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.
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:
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:
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:
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%.
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%.
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%.
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%.
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%.
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%.
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%.
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:
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.
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.
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