<- Customer Service Analytics Guide

Importance of Customer Service Analytics in 2024

Sharad Khandelwal
SentiSum CEO & Customer Service Expert
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Support conversations are a unique combination of easily available, highly valuable and yet...widely underutilised.

In many sizeable B2C & B2B brands, support tickets flood in continuously high volumes creating a large database of voice of customer data. 

Whilst overwhelming for many, if harnessed quickly they become a real-time finger-on-the-pulse of the customer’s experience.

With a quality tagging taxonomy, the customer insight hidden away in the conversations opens the door for a wide range of use cases that prioritise experience and create efficiencies for support teams.

“Talking to customers face to face, en masse, every day is unique to customer service. That close contact is an untapped and unappreciated opportunity to build strong customer relationships, increase loyalty, hear customer feedback, improve products, build community, upsell, increase basket size, and so on.”—Sharad Khandelwal, CEO at SentiSum

So, why do most brands ignore one of their most valuable customer experience assets?

While many companies still do not value their customer’s voice, those that do tend to find customer service analytics difficult. The sheer volume of conversations is overwhelming and the complex nature of voice calls and free-text (i.e. every customer describes things differently) makes it hard to draw out trends in the data with automation.

Further to this, customer service has been historically under-appreciated. Many simply don’t make the link between customer support and the increasingly valuable role the department plays in customer experience.

With the arrival of artificial intelligence-driven customer service analytics[ADD LINK], it’s now easy for support leadership to derive accurate, real-time insights from every support conversation.

Choosing to leverage those insights in creative ways to support improvements and optimisations is one way I see customer support winning the respect it deserves.

Contents:
  • 4 Ways to Use Customer Service Analytics
  • Importance of customer service analytics…comes down to your tagging capabilities

4 Ways to Use Customer Service Analytics That Are Important to Your Company & Customer

Customer service analytics can support company growth in several ways. One of the most important use cases I see every day is tackling friction points in the customer journey that drive customer churn.

1. Share Insights Company-Wide to Improve Customer Experience

If properly analysed, support conversations are an easy way to identify high priority issues for customers.

In a recent interview, Gousto, the British meal-kit delivery company that delivers 8 million meals to customer’s monthly, revealed how they use AI to analyse their support conversations to help identify CX issues to fix.

They didn’t want to lose their understanding of the customer as they scaled fast, so Gousto invested in text analytics to derive insights from the hundreds of thousands of conversations they had with customers each month.

Gousto had 9 customer contact channels in total. They brought them together into one analytics dashboard that would enable a unified understanding of customer issues.

“We use the latest technology to give open access to our voice of the customer data across the business for teams to self-serve insights for anything from discovery work for Tech initiatives through to root cause analysis for any operational complaints to guide improvements.” —Joe Quinlivan, Head of Customer Service at Gousto

By breaking down their tens of thousands of monthly conversations, the Gousto team identified trend topics and repeat issues that would become the basis of new CX improvements projects.

For example, Gousto identified that their #1 reason for contact was a particular food item was arriving damaged. By quantifying the impact on customers, Gousto showed the logistics department the evidence and they addressed the issue quickly.

Gousto is not alone in using support conversations to improve customer experience—many of our customers invest in AI-based tagging for that purpose.

Further examples come from companies like Vinted and Loom who revealed on the Support Insights podcast that they’ve built processes to feed qualitative customer service analytics directly into the product development cycle. 

2. Provide better customer service

Customer service is an important bottleneck for customer experience: when anything goes wrong in other touchpoints, the peeved customer funnels toward your department.

It is, therefore, critical that the customer service experience is on point.

Customer service analytics can help with that in a couple of ways. For example, conversation topic tagging analytics can help automate parts of the customer service journey—ensuring the customer gets better, faster service.

James Villas, a travel brand which owns more than 3,000 hotel properties, reduced first reply time by a whopping 46% and overall resolution time by 51% with topic tag-based triage. (Here’s the interview with their Head of Digital Transformation).

Real-time customer service analytics was able to identify urgent tickets amongst the high daily volumes, ensuring customers in dire need were prioritised in the queue. The results speak for themselves, as James Villas’ CSAT increased by 11%.

