Understanding how your knowledge base content performs helps you improve it. Support Station provides feedback collection and analytics to show what's working and what needs attention.

Article Feedback

How Feedback Works

At the bottom of each published article, customers see:

👍 "Yes, this helped"
👎 "No, this didn't help"

Customers can optionally leave a comment explaining their feedback.

Viewing Feedback

On individual articles:

Open the article
View the feedback summary showing thumbs up vs down
Read feedback comments if any were submitted

Across all articles:

Go to Knowledge Base
Sort by helpfulness to see top/bottom performers
Focus improvement efforts on articles with low ratings

Article Analytics

View Counts

Track how often articles are viewed:

Total views since publication
Views over time periods (day, week, month)
View trends (increasing, decreasing, stable)

Where to Find Analytics

Per article:

Open any article to see its view count
Historical view data in the article details

Knowledge base overview:

Go to Analytics > Knowledge Base
See aggregate stats across all content

Key Metrics

Helpfulness Rate

The percentage of readers who found an article helpful:

Helpful votes / Total votes × 100 = Helpfulness Rate

Targets:

90%+ - Excellent, article is serving customers well
70-89% - Good, minor improvements possible
Below 70% - Needs attention, review and improve

Views

Raw traffic to each article. High views indicate:

Popular topic
Good SEO performance
Frequently linked content

Low views might mean:

Niche topic (could be fine)
Poor discoverability
Topic not relevant to customers

Feedback Volume

Articles with many feedback submissions (votes and comments) are actively engaging customers—both positively and negatively.

Using Analytics to Improve

Identify Top Performers

Articles with high views AND high helpfulness:

Promote these prominently
Use as templates for new content
Link to them from related articles

Find Problem Areas

Articles with low helpfulness:

Read the feedback comments
Identify what's unclear or missing
Rewrite or restructure

Spot Gaps

Look for:

Topics with no articles but frequent support tickets
Articles customers search for but don't find
Questions AI can't answer (check AI escalations)

Track Improvements

After updating an article:

Note the date of changes
Monitor helpfulness over the following weeks
See if ratings improve

Feedback Comments

When customers vote "No, this didn't help," they can leave a comment. These are invaluable:

Common Comment Themes

Comment Type	What It Means	Action
"Outdated"	Content no longer accurate	Update article
"Didn't answer my question"	Missing information	Add coverage
"Confusing"	Unclear writing	Simplify and restructure
"Can't find X"	Navigation issue	Add links, improve search
"Steps didn't work"	Incorrect instructions	Verify and fix steps

Responding to Feedback

You can't directly reply to article feedback, but you can:

Update the article based on the feedback
Add clarification for common confusion points
Create new articles for uncovered topics

Knowledge Base Dashboard

The Knowledge Base section of Analytics shows:

Overview Stats

Total articles published
Total article views (period)
Overall helpfulness rate
AI chat interactions

Article Performance Table

Article title
Category
Views
Helpfulness rate
Last updated

Sort by any column to find:

Most viewed articles
Least helpful articles
Recently updated content

Trends Over Time

Views per day/week
Feedback volume
AI deflection rate

Optimizing Based on Data

Weekly Review

Set aside time weekly to:

Check articles with new negative feedback
Review most-viewed articles for accuracy
Identify trending topics to address

Monthly Deep Dive

Monthly, conduct a fuller review:

Sort by helpfulness, address bottom 10%
Look for topic gaps based on ticket analysis
Archive outdated content
Plan new content based on trends

Quarterly Audit

Quarterly, do a comprehensive audit:

Review entire category structure
Check all articles are still accurate
Consolidate or split articles as needed
Set goals for the coming quarter

Connecting KB Analytics to Support Metrics

Correlate knowledge base performance with support:

Does better KB content reduce ticket volume?
Which KB gaps generate the most tickets?
How does AI deflection rate correlate with KB quality?

A strong knowledge base should:

Decrease tickets on documented topics
Improve AI accuracy
Reduce repeat questions