With your Help Center being such a core component of your overall customer experience, it is important you put in place a robust set of metrics to track the performance of your Help Center and to help identify the areas where you can provide your customers with improved self-service support.
Why is a Help Center important
Providing your customers with a high-quality Help Center, one that is up-to-date and easy for your customers to navigate when searching for the answers they need, is one of the most crucial elements of your customer support experience.
In the past, companies often viewed the Help Center, and other self-service support offerings, from a cost reduction perspective and a way to streamline support operations. However, this perspective has shifted dramatically with customers increasingly using self-service as the first point of contact with your support organization and, oftentimes, your company. Increasingly companies are placing a greater emphasis on offering their customers self-service support solutions that allow them to resolve their own issues.
And the reason for this is simple: it’s what they want. Customers overwhelmingly prefer self-service support options, with Harvard Business Review reporting 81% of customers, across all industries, attempt to resolve issues themselves before reaching out to your customer support team.
As the emphasis on self-service support has increased, and customers have been provided with more avenues to support themselves, the core component of self-service support has remained consistent. The Help Center remains the cornerstone of self-service support with a study conducted by Forrester indicating it is the preferred self-service channel for customers
A high-quality Help Center will empower your customers to successfully find the answers they need to resolve their issues without contacting your customer support team and will positively impact some of your most important business drivers. It will lead to increased customer loyalty by reducing the effort your customers exert in resolving their issues, it will improve your overall customer experience as well as allowing you to reduce costs by providing customer support at scale.
Measuring the success of your Help Center
With your Help Center being such a core component of your overall customer experience, it is important you put in place a robust set of metrics to track the performance of your Help Center and to help identify the areas where you can provide your customers with improved self-service support. This blog post will highlight the following core metrics you can use to measure the success of your Help Center:
- Page Views
- Unique Users
- Average Time on Page
- Average Duration per Session
Content relevance metrics
- Pages per session
- Bounce Rate
- Bounce rate + avg. time on page
Self-service and ticket deflection metrics
- Self-service score
- Article view to support ticket ratio
- Ticket deflection
- Identify search terms and phrases
- Failed searches
- No follow-up action
- Searches that lead to a support ticket
- Search to support ticket ratio
One of the easiest ways to get started collecting data on your Help Center, and to gain insight into how your customers are interacting with it, is to use your Web Analytics platform. One of the most common Web Analytics platforms companies use to monitor their Help Center is Google Analytics, a free service which tracks valuable information on customer usage.
Even a basic implementation of Google Analytics, or your other Web Analytics platforms, will provide you with visibility on how your customers are using your Help Center with insight into areas like:
- The number of times your customers interact with your Help Center;
- Which help articles your customers view the most;
- The amount of time your customers spend on different help articles; and
- The pages in your Help Center which customers leave from.
This will help you monitor trends across your Help Center, determine how your most important help articles are performing and allow you to measure whether the initiatives you implement to improve your Help Center are moving the needle for your customers.
Measure customer interactions with your Help Center
Use the following metrics to measure how many interactions your customers are having with your Help Center to gain insight into how many of your customers are trying to self-serve and the most common issues they are trying to resolve.
Your page views metric measures the number of times a particular page on your Help Center has been visited. For example, this can be used to measure the number of page views of a specific help article or the number of page views on the homepage of your Help Center. Your page views metrics provides useful information on whether your customers are easily able to find, and use, your Help Center and provides insight into the help articles, and category of articles, your customers are using most to find answers.
Unique users is the number of times a page on your Help Center has been visited by a unique customer and allows you to measure the portion of your customer base that are actually using your Help Center to self-serve. When using the unique users metric to understand customer interactions with your Help Center, it is important to differentiate between a gated Help Center which sits behind a customer login and a publicly available Help Center which is visible to anyone on the web. With a gated Help Center you can directly equate a unique user to a customer whereas, with a publicly available Help Center, a unique user may not exclusively be a customer but rather could also be a search traffic to your Help Center or a lead looking for information before signing up.
