The insights hidden away in customer support conversations are often overlooked by companies.
Despite their detailed granularity and enviable volumes, these valuable insights get trapped by data silos, limited tools, and a lack of awareness.
However, we're now seeing organizations recognize their untapped potential in unparalleled numbers.
Advanced analytics tools, like AI and natural language processing, now enable the efficient extraction of insights from vast customer support data lakes.
Since we started creating technology in this industry in 2017, there is a night and day difference when looking at how many companies are investing in support data-driven decision-making.
The new field of Customer Service Analytics is now core part to the competitive strategy of a growing number of technology companies, and we expect the laggards to soon follow.
In this guide, you'll find:
In this section, we'll focus on the essentials. You'll learn the definition of CSA, how to get started with tagging, how to do root cause analyses and how to use tags to drive business outcomes.
The analysis of support conversations has many use cases far beyond the support department. Your insights can contribute to customer research, CX improvements, and time-saving processes company-wide.
Let's review six customer service analytics use cases (we find these a useful way to get buy-in for investing in support analytics):
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.
Check out these articles and podcasts to deep dive into the power of customer support ticket insights for your business.
In this section, we'll help you take action. You'll learn how to get started with tagging, how to do root cause analyses, and how to drive business outcomes with ticket tags.
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
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
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
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
“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
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 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
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
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.