There are many more interesting and creative use cases we’ve come across with our clients. For example, an increasing number of companies want to leverage AI conversation analytics to help train their service agents.

By running sentiment analysis across CSAT feedback and agent-customer conversations, we’re now able to identify the behaviours and language used that leads to poor customer satisfaction. 

3. Tackle rising customer service costs

Having a clear understanding of why customers are contacting customer service, and the issues that are trending up, is a necessary step towards reducing support ticket volume and their associated costs.

This was exactly how this leading e-commerce tyre retailer used their service analytics capabilities.

“Topic volume analysis allowed the retailer to quantify the problem and its cost to their business. Armed with quantitative evidence, the contact centre manager was able to direct the web team to prioritise fixing the booking page without the effort of manual analysis.”—From the case study here.

In short, your analytics capabilities are what helps build evidence and drive the change needed to reduce ticket volume.

In our podcast episode with Loom’s customer service leadership team, we discussed several other ways to reduce support ticket volume.

Over the pandemic, Loom saw their product usership explode past 10 million (from 700k). At that time their contact rate was 4%, which meant huge growth in their support ticket volumes each month.

By analysing their support tickets, the Loom team identified the most frequent topics driving contact and built a targeted knowledge-base to preemptively answer those common customer queries.

Using tactics like these—better for both the user and the company—Loom brought their contact rate down to 1%. Saving the company millions in the process.

4. Drive actual revenue outcomes

Perhaps one of the most important use cases of customer service analytics is fuelling support-driven growth.

In their podcast episode with us, the ethical clothing brand, Organic Basics, gave us a brilliant example of doing just that. The customer service team ran an in-depth analytics project and, as a result, built a revenue-focused ticket prioritisation algorithm.

Organic Basics’ analysis revealed that some customers, at specific points in time, would approach customer service to ask questions right at the moment before making a purchase. By auto-prioritising customer tickets at that moment in the buying funnel, they could help those ‘leads’ quickly before their attention turned elsewhere—increasing sales.

Our AI-based topic analysis takes this one step further by prioritisation support conversations and tickets based on importance, profitability or ‘risky’ words and phrases.

Learn more: 9 Ways to Properly Prioritise Customer Service Requests

Importance of customer service analytics…comes down to your tagging capabilities

These examples and use cases show that customer service analytics is clearly a valuable investment. 

A word of caution, however. The impact your analytics has is dependent on the speed and quality you can do it. When your monthly customer service conversations drift into the thousands—both speed and quality become difficult to achieve.

Manual or semi-automated conversation tagging is one option for getting regular insights—although, results are regularly inaccurate or too surface-level to open up the full number of use cases possible.

At SentiSum, we build AI-based analytics engines for support teams like Gousto, Hopin, Nestlé and HotJar. They chose SentiSum because of the accurate and detailed ticket tagging, real-time sentiment analysis and time-saving automations.

Book a product tour with us here to see how we can help you.

Importance of Customer Service Analytics FAQs

What is customer service analytics?

Customer service analytics is the process of analysing your customer support calls, texts, chats and emails to discover valuable customer insights. Customer experiences converge at the customer service touchpoint, making it an abundant place to understand CX issues faced in other departments. Customer service analytics brings those issues to the surface in a fast and reliable way—usually led by data tagging to get quantitative insights from qualitative data sources.

What to do with customer service analytics?

Customer service analytics has many valuable use cases. The most obvious is to understand customer sentiment on different issues, which can help identify which touchpoints and experiences need improving if you want to tackle churn and inspire more customer loyalty.

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Customer Sentiment Analysis FAQs

How is sentiment analysis useful? 5 examples of customer sentiment analysis

Here are 5 ways sentiment analysis is useful in customer service:

Prioritize customer issues: Sentiment analysis can help businesses quickly identify and prioritize customer issues based on the emotional tone of their messages. This can enable customer service agents to respond promptly to unhappy customers and resolve issues before they escalate.

‍Personalize customer interactions: By detecting the emotional tone of a customer's message, sentiment analysis can help businesses tailor their responses to the customer's needs. For example, if a customer is expressing frustration, a customer service agent can respond with empathy and offer a solution to address the issue.

