What are Knowledge Base Analytics & How to Measure it?
Knowledge base analytics comes with 7 specific metrics that will increase employee productivity as well as overall business growth.
Running a company isn’t just a simple task. Apart from ensuring to sail smoothly, you also need to stay ahead of the competitors. How can you be unique amongst the crowd?
70% of the customers prefer to find answers to their questions on a website rather than a phone call or email.
Let’s leverage knowledge base analytics into making a robust search engine that only leads to organizational success in the long run. In this article today, I’ll touch base about the benefits and metrics you should use for a happy customer experience.
Table of Content
- 1 What is Knowledge Base Analytics?
- 2 Why Do We Need Knowledge Base Analytics?
- 3 7 Metrics Used to Measure Effectiveness in Knowledge Base Analytics
- 4 Best Practices to Optimize Knowledge Base Performance with Analytics
- 5 Identify Areas for Improvement with Knowledge Base Analytics
- 6 FAQs on Knowledge Base Analytics
What is Knowledge Base Analytics?
Let’s first understand what knowledge base is. It is a centralized repository of information where customer support teams get access and provide users with self-service options for finding answers to their queries.
This analytical approach transforms raw usage data into actionable insights about content performance and user satisfaction. By monitoring metrics like search success rates, most accessed articles, and common user pathways, analytics reveals both the strengths as well as weaknesses of your information architecture, enabling data-driven decisions for continuous improvement.
Key objectives:
- Identify content gaps by analyzing frequently searched terms that return no relevant results.
- Optimize article performance by tracking which content receives high engagement versus low user satisfaction.
- Reduce support ticket volume by pinpointing common issues that users cannot resolve through self-service.
- Improve search functionality by understanding query patterns and refining search algorithms accordingly.
- Enhance user experience by mapping user journeys to eliminate navigation bottlenecks and streamline information discovery.
Why Do We Need Knowledge Base Analytics?
A study by Harvard in 2010 found that simply improving the help section helped reduce the incoming customer service calls by 5%. Here’s the list why knowledge base analytics are crucial:
- Increase Efficiency: Knowledge base analytics helps organizations determine which articles are most useful to users. So, you can prioritize content creation and updates accordingly. It also results in faster problem resolution with more efficient operations.
- Identify Training Needs: Knowledge base analytics reveal gaps in knowledge or areas where employees may require additional training or support. Addressing these needs improves an organization’s overall competency and productivity of their workforce.
- Support Decision Making: You learn user behavior and preferences to make strategic decisions related to content creation, resource allocation as well as overall knowledge management. This data-driven approach leads to more informed and successful decision making.
7 Metrics Used to Measure Effectiveness in Knowledge Base Analytics
54% of companies offering mobile self-service with the web saw an increase in website traffic quite immediately. But, how did they come to such a conclusion? Let’s explore those metrics:
1. Search Analytics
This metric tracks the performance of the search function within the knowledge base. What are the number of searches conducted by users, what are the most popular search terms and what’s the success rate of search queries, these are some of the metrics that this analysis measures. When calculated, it accurately identifies common user needs and improves the search functionality to deliver more relevant results.
Formula: Search success rate = (Number of successful searches / Total number of searches) x 100
Example: If there were 400 successful searches out of a total of 500 searches, then the search success rate would be (400/500) x 100 = 80%.
2. User Engagement
User engagement metrics, such as page views, time spent on page and click-through rates, provide insights into how users interact with the knowledge base content. Analyzing user engagement data helps businesses determine which articles are most popular, which topic of interest peaks users curiosity and how to optimize content to increase user engagement.
Formula: User engagement rate = (Number of engaged users / Total number of users) x 100
Example: If there were 300 engaged users out of a total of 500 users, then the user engagement rate would be (300/500) x 100 = 60%.
3. Self-Service Rate
Self-service rate is the percentage of customer inquiries that are resolved through self-service within the knowledge base, without the need for interaction with a support agent. A high self-service rate indicates an effective knowledge base that empowers users to find answers on their own, reducing the workload on customer support teams.
Formula: Self-service rate = (Number of self-service transactions / Total number of transactions) x 100
Example: If there were 200 self-service transactions out of a total of 300 transactions, then the self-service rate would be (200/300) x 100 = 66.67%.
4. Customer Satisfaction (CSAT) Score
CSAT score is a widely used metric to measure customer satisfaction with the knowledge base content and overall support experience. Gather feedback through customer surveys to assess the quality of information provided in the knowledge base and identify areas for improvement.
Formula: CSAT score = (Sum of all satisfaction ratings / Total number of responses) x 100
Example: If the sum of all satisfaction ratings was 800 out of a total of 1000 responses, then the CSAT score would be (800/1000) x 100 = 80%.
5. First Contact Resolution (FCR) Rate
FCR rate measures the percentage of customer inquiries that are resolved on the first interaction with the knowledge base. A high FCR rate indicates that the knowledge base is comprehensive and effective in addressing user queries, leading to faster resolution times as well as improved customer satisfaction.
