** of dissatisfaction or satisfaction
* track **trends over time**

### **Example: Severity Score** 

This Smart Column assigns a value from **1 to 5** based on how severe the user’s problem is.\
It helps Support, CX, and Product teams quickly identify high-impact issues. 

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**Use cases:**

* Identify high-severity complaints
* Filter severity ≥ 4
* Build driver chart (What drives severity?)
* Visualize severity by topic or brand

### Example: Employee Engagement Score

This is especially helpful for internal HR, pulse surveys, and employee listening programs.

<iframe src="https://www.loom.com/embed/65bf2acaab0b421cb2b90995a761079a" title="Loom video player" frameborder="0" className="w-full aspect-video rounded-xl" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen />

**Use cases:**

* Track engagement across teams or locations
* Identify early signs of burnout or dissatisfaction
* Compare engagement drivers (workload, leadership, recognition)
* Combine with topics to reveal root causes
