Customer service analytics is a rapidly developing field.
Where support conversations were once concealed in vast log histories and siloed help centre platforms, advanced technology has their insights easily accessible.
Where support departments were once sidelined for investment, C-Suite executives are realizing the potential of conversation and survey insights for improving decision-making and customer experience projects across the company.
This guide will help you kickstart and improve your customer service analytics (CSA) capabilities. If you're looking to learn about the ROI, benefits and use cases of CSA, or to understand exactly how to get granular, root cause level insights in real-time, you're in the right place.
👇Here are nine customer service analytics tips to get you started 👇
The analysis of customer service conversations has numerous beneficial uses far beyond only yourself and your team. Those insights can contribute to customer clarity, CX improvements, and time-saving processes company-wide.
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 about 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.
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.
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.
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.
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.
“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 Product at SentiSum
AI continues to play a growing role in customer service. 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 ensure analytics are 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.
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 the detailed topics even when spelling mistakes and convoluted sentence structures are present.
Companies like Zendesk or MonkeyLearn use only the most basic AI analytics techniques, so their analytics capabilities produce poor quality insights. Whereas SentiSum's platform uses NLP.
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; and 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.
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 prioritisation 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 the example of customer analytics? A great example comes from British unicorn, Gousto. They do customer support analytics to get customer insights which 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.