‍Improve customer experience: By providing personalized and efficient customer service, sentiment analysis can help improve the overall customer experience. Customers who receive prompt and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

‍Analyze customer feedback: Sentiment analysis can be used to analyze large volumes of customer feedback to identify trends and patterns. This can help businesses identify areas for improvement and make data-driven decisions to improve their products and services.

‍Monitor brand reputation: Sentiment analysis can be used to monitor online mentions of a brand or product to detect negative sentiment and address issues before they become a larger problem. This can help businesses protect their brand reputation and maintain customer loyalty.

What is real time sentiment analysis in customer service?

Real-time sentiment analysis in customer service refers to the process of analyzing the emotional tone of customer messages or conversations as they are happening, in real-time. This enables businesses to quickly identify and respond to customer issues, prioritize certain conversations, and personalize interactions based on the customer's emotional state.Here are some examples and analogies to help understand real-time sentiment analysis in customer service:

‍Real-time monitoring: Real-time sentiment analysis involves monitoring customer messages or conversations as they are happening, in real-time. This is similar to a security guard monitoring a building in real-time for any signs of danger or security threats. Just as the security guard can quickly respond to any threats they detect, businesses can quickly respond to customer issues as they are identified.

‍Prompt customer service: Real-time sentiment analysis allows businesses to quickly identify and respond to customer issues before they become larger problems. For example, if a customer is expressing frustration about a product issue, real-time sentiment analysis can alert customer service agents to prioritize that customer's message for a quick response. This can help the business resolve the issue before it leads to a negative online review or loss of customers.

‍Personalized interactions: Real-time sentiment analysis can help businesses personalize their interactions with customers based on their emotional state. For example, if a customer is expressing happiness about a recent purchase, a customer service agent can respond with enthusiasm and congratulations. Conversely, if a customer is expressing frustration or anger, a customer service agent can respond with empathy and an apology. This personalized approach can help businesses build stronger relationships with their customers.

‍Improved customer experience: Real-time sentiment analysis can help improve the overall customer experience by providing prompt and effective customer service. Customers who receive quick and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

‍Continuous monitoring: Real-time sentiment analysis can be used to continuously monitor customer messages or conversations, providing businesses with a wealth of data that can be used to improve their products and services. For example, if customers are expressing negative sentiment about a particular product feature, a business can use that information to make improvements and better meet the needs of its customers.

Overall, real-time sentiment analysis is a valuable tool in customer service that can help businesses quickly respond to customer issues, personalize interactions, and improve the overall customer experience.

What type of information do companies analyze when conducting sentiment analysis?

Here are the two overarching areas of customer information you can include in your sentiment analysis:

‍Text data: Sentiment analysis of text data is like analyzing a written letter to detect the writer's emotional tone. By detecting the emotional tone of customer feedback, customer service chats, reviews, or social media posts, companies can gain valuable insights into how their customers feel about their products or services.

‍Voice data: Sentiment analysis of voice data is like interpreting a person's tone of voice during a conversation to detect their emotional state. By analyzing phone calls or video chats with customers, companies can detect the emotional cues in a customer's tone of voice, such as frustration or anger, and provide a more personalized response.

What are the main goals of sentiment analysis?

The main goals of sentiment analysis are to gain insights into customer emotions and opinions, and to use these insights to improve customer satisfaction and loyalty. Here are some examples of the main goals of sentiment analysis:

‍Understand customer feedback: One of the main goals of sentiment analysis is to understand customer feedback and opinions about a product, service, or brand. By analyzing the emotional tone of customer feedback, companies can gain insights into what customers like and dislike about their products or services, and make improvements accordingly.

‍Improve customer experience: Another goal of sentiment analysis is to improve the overall customer experience. By understanding customer emotions and opinions, companies can address any issues or pain points and provide a better customer experience. For example, if sentiment analysis reveals that customers are frequently complaining about long wait times, the company can take steps to reduce the wait times and improve the customer experience.

‍Enhance customer engagement: Sentiment analysis can also be used to enhance customer engagement by identifying opportunities for positive interactions with customers. For example, if sentiment analysis reveals that customers are expressing positive emotions towards a new product or service, the company can engage with those customers to learn more about what they like and how they can improve the product or service even further.

