Imagine it’s the end of a crazy support season. Your agents are absolutely bushed; some have had their confidence shaken and their egos bruised. It was a challenge for everyone involved, and you, the support manager, feel like your customer service team has been through hell and back. But it’s nearly time to rally the squad once again – how can you make sure your team is better prepared for the next time support requests get out of hand?
You can seek out all the context you’ll need by strategically answering this question: What happened?
Truly great customer service is proactive, meaning that agents need to be set up for success if they’re to succeed. There are multiple types of customer analytics that helps support teams stay proactive, but for proper preparations and clarity, there needs to be strategies developed from descriptive analytics.
What are descriptive analytics?
Descriptive analytics might be seen as the “standard” type of analytics, or what’s most often associated with the term “analytics”. This kind of data provides an insight into the past; it’s a report of something that has already occurred. It could be data indicative of website traffic, previous quarter revenue, or the calories that you ate in a day – all of these count as descriptive analytics.
So what makes these helpful? Well those kinds of “historical insights” can become benchmarks for a goal, and they can show the progress being made towards that goal. From there, strategies can be developed or adjusted depending on the data. It’s a typical business standard and one of the best approaches we have towards fully realising, and accomplishing, our goals.
What do descriptive analytics tell you in customer service?
Who your customers are
Support interactions shine a whole other light onto customer behaviours. If you really want a clue into who your audience is, take a look at the number of tickets based on a particular topic – it will highlight where your customers are coming from based on locations, industries, and interests (depending on how your product is used). This can lead to a very critical discovery: the expectations of your customers.
Perhaps you’ll discover that your customers aren’t as technically-savvy as you had hoped, resulting in more work for your agents. Or maybe there are certain parts of the day that customers like to use your product, leading to an influx of support requests during that time. That can influence adequate agent headcount and how a manger should set business hours.
If there’s something in particular that you want to know about your customers, you can create custom fields that add various descriptors to a support ticket. These fields can provide additional insights, like if a segment of your customer base comes from a specific industry and share similar issues, agents can be better trained for those problems when they arise.
What your customer’s journey is like
But knowing who your customers are is only part of it – it’s necessary to understand the journey that brought them to an agent. This means knowing if the customer’s new to or experienced with the product, if they attempted to fix the issue themselves, or if whatever’s plaguing them is a recurring problem. Tracking this sort of data means strategically labelling tickets or utilising additional applications. One such tool is Pathfinder, which shows which web pages, Help Center articles, and community posts that customers have already viewed or searched for.
Insight into the customer journey is how customer service gets really optimised. Managers are able to tell when recurrent issues are becoming something a product team should handle, or if the support team should enhance on their self-service offerings. It can also influence your support tiers for customer’s that are on different parts of their journey.
How to better prepare for when things get crazy
Descriptive analytics can be extremely valuable when you-know-what hits the fan for customer service. We’re talking holiday rushes, new product launches, critical system failures (for which there isn’t any established protocol), or anything that can trigger an influx of support tickets that makes agents curse under their breath.
For those type of events, be sure that custom fields are being used to track issue types (which is really as simple as including an “About” field). You’ll also want to measure ticket volumes, first reply times, and resolution times. Those descriptive analytics provide a benchmark for catastrophic events and can influence your preparations should they occur again. Don’t forget to try a few fire drills to make sure the team knows what to do.
Other ways to stay proactive: dig into the issues that had the longest response times during difficult events. See if there’s anything that can be learned to shorten down your team’s response time. Also, be on the lookout for repetitive tickets – these indicate an opportunity to enhance your self-service by improving Help Center articles or utilising an AI-powered helper like Answer Bot.