Why is a knowledge base important?
A good Knowledge Base will empower your customers to successfully find answers to their questions without contacting your customer support team which will result in: Increased customer loyalty by reducing the effort required by your customers to find the help they need, improved customer experience, and reduced support costs.
The emphasis on providing self-service support solutions has been growing for many years. Customers are increasingly using self-service as the first point of contact with your Support organization and, oftentimes, your company. Support organizations continue to invest into the self-service options available to customers, yet the Knowledge Base remains the cornerstone of self-service support with a study conducted by Forrester indicating it is the preferred self-service channel for customers.
Given your Knowledge Base is a crucial piece of your overall customer experience, it is important you have in place a robust set of metrics to track how well your Knowledge base is performing and to help identify areas where you can deliver an improved self-service experience. In this blog post we will highlight a few of the metrics you should track to measure the success of your Knowledge Base, organized into the following three groups: Page View metrics, Self-Service and Ticket Deflection Metrics, and Search Metrics.
What are the benefits of a knowledge base?
This article will describe 16 KPIs for you to use when evaluating the performance of your Knowledge Base:
What metrics should I start with?
A great way to start collecting data and understanding how your customers are interacting with your Knowledge Base is to track it using your Web Analytics platform (e.g. Google Analytics).
Even basic instrumentation of a Web Analytics platform on your Knowledge Base will provide visibility into things like:
How many interactions your customers have with your Knowledge Base;
Which articles get the most views;
How long your customers are spending on each article; and
Where people bounce on your Knowledge Base
Monitor trends to help determine which articles perform better than others and whether the updates you make to your Knowledge Base move the needle for your customers.
Knowledge Base Interaction Metrics
The following metrics allow you to see how many interactions your customers are having with your Knowledge Base and provide useful information on how many of your customers are trying to self-serve:
Page views: is the number of times a page on your Knowledge Base has been visited (e.g the views of a specific article or your Knowledge Base homepage). This metric provides useful information on whether your customers are easily able to find your Knowledge Base and on which articles, and category of articles, your customers are viewing most.
Unique users: is the number of times a page on your Knowledge Base has been visited by a unique user and helps you understand how many of your customers are actually using your Knowledge Base to self-serve. It is worth noting, if you run a gated Knowledge Base which sits behind a login, you can directly infer that a unique user equals a customer. However, if you run a public Knowledge Base (e.g. help.company.com), visible on the web, a unique user could also be a lead, a trial user or other search traffic to your public Knowledge Base. So a unique user may not equate directly to a customer on your gated Knowledge Base.
Sessions: is the number of times a user visits any page/s on your Knowledge Base (n.b. a customer visiting multiple pages over the same consecutive period of time is considered to be one session). You can compare your sessions metric against unique users to gain some insight into how often your customers are using your Knowledge Base. You can also look at the % of sessions from new customers versus returning customers to get a better understanding of whether you should tailor your content more towards your new customers or your more tenured customers.
Knowledge Base Engagement Metrics
You can also expand on your page view metrics to get an indication of the level of engagement your customers have with the content in your Knowledge Base:
Average Time on Page
Avg. time on page: is the average amount of time your customers spend on each particular article and can give you an indication on whether they engaged with your content or not. For example, if the amount of time spent on an article is very low, this could suggest your customers were unsatisfied with the content, whereas a higher amount of time spent on the article could indicate your customers more fully engaged with the content.
Average Duration per Session
Avg. duration per session: is the average amount of time your customers spend on your Knowledge Base for each individual session and, similarly to the average time on page, can give you a good indication of whether your customers are engaging with your content and finding what they need.
Adding product screenshots to your content, and other Knowledge Base visuals, is one important way to make your articles more interesting and engaging, and to increase both your average time on page and average session duration. Your content only has a few seconds to make an impression on your customers, and visual cues can make a big difference. Additionally, product screenshots also help you quickly convey to your customers what each article is helping them resolve, and allows them to immediately return to the category page should the visual cues not indicate they have landed on the right article. A good example of this kind of software is LaunchBrightly.
Knowledge Base Relevance Metrics
As well as understanding how often your customers interact with your Knowledge Base and how engaged they are with the content, tracking bounce rate metrics and pages per session can provide additional insight on how relevant your customers are finding your content.
Pages per Session
Pages per session: measures the number of articles your customers view in a single session on your Knowledge Base and provides useful information on whether your customers are finding your content useful or not. Customers viewing many articles in a single session would tend to suggest they had some difficulty or, even worse, were unsuccessful in finding what they were looking for.
Bounce rate: is measured when your customer leaves the first page on their Knowledge Base journey without engaging with another page. It is the percentage of single-page sessions your customers experience on your Knowledge Base.
A high bounce rate can be an indicator of success on a Knowledge Base article implying your customer has found the answer and stopped browsing (i.e. the content was relevant)
Alternatively, a high bounce rate on your category or section pages may negatively suggest your customer was unable to find an article related to the issue they were trying to resolve.
Bounce rate + Average time on page
Bounce rate + avg. time on page: is a measure that compares two metrics described above to get a more complete understanding of the relevance of your Knowledge Base content. For example, a Knowledge Base article with a high bounce rate and higher time spent on the page would indicate your customers have engaged with the content on the page and found the answer they were looking for.
Self-service and ticket deflection metrics
In addition to providing useful insight into how customers are interacting with your Knowledge Base as standalone metrics, the above page view metrics can also be compared against the support ticket data from your Help Desk Ticketing System (e.g. Zendesk, Intercom, Zoho, Help Scout) to get a better overall understanding of how your Knowledge Base is performing.
