When I started working on the support team at Zendesk three years ago, there were only 13 people on the team. Now, that same team is around 60 advocates strong. In the early days, our support process was simple: Start grabbing tickets from the queue when you get to work on Monday and clear the queue out by happy hour on Friday. As our company and customer base grew, we of course outgrew that simple approach.
We needed to figure out a way to solve more tickets from more customers without hiring at the same rate as our growth in ticket volume. After trying several different approaches and tactics, we settled on a simple philosophy: Work smarter, not harder.
First, we looked at the data behind our support interactions and figured out which types of questions were taking up most of our advocates’ time. Then, we created self-service articles for these problem areas and tracked their effectiveness. Finally, we expanded how we offered support to our customers and kept the most efficient channels.
1. Issue and time tracking
Start systematically tracking the types of issues that are coming in and how long tickets are taking to complete. You can use custom ticket fields in Zendesk to identify common types of issues, and the Time Tracking app will show how long it takes to solve those types of issues.
When setting up your “about” custom ticket field for issue type, be as specific as possible without being too restrictive—try to balance utility (you’ll need to drill into this information later) with efficiency (you don’t want your advocates to spend too much time looking for the right selection).
Once you’ve gathered this data, your “about” field will be available as a ticket attribute in Insights automatically. The data from your Time Tracking app will need to be created as a metric before you can start reporting.
2. Self-service and ticket deflection
One of the most important things to watch as your company and your support volume grows is ticket deflection. This is especially hard to track since by definition, ticket deflection means tickets are not being submitted to your team (and how do you measure something that’s not happening?).
The key here is a well-stocked Help Center, full of articles on all the most common issues that your customers have. The more often your customers can find their own answers and resolve their own issues, the better. Be sure to keep supporting those articles by updating the content and keeping the conversation going in the comments section.
Self-service dashboards or even Google Analytics can tell you how many views your Help Center articles are receiving. When I was an advocate here at Zendesk, our rule of thumb was every question publicly answered in an article was worth 10 privately answered tickets. You can track overall effectiveness of your self-service articles and how you’re doing on ticket deflection by comparing the number of article views to the number of submitted tickets (aiming, of course, for a very high ratio of views to tickets).
3. Multichannel support
If your ticket volume from email or Web forms is increasing, try adding additional channels, like social media or chat support. Chat in particular can be an effective way to resolve inquiries with greater efficiency. For example, our studies show that live chat can resolve customer issues seven times faster than email. And when you dig into the data, you’ll uncover ways to make it even more efficient as you scale.
With Zopim Chat for example, you can get insights into the times when volume is high or wait and response times are long, so you can make staffing changes to handle the influx of inquiries to prevent customers from getting frustrated and submitting tickets or turning to social media.
Among other things, Zopim Analytics also helps you identify which of your agents are the best performers in chat, so you can get the right people working the right channels.
Data and analytics are powerful allies to companies that are growing and changing. In addition to the three examples here, there are dozens of other insights you can gain by mining your customer service data to uncover the ways your organization can work smarter.