Your sessions metric measures how many times a customer visits any of the pages on your Help Center which includes a single customer visiting several pages in your Help Center during the same consecutive time period (considered to be one session). Using your sessions metric alongside your unique users metrics provides useful information on how often your customers are using your Help Center. Further, you can also segment these metrics by whether these sessions are being generated by new customers or existing customers to determine whether you should focus your help content in a manner that would be more useful for newer customers or existing customers on your platform.
How engaging is your Help Center content
As well as measuring how often your customers are interacting with your Help Center, it is also important to measure how engaged your customers are with your Help Center content.
Average time on page
This metric measures the average amount of time your customers are spending on any particular help article, or homepage, and provides useful information to better understand the level of engagement on these pages. There are no strict benchmarks when using average time on page to understand help center engagement however, as a general reference, a lower average time on a help article would indicate your customers are not finding the content particularly useful, while a higher average time on page would provide an indication of a more engaging help article.
Average duration per session
Average duration per session measures the average amount of your time your customers spend on your Help Center during any one session and is another important metric to understand how your help content is resonating with your customers.
An important way to make your help articles more interesting and engaging for your customers is to include product screenshots and other product visuals. Your content only has a few seconds to capture the attention of your customers, and visual cues like product screenshots can make a big difference in improving your average time on page and average duration per session.
How relevant is your Help Center content
In addition to tracking customer interaction and engagement, measuring bounce rate metrics and the number of pages your customers view per session can provide another layer of valuable information on how relevant your help center content is for your customers.
Pages per session
The pages per session metric is used to measure the number of help articles your customers visit in any single session on your Help Center which can give you useful insight on how well your content is connecting with your customers. For example, a customer viewing multiple help articles in one session would suggest they have not been successful in easily finding the answers they are looking for and have had to navigate to multiple pages to find a solution. Or, even worse, did not ultimately find what they were looking for.
Your bounce rate is measured when a customer leaves, or bounces, from the first page they visit in your Help Center without engaging with the page. Measured as a percentage, it shows the number of single-page sessions your customers experience when visiting your Help Center.
It is important to note, a high bounce rate can actually be indicative of a successful Help Center experience for your customers suggesting they were able to resolve their issue and stopped browsing. In other words, they found the content relevant. However, conversely, a high bounce rate on your Help Center homepage may imply a negative experience for your customers. This would suggest they were unable to find a help article relevant to the problem they were facing.
Bounce rate + Average time on page
One way to get a deeper understanding of the relevance of your Help Center content is to combine both your bounce rate and average time on page metrics.
For example, if one of your help articles has a high bounce rate as well as a higher average time on page, this would suggest your customers have engaged with the content in the article (higher time on page) and were able to find the answer to their problem and bounced from the page.
Including product screenshots is not only crucial for increasing the level of customer engagement with your help articles, but also the relevance of your help articles. The product screenshots in your help articles is the most immediate way your customers identify what an article is trying to help them resolve. If your product screenshots are fully up-to-date, and exactly match the visuals your customer sees on their screens, it will build trust as your customers will easily be able to see that they are in the right place to resolve their issue.
Self-service and ticket deflection metrics
Another important way to gain valuable insight into the performance of your Help Center is to combine your interaction metrics with the support ticket data from your Help Desk Ticket System which measures the interactions between your customers and your customer support team.
Your self-service score metric compares the number of customers who visit your Help Center (i.e. unique users) against the total number of customers who submitted support tickets through your ticketing system.
Self-service score = Total unique Help Center visitors / Total customers submitting tickets
Your self-service score is represented as a ratio (i.e. for every customer who submits a ticket, X finds what they are looking for), and tracks whether your customers have been able to successfully resolve their own issues by using your Help Center rather than reaching out to your customer support team directly. Zendesk published a Benchmark Report stating the average self-service scores for companies using their platform 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
This ratio measures the number of help articles your customers view (i.e. page views) against the number of inbound tickets that are submitted to your customer support team.