Your self-service score measures the number of customers who access your Knowledge Base against the total number of customers who submitted support tickets through your ticketing system.
Self-service score = Total unique Knowledge Base visitors / Total customers submitting tickets
It describes how often your customers are looking to self-serve and access your Knowledge Base rather than contacting your Support organization. Zendesk’s Benchmark Report indicates the average self-service score for companies using Zendesk is 4.1, meaning that for every four customers attempting to self-serve, one submitted a customer support ticket.
Article view to support ticket ratio
The article view to support ticket ratio measures how often your customers view an article on your Knowledge Base against the number of inbound tickets submitted to your Support organization:
Article view to support ticket ratio = Total Knowledge Base views / Total tickets submitted
Tracking this ratio gives you an indication of how well your Knowledge Base is performing in allowing your customers to self-serve and in deflecting incoming tickets from your Support organization. Increasing this ratio shows that customers are prioritizing self-service through your Knowledge Base rather than contacting Support, and that your Knowledge Base is more successfully allowing them to resolve their issues through self-service.
Your ticket deflection can be difficult to track, and how ticket deflection is measured can vary from company to company, but it is another important metric to better understand the performance of your Knowledge Base and the impact of self-service support on your customers.
Ticket deflection: is the number of support tickets which could have been resolved self-service through your Knowledge Base.
One method many companies use to track their ticket deflection is to add a tag to every macro, or prepared response, in their ticketing system which could have been answered by one of their Knowledge Base articles instead of through a support ticket. Or, alternatively, other companies have their Support agents mark the tickets which could have been deflected to calculate the overall ticket deflection.
You naturally want to keep this number as low as possible, which shows your customers are prioritizing self-service support, and can track this over time to get an early indication on any fluctuations in performance. If you want to improve this metric week-over-week, or month-over-month, your best bet is to design this as a ratio so your numbers are not susceptible to volatility in support ticket volume. Additionally, you can also use your ticket deflection as an absolute number to track which macros, or prepared responses, are getting used most frequently and look to refresh the article content or make these articles more visible in your Knowledge Base.
Including product screenshots in your Knowledge Base articles is an essential part of providing an enhanced self-service experience for your customers. These product images allow your customers to digest your Knowledge Base content in a more simplified way which keeps them more engaged, increases the likelihood of them finding answers from within the article and, therefore, will help reduce your number of support tickets (i.e. improving ticket deflection).
Furthermore, by adding image titles and alt text to your product screenshots, you can also improve the search performance of your Knowledge Base articles.
Another great way to get useful insight into how your customers are interacting with your Knowledge base, and to identify content gaps and ways to improve your content, is to understand the words or phrases your customers search on your Knowledge Base.
Depending on which platform you use to run your Knowledge base, most will have reporting and analytics capabilities that allow you to track the key terms and phrases your customers are searching for. Additionally, Google Analytics is another useful tool you can use to track site search patterns or to supplement the reporting directly from your Help Desk software.
By reviewing the search terms from your Knowledge Base you can see a list of the keywords and phrases your customers search for when trying to find answers to resolve their issues, the number of other Knowledge Base pages they look at after the search and the time they spend on your Knowledge Base following the search.
This is a great way to understand the issues that are bringing customers to your Knowledge Base, but it also allows you to better understand the terminology your customers use to describe the issues they are facing and allows you to tailor your content to be more relevant for your customers.
In addition to understanding what your customers are looking for, you can also review your customers search results to identify content gaps in your Knowledge Base and assess how well you are surfacing the right content for your customers to resolve their issues.
Analyzing the searches that return no search results (or failed searches) provides valuable insight into the issues your customers are facing but are unable to find a relevant answer for. This may indicate your customers are using different terminology to describe their problems, the tagging and navigation of your content needs updating or even that a Knowledge Base article does not exist for this particular problem.
You can also analyze the searches your customers are making that result in nothing being clicked from the list of content provided. This is a great way to assess the performance of your Knowledge Base search functionality to ensure you are surfacing the most relevant content to help your customers resolve their own issues without them needing to contact your Support team.
Count of searches leading to a support ticket
Another great way to assess the performance of your Knowledge Base is to track the number of searches that lead to a support ticket to see how often a support ticket is created after your customer searches for help on your Knowledge Base.
Searches that lead to a support ticket ratio = Total number of Knowledge Base searches / Total number of tickets created following a Knowledge Base search
This gives you a direct indication of whether your customers were able to find what they were looking for to self-serve and resolve their issue on their own. If your customers are unable to find the help they need by searching your Knowledge Base and are required to contact your Support team, this increases the effort needed to resolve their issue and can negatively impact your customer loyalty.
Search to support ticket ratio
In addition to your self-service score and ticket deflection metrics, another metric that helps you understand the overall impact of your self-service support is your search to support ticket ratio. This measures how often your customers are searching for answers on your Knowledge Base compared to creating a support ticket:
Search to support ticket ratio = Total number of Knowledge Base searches / Total number of support tickets
If your customers are searching, and finding answers, on your Knowledge Base more frequently than creating support tickets and contacting your Support team, this is a good indication your Knowledge Base is helping your customers self-serve.
About the author
Josh Peacock is the COO and Co-founder at LaunchBrightly, an automated product screenshot platform. He has worked in the startup game for 15+ years and, like most entrepreneurs, has worn many hats including filling the role of the primary Customer Support person and running the company Knowledge Base. Having taken, quite literally, thousands of manual product screenshots and manual updates to help articles, he is super passionate about solving the manual screenshot chore.