Customer Service Analytics

An Education Hub for Support 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

CS Analytics Quick Tips

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 👇

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Customer service analytics tweet
Customer service analytics tweet
Customer service analytics tweet
Customer service analytics tweet
Customer service analytics tweet
Customer service analytics tweet
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Essential Guides

What is Customer Service Analytics?

Definition

Customer service analytics is the process of uncovering valuable customer insights from interactions between customer and agents in the support department.

Whether you manually listen to calls or read through chat logs, or leverage smarter techniques like topic and sentiment analysis, your support conversations are a rich source of customer insight.

An effective customer service analytics process provides root cause insights, continuously and in as close to real-time as possible. The faster teams across a company can understand and react to the flow of customer insight, the more able they are to make better decisions that drive positive results.

Customer service analytics is fundamental to building a customer-centric culture. In our work with support leaders, we see that strategically distributed customer service insights has a large impact on the standing of customer service in the company. It turns from a cost-centre to a powerhouse for change.

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 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 your shortcut to a customer-centric culture that invests in experience.

Three Customer Service Analytics Benefits:

There are so many benefits to build a customer service analytics machine. They span 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 of you can prioritise 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.

Read more benefits in the article below.

Call Centre Analytics Use Cases

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.

1

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.

2

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.

3

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?

4

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.

5

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

6

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.

Essential Reads

Check out these articles and podcasts to deep dive into the power of customer support ticket insights for your business.

Actionable Guides

Customer support ticket categorization 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.

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.

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.

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.

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 Product at SentiSum

The Role of AI in Customer Service Analytics

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.

Everything you need to know about AI in customer service analytics

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.

Why is machine learning NLP so good at Zendesk support ticket categorization?

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.

Is customer service analytics software worth the investment?

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.

Frequently Asked

FAQs About Customer Support Analytics

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.

1

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.

2

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.

3

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.

4

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.

5

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.