Formula: FCR rate = (Number of cases resolved on first contact / Total number of cases) x 100
Example: If there were 150 cases resolved on first contact out of a total of 200 cases, then the FCR rate would be (150/200) x 100 = 75%.
6. Content Performance
Content performance metrics, such as article views, shares and feedback, provide insights into the effectiveness of individual articles within the knowledge base. Monitor content performance to identify top-performing articles, update outdated content and create new content to fill knowledge gaps.
Formula: Content performance rate = (Number of views or interactions with content / Total number of visitors) x 100
Example: If there were 1000 views on a particular article out of a total of 5000 visitors, then the content performance rate would be (1000/5000) x 100 = 20%.
7. Deflection Rate
Deflection rate is the percentage of customer inquiries that are redirected from live support channels to the knowledge base for self-service resolution. A high deflection rate indicates that the knowledge base is successfully deflecting support tickets, reducing support costs and improving operational efficiency.
Formula: Deflection rate = (Number of deflected cases / Total number of cases) x 100
Example: If there were 50 cases deflected out of a total of 200 cases, then the deflection rate would be (50/200) x 100 = 25%.
Best Practices to Optimize Knowledge Base Performance with Analytics
Analytics can play a key role in optimizing knowledge base performance. This information can then be used to make informed decisions and improvements to enhance the overall user experience.
- Define clear objectives: Before diving into analytics, it is important to establish clear goals and objectives for the knowledge base. Determine what you want to achieve with the knowledge base, whether it is improving customer satisfaction, reducing support tickets, or increasing self-service success rates. These goals will guide your analytics strategy and help you measure success.
- Track useful metrics: Identify key performance indicators (KPIs) that align with your goals. Some common metrics to track include search query data, article views, user engagement, time on page, bounce rates and conversion rates. These metrics help you learn the effectiveness of the strategy.
- Utilize analytics tools: There are many analytics tools available that help you track data related to your knowledge base. Google Analytics, for example, offers valuable insights into user behavior, traffic sources and content performance. Consider integrating analytics tools with your knowledge base platform to streamline data collection and analysis.
- Conduct regular audits: Regularly audit your knowledge base module to ensure it remains accurate, relevant and up-to-date. Use analytics data to identify popular articles, outdated content and areas of user confusion. Make necessary updates and improvements to optimize the knowledge base for users.
- A/B testing: Experiment variations of articles, layouts, formats and search functionality to see what resonates best with users. A/B testing identifies the most effective strategies for improving user engagement. Use analytics data to track the performance of each variation and make data-driven decisions.
- Monitor user feedback: User feedback is a valuable source of information for optimizing knowledge base performance. Monitor user comments, ratings and surveys to gain insights into user preferences, pain points as well as suggestions for improvement. Use this feedback to make informed decisions and prioritize enhancements.
Identify Areas for Improvement with Knowledge Base Analytics
Knowledge base analytics serve as a diagnostic tool that reveals critical gaps in your organization’s information system. By examining user search patterns, content engagement metrics, and feedback data, you can pinpoint where users struggle to find answers or encounter outdated information.
The insights gained from analytics enable strategic improvements that enhance user experience and operational efficiency. You can prioritize content updates, restructure navigation pathways, and identify training needs, ultimately transforming your knowledge base from a static repository into a dynamic, user-centered resource.
Tushar Joshi is a passionate content writer at Omni24, where he transforms complex concepts into clear, engaging and actionable content. With a keen eye for detail and a love for technology, Tushar Joshi crafts blog posts, guides and articles that help readers navigate the fast-evolving world of software solutions.
Latest posts by Tushar Joshi (see all)
- AI Workflow Automation: Benefits, Use Cases & Best Practices - July 23, 2025
- A Comprehensive Guide to Internal Knowledge Base - July 14, 2025
- Why External Knowledge Bases Are Essential for Business Growth - July 8, 2025
FAQs on Knowledge Base Analytics
What is knowledge base data?
Knowledge base data refers to the information stored in a knowledge base. A knowledge base is a centralized repository of knowledge that can be accessed and utilized by individuals within an organization or community. This data can include articles, documents, FAQs, troubleshooting guides and more.
What are the two types of knowledge base?
There are primarily two types of knowledge bases: internal and external. Internal knowledge base articles are used within an organization to store and share information among employees. External knowledge bases are designed for customers or users to access information about a product or service.
What is a knowledge base tool?
A knowledge base tool is a software application designed to create, manage and publish knowledge base content. These tools make it easy for organizations to organize, store and share information effectively. Some popular knowledge base tools include Zendesk, Freshdesk and Confluence.
What is a knowledge-based skill?
A knowledge-based skill refers to an ability or expertise that is based on factual information, concepts, principles, or procedures. These skills are typically acquired through education, training, or experience. Examples of knowledge-based skills include data analysis, programming, project management and medical diagnosis.
