Customer Service Analytics Guide

A Hub for Customer Service Leaders

💡 Written by Sharad Khandelwal, CEO of SentiSum, Analytics Expert

💡 Fact checked by Kirsty Pinner, Director of Product & Customer at SentiSum

A cute picture of people working together to help the customer


Customer service analytics (CSA) is a rapidly developing field.

Support conversations were once siloed in help center platforms, and overlooked as a source of insight. Now, advanced technology has made their insights easily accessible.

Support departments were once sidelined for investment, and overlooked as a key source of customer growth. Now, top companies have realized the department's strategic position in retention and customer experience.

This guide will help you kickstart and improve your customer CSA capabilities.

You'll learn:

  • Best practices for support ticket analysis
  • The different use cases of CSA in your business
  • The role of AI in analytics & real-time insights
Essential Guides

In this section, we'll focus on the basics. You'll learn the definition how to get started with tagging, how to do root cause analyses and how to use tags to drive business outcomes.

What is Customer Service Analytics?


Customer service analytics (CSA) is the process of extracting valuable insights from customer conversations using accurate, detailed tagging.

Support conversations are a rich source of customer insight. Extracting insights from them should be a priority for any customer service team looking to deeply understand customer sentiment and drive CX improvements.

Support conversations are unique, they're frequent, usually conducted in the moment an issue occurs, and the customer comes to you. By nature they avoid the complexity and biases that usually appear in surveys.

Support teams looking to scale efficiently should also consider customer service analytics. Accurate tags are a key part of automating time-consuming tasks like routing, agent feedback and prioritization.

CSA also has wider business implications. Whether it's support improvement and optimization in operations, or providing marketing campaign ideas, we see that strategically distributed support insights are extremely valuable across the business.

Why is Customer Support Analytics Important for Your Company?

Companies that listen and act on customer feedback set themselves up for healthy, sustainable, fast growth.

In 2021, Fred Reichheld from Bain & Co introduced a new concept to the voice of customer world: Earned Growth.

Earned growth is a metric that measures the quality of your growth. In short, it answers the question: how much of your growth is coming from existing customers, retention, and organic referrals?

It's self-evident that companies with great customer experiences have more earned growth, and, therefore, have to spend less on new customer acquisition in order to grow.

Your customer support is at the centre of creating great customer experiences. Customer issues flow in on such a regular basis, in such high quantities, making it the ideal place to collect feedback, use it to iterate, and improve the entire business.

Customer service analytics is your shortcut to a customer-centric culture that invests in experience.

Three Powerful Customer Service Analytics Benefits

There are so many benefits to building a customer service analytics machine. They span your customer, company, and employees.

Here are just three:

1. Improve customer experience: Bring all your customer contact channels together into one text analytics-driven platform, and you'll know what's driving customer contact and in what quantity. That knowledge is useful for improving customer-centric decision-making across departments.

2. Provide better customer service: The tags that underpin your customer service analytics have other uses. For example, James Villas uses them to identify urgent tickets and triage them to a priority queue.

3. Tackle rising customer service costs: By understanding the key drivers conversations, you can prioritize new projects to reduce ticket volume.

Whether that's investing in key knowledge base articles or setting up auto-replies to easy-to-tackle topics, you'll be able to reduce the resources required to handle rising contacts.

Six Valuable Use Cases

The analysis of support conversations has many use cases far beyond only yourself and your team. Those insights can contribute to customer perception research, CX improvements, and time-saving processes company-wide.

Let's review six customer service analytics use cases:


Reduce lead time on projects. Let's say your operations team is working on improving a product delivery process. Your analytics can reduce the research time by showing where the largest issues are for customers.


Auto-prioritize urgent tickets. With a custom real-time tagging system in place, you can set macros that flag priority requests at key moments in the customer journey.


Build a knowledge base. Wondering what content can help improve the customer service experience? Your reason for contact analytics is a great place to start. Which issues are high frequency, high frustration, and easy to tackle?


Triage tickets to expert agents. Your analytics can identify characteristics of incoming support requests (like topic, complexity, or language) and route them to the best agent for the job.


Improve CSAT. Through sentiment and topic analytics, you can get to the heart of what's driving customer satisfaction—and improve it continuously.


Drive revenue. Some customers are further down the sales funnel than others—for example, those that opened your newsletter or visited your website multiple times. Real-time analytics can identify these customers so you can get help to them fast.

Actionable Guides

In this section, we'll focus on action. You'll learn how to get started with tagging, how to do root cause analyses and how to use tags to drive business outcomes.

Support Ticket Category Best Practices

Your ticket categorization taxonomy determines the insights you get out of your analytics process. Your categories could produce high-level or root-cause insights across any topic you want to track.