Article view to support ticket ratio = Total Help Center views / Total tickets submitted
Your article view to support ticket ratio is a key metric used to track the health of your Help Center as a self-service tool. Increasing this ratio would indicate that more customers are prioritizing self-service, and are successfully able to self-service, by using your Help Center rather than reaching out to customer support.
Ticket deflection is one of the most important metrics companies use to measure the performance of their Help Center. Although it can be difficult to track, and the way it is measured often varies from company to company, it is an important way to measure the impact of your self-service support on your customers.
Ticket deflection: is the number of support tickets which could have been resolved self-service through your Help Center.
A common method used by companies to help more accurately track ticket deflection is to add tags to every macro in their ticketing system that answers a question which could have been resolved by one of their help articles rather than a ticket to customer support. Others have the customer support agents manually mark the inbound tickets which could have been resolved by a help article to calculate ticket deflection.
Tracking this number over time provides a leading indicator of fluctuations in the performance of your self-service support. In addition, ticket deflection tracking can also be used to identify which of your macros are being used the most and prompt a review of those help articles related to these macros to ensure they are fully up-to-date or to make these articles more visible in your Help Center.
A great way to identify gaps in your help documentation, and areas to improve your overall Help Center, is to track the words or phrases your customers use when searching on your Help Center. Most leading Help Center platforms have in-built reporting and analytics functionality which allows you to easily track these words or phrases. Most companies also use their Web Analytics platform (e.g. Google Analytics) to provide an additional layer of understanding into your customer search patterns and to supplement the reporting directly from your Help Center software.
Identify search terms and phrases
You can use your Web Analytics patterns to review and analyze your customers search patterns and the different key terms or phrases they use when searching for answers on your Help Center. You can also analyze the other pages your customers view in their Help Center journey and the time they spend on your Help Center following a particular search.
This provides valuable information on the different issues which are bringing your customers to your Help Center as well as allowing you to more intimately understand the terminology your customers use to communicate their issues. Analyzing and understanding this allows you to tailor your content to be more relevant and better suit your customers.
Understanding what your customers are looking for, and how they describe those issues, is a powerful tool to improve your existing help content. However, it is also important to review the search results from the searches your customers make to identify areas where you might have content caps and to ensure you are highlighting the right content for your customers to resolve their problems.
By analyzing those searches that return no search results, or are failed searches, you will gain valuable insight into the issues your customers are facing but that they are unable to find answers for. Searches that return no results may indicate that the terminology your customers use to describe an issue may differ to how you describe it as a support organization. It may also indicate the tagging of your help articles or help center navigation needs updating. Or it may even indicate that a help article does not exist for this particular issue and there is a content gap in your Help Center.
No follow-up action
Another useful way to analyze the searches your customers make is to review the searches that result in nothing being clicked from the list of help articles the search provides. This provides useful information on the performance of the search functionality in your Help Center and helps to assess whether you are surfacing content that will help your customers address their issues.
Searches that lead to a support ticket
Tracking the number of searches that ultimately results in your customers creating a support ticket is another useful way to assess the overall performance of your Help Center.
Search to support ticket ratio = Total Help Center searches / Total tickets created after a Help Center search
This is the most direct indication you get on whether your Help Center was successful in providing your customers with the information they needed to resolve their own issues. If your customers are unable to find the help they need by searching your Help Center and are required to contact your customer 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, measuring your search to support ticket ratio is another great way to better understand the overall health of your Help Center. It compares how often your customers search for answers in your Help Center against creating a support ticket.
Search to support ticket ratio = Total number of Help Center searches / Total number of support tickets
If your customers are able to search your Help Center to find the answers they need more often than they are reaching out to your customer support team and creating tickets, this positively indicates that your Help Center is allowing your customers to self-serve.