Good knowledge management requires good data, including how well (or poorly) content is helping to prevent cases. Traditionally, help centers are stockpiled with a lot of great information. Self-help articles. Video tutorials. FAQs. But they are often not “sticky,” and if a customer cannot easily find what they need, they will submit a support ticket and connect with a support team. Such communication channels are expensive to maintain and, according to the Harvard Business Review, customers “are four times more likely to leave a service interaction disloyal than loyal.”

Assessing the ROI of KM for Ticket Deflection

Evaluate the potential ROI for contextual help on the ticket submission form

One way to achieve this is by implementing a ticket deflection workflow that surfaces relevant information to customers in real-time while they are filling out a support ticket. This gives customers a last-ditch opportunity to see relevant articles they can use to self-help before they have to deal with a support agent. It also tees things up so you can measure ticket deflection in Google Analytics.

Here’s how it looks for Avalara, a MindTouch customer. You can see in the screenshot that, after the user types in a search term—”install,” in this example—their system automatically serves up suggested articles that might solve the issue.

Image of Avalara ticket deflection in action, powered by MindTouch

So, how does this all work? Let’s say you’re a customer of Avalara who has come to their site for support. You’ve scanned through the help center for information, but can’t find the exact information that you need. So, you give up and start filling out a support ticket. While you type into the text boxes, your self-service software (in this case, MindTouch) mines the entire site for relevant articles and displays them on the same page, in real time. More likely than not, you will be able to find the content that you need and avoid submitting the ticket.

With a solution like this in place, you can now measure ticket deflection in Google Analytics. Although this method is not perfect, it is ideal for businesses that are looking to take the first steps in measuring ticket deflection.

To start, you need to log into Google Analytics. In the left navigation bar under Site Content, select All Pages. Then, dig into the data by clicking Navigation Summary.

Image of how to measure ticket deflection in Google Analytics

How to measure ticket deflection rate

Focus on the flow from the initial ticketing landing page to all of your knowledge base content. The initial ticketing landing page is found under Current Selection, named /ticket/. The knowledge content is selectively listed under Next Page Path, such as “helparticle1” and “helparticle2.” To calculate the deflection rate, sum the total # of page views for all “next page path” knowledge articles and divide it by the total number of page views of the initial ticket landing page – “/ticket/.”

Ticket Deflection Rate = (Sum total # of page views for all “next page path” knowledge articles) / (Total # of page views of the initial support ticket landing page)

The deflection rate can be calculated this way because the self-service software in use—MindTouch, in this example—surfaces knowledge articles in real-time, on the same page, while the customer is filling out the initial support ticket. Deflection occurs when the “next page path” is a help article. Failure to deflect occurs when a support ticket is submitted and, consequently, the “next page path” is the “thanks4submittingaticket” page. Any customer that does not submit a support ticket or convert to a help article is considered part of the bounce rate.

This is just one simple example. If you’re not doing so already, you should consider tracking your ticket deflection rates. This is not just a good way to help decrease churn. It’s about driving customer success by creating a better self-service experience. It’s about finding which articles your customers are deflecting to, which ones are failing to answer a customer’s questions, and getting actionable data so that you can make improvements. We recommend you track these trends over time so that you can compare these metrics against other customer support KPIs like customer effort score (CES), customer satisfaction (CSAT), and Net Promoter Score® (NPS).

Additional categories: Customer Experience, Knowledge Management