How norm Measures Success: The Key Metrics We Track, and How they Can Help your Business

Adrentech’s norm - AI Knowledge Base Metrics for Measuring Performance

Adrentech’s norm – our AI knowledge base platform – has the ability to provide AI-generated answers to user questions based on knowledge articles provided by your business. You can read more about this feature, and norm here. Measuring customer perceived success of these AI-generated answers, along with other data collected through customer use of the platform, is key to your businesss' ability to harness norm at its full potential.

To ensure this, we track this data alongside a wide range of additional metrics that give us – and your business by extension – valuable insights into norm’s performance. These metrics are particularly important as they can allow your business to identify areas for improvement within the platform's knowledge base, judge customer satisfaction, and ensure customers are getting the best answers quickly.

In today's article, we will outline two categories of these metrics — top articles and generative search data — that are key to measuring success.

Top Articles

Top articles searched are the first category of data collected by norm. The collection of these most frequently accessed articles is particularly helpful, as this information can provide insights into what topic areas users need the most help in.

To track this, we collect metrics such as:

• Session Count – Tracks the number of unique visits an article has received over a given period. High session counts suggest relevance and popularity.
• Helpfulness Ratings – Customers can leave feedback on articles. Combined with session data, this can identify gaps and highlight content needing updates.
• Hit Count – Measures total number of accesses, including repeat visits. A high hit count may suggest repeated value or difficulty in finding answers quickly.

Tracking these metrics helps identify popular topics and expose weaknesses or redundancies in the content base.

Generative Search Data

Generative search data provides information about how successful AI-generated answers are in real time. It also gives your business insights into customer satisfaction with search responses.

Some of the key metrics collected include:

• Success Metric – Indicates whether a customer’s search returned at least one relevant article. Over time, this reveals coverage gaps or overrepresented topics.
• Numeric Score – Users rate the usefulness of generative responses (1 to 5 stars). These scores provide quantifiable user feedback on AI effectiveness.

These generative search metrics help tailor content updates and make the knowledge base more responsive to real customer needs.

In combining insights from article tracking and generative search data, Adrentech’s norm gives your business a roadmap to a better, smarter, and more effective AI knowledge base. From identifying content gaps to understanding user intent, these metrics power real-time improvement and long-term support efficiency.

Curious to learn more about how these metrics can transform your support experience?
Schedule a demo today.