What Are Knowledge Base Analytics & How To Measure Effectively?

Knowledge base analytics comes with 7 specific metrics that, when used correctly, will increase employee productivity as well as overall business growth. Learn how it benefits your business and its best practices.

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?

One efficient way is through knowledge base analytics. It provides a systematic approach to analyzing the vast amount of information stored in a company’s knowledge base.

A decade or two ago, people had to wait for over 30 minutes to connect to customer service. However, now it requires only a couple of clicks to get what you need.

70% of the customers prefer to find answers to their questions on a website rather than a phone call or email.

Let’s leverage this into making a robust knowledge base 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.
Knowledge base analytics helps businesses learn how users are interacting with the information in the knowledge base and identify areas for improvement.

Key Objectives of Knowledge Base Analytics:

Understanding User Behavior
Optimizing Search Functionality
Drive Decision Making
Enhancing Knowledge Sharing

Why Do We Need Knowledge Base Analytics?

A study conducted by Harvard back in 2010 found that simply improving the help section helped reduce the incoming customer service calls by 5%.
Now imagine the drastic change it would do NOW! Knowledge base analytics essentially optimizes customer support, improves content strategy and drives business growth.
Here are a few reasons 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 along with the web saw an increase in website traffic quite immediately. But, how did they come to such a conclusion? They obviously did their math using analytics.
Now, let’s see all the ways companies can gauge their performance using various metrics. Here are seven key metrics used to measure effectiveness in knowledge base analytics:

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.

Here are some best practices to optimize knowledge base performance with analytics:

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

Utilizing knowledge base analytics empowers you to identify areas for improvement in digital marketing strategies. By analyzing data and metrics from customer interactions, businesses can gain valuable insights into what is working well as well as what can be enhanced in your knowledge base management.

This data-driven approach helps businesses make informed decisions on how to optimize their marketing efforts, increase customer satisfaction and ultimately drive more conversions.

In my opinion, implementing knowledge base analytics leads to improved targeting, better customer engagement and overall success in the digital marketing realm. It’s essential for businesses to continuously assess and adjust their strategies based on these insights for long-term success.

Author
Recent Posts

Tushar Joshi

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.