5 trends to uncover in your customer service data

A whole lot of data comes out of support interactions, enough so that astute support leaders can pull insights, discover trends, and improve how their business serves customers. While the data may only suggest what’s going on with your customers and agents, it’s important to be able to spot trends in your customer service analytics quickly.

Here are five telltale signs that there’s a trend involving your customers and the effectiveness of your customer support:

Changes in your customers’ expectations

Metrics to watch: CSAT, spikes in self-service resolutions

Customer satisfaction (CSAT) scores indicate more than just a gratifying support interaction – it’s metric that can indicate if customers are happy with the entire business.

If overall CSAT is decreasing, it’s a good practice to look deeper and find out why. You could send out a follow-up survey, or even analyze tickets that received a poor satisfaction score to try and find similarities between them (look out for slow resolution times or too many escalations). Tickets with low satisfaction provide great insight into opportunities to make process adjustments or provide additional training to your team.

Pay attention to spikes in self-service resolutions as well. If multiple customers are finding the assistance they need in a specific help article (or one is frequently recommended by an AI assistant like Answer Bot), it could indicate a point of interest amongst your customers. That information could be passed along to a product team for future improvements, or it might inspire a campaign to drive more awareness towards how the feature works.

Your customers’ channel preferences

Metrics to watch: volumes by channel, self-service resolutions, ticket deflection rate

If you offer multiple support channels, knowing which channels your customers gravitate towards could inform your support optimization strategies.

By looking at ticket volumes by channel, you can see how many tickets are coming in through each one. That could give support leaders a better sense of how to staff agents on those channels or which channels customers should be guided towards. For example, a customer may prefer to live chat with someone when they’re looking to make a purchase or urgently need support. That option could be surfaced to customers on your pricing page or in their shopping cart.

Since many customers prefer to self-serve before reaching out to an agent, take note of how frequently self-service resolutions occur. It may be worthwhile to optimize those options. Measuring the success of self-service involves finding your ticket deflection ratio by dividing the total number of users of you help center by the total number of submitted tickets. Pay close attention to article views and comments as well—those numbers will indicate if the help guide is being used.

When your customers want to seek help

Metrics to watch: ticket volume spikes

Spikes in ticket volume reveal when your customers prefer to use your products, which often correlates to when they seek customer support. Look for patterns and cross-check them with other factors, such as an uncommon event like a service outage. If spikes are occurring without unusual circumstances, it’s likely that your customers prefer to seek help around those times.

Knowing when your customers often seek help can inform proactive strategies as well—if you’re looking to launch a new product, take a look back at your last product launch and review the ticket spikes from it. It could provide insight into how customers respond to a new product: what they ask about, when they ask, etc. This can better inform a team of agents on what to look out for.

The complexity of your customers’ requests

Metrics to watch: Full resolution time, ticket reopens, first-contact resolution, routing

Time-to-resolution highlights how long it takes for your support organization to resolve a ticket—longer times will be indicative of more complexity in your customers’ issues. Ticket reopens and routing details could suggest that tickets are being moved around between support agents (customers with difficult issues often require more than one agent’s help).

First-contact resolution (FCR) is a metric that may suggest complexity, but be careful. A high number of FCRs could suggest that the tickets aren’t receiving the effort they require. This is especially true if there are a high number of ticket reopens as it suggests that resolution speed is being valued over support quality.

How effectively agents are managing requests

Metrics to watch: CSAT, reply time metrics, resolution time metrics, ticket backlog, open tickets by agent

If agents are having trouble managing incoming requests, it doesn’t bode well for a quality customer experience. CSAT can indicate that customers are properly supported, but also keep an eye on time-based metrics like reply time and resolution time. Higher numbers in those categories could mean that agents have their hands full.

A clear sign of how well a team manages customer requests is the ticket backlog. An overstuffed ticket backlog will happen from time to time, but it shouldn’t happen too often (they can be alleviated with a coordinated, team-based “ticket smash”). A metric detailing open tickets by agent can highlight how individual agents are managing requests—too many could suggest that the agent has too much on their plate.