There are eight best practices for ticket categorization in the article below.

The most important? Identify your goals upfront and include teams across your company in that process—or, risk the results being ignored.

💡 Deep dive this topic: Best Practices for Conversation Categorization

How to Do a Root Cause Analysis in Customer Service

What's really at the root of a piece of customer feedback?

When you have 10s of thousands of support conversations each month, it can seem like a mind-numbing task to understand all their issues.

Your customer is an expert in their experience, but your team is the expert at creating innovative solutions. We recommend listening and understanding your customers with a root cause analysis, and going from there to improve.

💡 Deep dive this topic: How to Do a Root Cause Analysis in Customer Service

How to Manually Analyze Customer Support Tickets

To conduct a manual support ticket analysis visit the link below and download our Excel template (no email required).

From there, you can build a taxonomy according to your goals, tag a sample of support tickets, identify patterns and share the results.

This process, of course, becomes unmanageable at any amount of scale. That being said, it's a useful exercise to understand what an artificial intelligence tool (the best for this job at scale) is automating for you.

💡 Deep dive this topic: How to Manually Analyze Support Tickets

How to Setup Your Customer-Focused Ticket Triage Process

Imagine if during the COVID-19 pandemic when airline customers were facing cancellations... the resulting customer tickets were triaged to a dedicated team equipped to quickly reduce alarm and provide a path forward.

Rather than urgent, fearful customer contacts that were lost in the mix of non-time-sensitive requests, automated triage would have quickly and dramatically improved the customer service experience.

This is the power of leveraging ticket triage.

💡 Deep dive this topic: Ticket triage: How to Reduce Reply Time

Artifical Intelligence in CSA

“AI can cut through the subjectivity of human opinion, and no matter how something is said, it can report on the customer issue in a simple way. The latest developments in AI analytics can handle complexity extremely well.”

“No other method gives a representation of customer conversations this accurately. Manual tagging is too subjective and keyword analysis is too blunt a tool.” —Kirsty Pinner, VP of Product at SentiSum

The Role of AI in Customer Service Analytics

AI continues to play a growing role in customer service. The team's on the cutting edge of innovation, typically in businesses with a data and software-first mindset, leverage AI to automate repetitive processes and drive better, faster decision-making.

Artificial intelligence plays a key role in analytics. The technology is brilliant when it comes to consuming large quantities of unstructured text and speech and categorizing it accurately.

This handy function of the latest AI algorithms ensures analytics is fast, accurate, and thorough.

For those wanting an objective understanding of why customers are contacting them, and what experiences are driving negative sentiment, AI is an ally to keep close.

💡 Deep dive this topic: Power of AI for Customer Service Analytics

Machine Learning NLP: Why's it so Good at Ticket Tagging?

Machine learning NLP produces the most accurate and granular insights from support conversations.

It does not rely on keywords or keyword libraries, instead, it understands sentences and phrases in the same way a human does.

It could quickly identify detailed topics even when spelling mistakes and convoluted sentence structures are present.

💡 Deep dive this topic: Why Machine-Learning is So Great for Zendesk

Is Customer Service Analytics Software Worth the Money?

Companies with large volumes of monthly customer conversations will see a significant return on their investment.

Customer service analytics software will provide accurate tags that uncover insights about customer experience and enable automated processes. The return is two-fold: improved loyalty and retention; time saved on CX project lead time; and less wasted support agent time.

Read more on ROI here or review the top CS analytics tools on the link below.

💡 Deep dive this topic: 5 Best Customer Service Analytics Tools & Softwares

Frequently Asked

Customer Support Analytics FAQs

Choosing a tool is the most difficult step to take. We suggest gathering a small team and first determining what you want the final results of your analysis to look like.


What is SentiSum? SentiSum plugs into your help desk to automatically uncover reasons for contact, customer sentiment, and key CX drivers in real-time. The platform also enables accurate auto-triage and prioritization based on any criteria.


How do you do customer analytics? The best customer analytics is done using advanced techniques like machine learning-based NLP. To have an impact, your customer insights should be both root cause level and widely easy to access.


Why is customer support data analyzed? Customer support is the front line of interaction between customer and company. The conversations hold a wealth of insights that are powerful for company-wide improvement.


What is an example of customer analytics? A great example comes from British unicorn, Gousto. They do customer support analytics to get customer insights that support the success of ongoing projects across the company, from operations to marketing.


How do I ensure other teams use my insights? To ensure insights are used, (1) understand the immediate priorities of other departments, and (2) send them insights that help with those. You'll show the value without causing friction, and are likely to see the relationship reverse in the future—they'll come to you.

Got questions? Reach out to me at to chat.