Customer service analytics is the process of measuring and tracking all aspects of your customer service experience. This data plays a vital role in helping you understand and improve your customer service performance as well as showcasing the value of your department.
Let’s be honest: when it comes to judging the performance of your customer service team, only one opinion counts – that of your customers.
96% of customers say they would leave a company because of bad customer service. On the other hand, 94% of consumers would recommend a brand after a great experience.
To consistently deliver the outstanding service that customers demand, businesses need to track customer service analytics. The data found here will allow you to spot issues, make improvements, and increase brand loyalty and retention as a result.
In this article, we’ll explain everything you need to know about customer service analytics, and how to monitor and improve the performance of your customer service team.
Customer service analytics use cases
Tracking your customer service analytics can help you in many different ways.
Here are some of the benefits of using customer service analytics.
Understand the customer journey
Analyzing customer service data gives you a clearer picture of the customer journey.
Customers encounter problems at different stages of the journey, such as the discovery phase, after purchase, or after onboarding. Your data can tell you where customers are on the journey when they need your assistance.
As you map out the customer journey, you’ll see where problems often arise. Then you can reduce friction from the customer journey, and make the process smoother for customers. Remember, effortless experiences are a key part of building customer loyalty.
Identify pain points
It’s likely that many of your customers have similar pain points or problems while using your products.
Customer service analytics data can tell you the most common types of questions that customers ask your support team.
Once you identify these common issues, you can solve them faster. Maybe that means giving clearer instructions during onboarding, expanding the information in your knowledge base, or even making changes to your product.
Over time, you’ll figure out what customers need, faster. You can provide this to them without them needing to go through a lengthy back-and-forth.
Get instant feedback
Customer service analytics gives you real-time feedback from customers. For example, you could ask customers to rate their experience immediately after an interaction with your support team.
You can address problems before they escalate and lead to bad reviews or lost customers.
Measure satisfaction
By looking at the big picture and spotting trends in your data, you can measure the overall satisfaction of your customers.
You’ll know if the results are below average and you’ll be able to find ways to improve customer satisfaction, increasing your retention rate and loyalty.
It’s important to assess your team’s performance in a fair and objective way, and data gives you the insights you need to do this.
You’ll know which team members are underperforming, so you can step in to coach them and bring them up to snuff. Similarly, you can recognize and reward the hard work of your top-performing agents. This boosts morale and gives everyone an incentive to do better.
Reduce support costs
Data may even help you reduce support costs.
When you know what customers struggle with, your team can provide assistance faster.
You can improve the product or service to address the most common concerns, or make more information available through your customer-facing knowledge base, reducing the number of contacts.
Customer service success metrics
There are two types of customer service success metrics that you can collect: quantitative and qualitative.
Quantitative metrics can be measured in numbers, minutes, or another concrete measurement. They can also be averages or percentages.
Number of new customer requests per day and hourly productivity are examples of quantitative metrics.
Qualitative metrics provide more detailed insights into what’s going on with your customer support and the quality of service you deliver.
They include the content of customer support requests and responses to open-ended questions in surveys, as well as customer satisfaction, first contact resolution rate, and customer effort score.
Though qualitative metrics can be more difficult to analyze, we’ll show you how you can use customer service software like Dixa to extract actionable insights from customer answers using AI.
Now we’ll look at some of the most important customer service analytics metrics to track.
There are lots of metrics that can measure the performance of your customer service team.
Here are some examples:
Average response time/response time bands
Customers don’t want to be kept waiting. It’s important to measure how long it takes your agents to respond to a customer’s request.
This can be measured in minutes or grouped into response time bands, which show the percentage of customer queries that get replies within certain timeframes (under 15 minutes, 15-30 minutes, 30-60 minutes, etc.).
This metric shows the percentage of queries resolved in a single interaction.
If customers are getting their questions answered the first time around, this shows that your agents are solving their problems efficiently.
Average query resolution time
It’s important for customer service managers to know how long it takes the team to resolve individual customer questions, on average.
This gives a benchmark and helps you reduce the time it takes to resolve issues for customers.
Total number of conversations
If you’re receiving a very high number of customer queries, this could indicate that your product is confusing, or that explanatory information isn’t easy to find.
This data can give context to the other metrics that you track. It will also tell you how much pressure your customer support team is under.
Helpfulness of knowledge base articles or chatbot responses
At the bottom of help center articles, include a button where customers can rate how helpful they found the resource.
This could either be on a scale of 1-3, or “Was this article helpful” with simple yes or no options.
You can do the same when customers interact with a chatbot on your site.
Customer retention rate
We know that the quality of customer service has a big impact on your retention rate.
If your customer retention is low, you may be able to fix this by improving other customer support metrics.
How to measure customer experience with customer service analytics
Next, we’ll look at metrics that can help you see things from your customer’s perspective.
Here are a few metrics to measure customer experience.
Customer Satisfaction Score (CSAT)
To find out how satisfied your customers are with the help and support they receive, ask them to fill out a survey and rate their experiences from 1-5.
Make these surveys short and simple so more customers complete them. The more customers take part, the more accurate this metric will be.
After a customer service interaction, you can ask something like:
“How satisfied are you with your experience today? Please rate your experience on a scale of 1-5 with 1 being awful, and 5 being excellent.”
Customer Effort Score (CES)
Customer effort scores indicate how difficult it is for customers to get their problems solved. If your support process requires too much customer effort, this can cause frustration.
To measure this, you can ask customers to rank how easy it was to get help from your support team.
Long waiting times or having to speak to multiple agents can lead to a high-effort experience that works against loyalty-building efforts.
NPS measures how likely customers are to recommend your product to other people. If customers are willing to recommend your product, this shows that they are loyal to your brand and you have the potential for growth.
You can divide responses into three segments: Promoters, Passives, and Detractors.
NPS surveys may also include an element of qualitative data. You can ask your customer base to provide a reason for the score.
How to get the most out of your customer service analytics
The best way to measure customer service analytics, and make data-backed decisions, is to use an AI-driven analytics solution. Look for an AI tool that gives you autonomy, doesn’t depend on any third party to get accurate information, and gives you the right information, at the right time, allowing you to make smarter decisions.
Not only will you be able to collect and track quantitative data, but an AI tool can also help you extract valuable insights from qualitative data. Instead of having to review survey answers and conversations manually, AI can scan responses for keywords and categorize them for you.
With Dixa, you can track a wide range of quantitative metrics including those mentioned above. You can also dive deep into customer behavior analysis, looking at responses and conversations with support to understand how your customers feel.
For example, sentiment scoring reports analyze customer language to identify emotions and common themes, for example, frustration, anger, or positive emotions.
Our AI categorizes your support conversations by topic, so you’ll know which issues are most common among your customers. Working with Dixa is like having your own virtual data scientist, someone who can help managers make fairer decisions, driven by data. To see how we can help your team, book a demo.