‍Prevent negative customer experiences: Another goal of sentiment analysis is to prevent negative customer experiences by identifying potential issues and addressing them proactively. For example, if sentiment analysis reveals that customers are frequently complaining about a specific product feature, the company can address the issue before it becomes a bigger problem and affects customer satisfaction.

‍Monitor brand reputation: Sentiment analysis can also be used to monitor brand reputation by tracking what customers are saying about a brand, product or service on social media, review sites, and other online platforms. This information can be used to prevent a potential PR crisis and maintain a positive brand reputation.

Want to learn more about how SentiSum automates your customer sentiment analysis? Book a meeting with our team here.

← Customer Service Analytics Guide

Importance of Customer Service Analytics in 2024

Sharad Khandelwal
Sharad Khandelwal
CEO & Co-founder at SentiSum, Expert in AI Analytics

Support conversations are a unique combination of easily available, highly valuable and yet...widely underutilised.

In many sizeable B2C & B2B brands, support tickets flood in continuously high volumes creating a large database of voice of customer data. 

Whilst overwhelming for many, if harnessed quickly they become a real-time finger-on-the-pulse of the customer’s experience.

With a quality tagging taxonomy, the customer insight hidden away in the conversations opens the door for a wide range of use cases that prioritise experience and create efficiencies for support teams.

“Talking to customers face to face, en masse, every day is unique to customer service. That close contact is an untapped and unappreciated opportunity to build strong customer relationships, increase loyalty, hear customer feedback, improve products, build community, upsell, increase basket size, and so on.”—Sharad Khandelwal, CEO at SentiSum

So, why do most brands ignore one of their most valuable customer experience assets?

While many companies still do not value their customer’s voice, those that do tend to find customer service analytics difficult. The sheer volume of conversations is overwhelming and the complex nature of voice calls and free-text (i.e. every customer describes things differently) makes it hard to draw out trends in the data with automation.

Further to this, customer service has been historically under-appreciated. Many simply don’t make the link between customer support and the increasingly valuable role the department plays in customer experience.

With the arrival of artificial intelligence-driven customer service analytics[ADD LINK], it’s now easy for support leadership to derive accurate, real-time insights from every support conversation.

Choosing to leverage those insights in creative ways to support improvements and optimisations is one way I see customer support winning the respect it deserves.

Contents:
  • 4 Ways to Use Customer Service Analytics
  • Importance of customer service analytics…comes down to your tagging capabilities

4 Ways to Use Customer Service Analytics That Are Important to Your Company & Customer

Customer service analytics can support company growth in several ways. One of the most important use cases I see every day is tackling friction points in the customer journey that drive customer churn.

1. Share Insights Company-Wide to Improve Customer Experience

If properly analysed, support conversations are an easy way to identify high priority issues for customers.

In a recent interview, Gousto, the British meal-kit delivery company that delivers 8 million meals to customer’s monthly, revealed how they use AI to analyse their support conversations to help identify CX issues to fix.

They didn’t want to lose their understanding of the customer as they scaled fast, so Gousto invested in text analytics to derive insights from the hundreds of thousands of conversations they had with customers each month.

Gousto had 9 customer contact channels in total. They brought them together into one analytics dashboard that would enable a unified understanding of customer issues.

“We use the latest technology to give open access to our voice of the customer data across the business for teams to self-serve insights for anything from discovery work for Tech initiatives through to root cause analysis for any operational complaints to guide improvements.” —Joe Quinlivan, Head of Customer Service at Gousto

By breaking down their tens of thousands of monthly conversations, the Gousto team identified trend topics and repeat issues that would become the basis of new CX improvements projects.

For example, Gousto identified that their #1 reason for contact was a particular food item was arriving damaged. By quantifying the impact on customers, Gousto showed the logistics department the evidence and they addressed the issue quickly.

Gousto is not alone in using support conversations to improve customer experience—many of our customers invest in AI-based tagging for that purpose.

Further examples come from companies like Vinted and Loom who revealed on the Support Insights podcast that they’ve built processes to feed qualitative customer service analytics directly into the product development cycle. 

2. Provide better customer service

Customer service is an important bottleneck for customer experience: when anything goes wrong in other touchpoints, the peeved customer funnels toward your department.

It is, therefore, critical that the customer service experience is on point.

Customer service analytics can help with that in a couple of ways. For example, conversation topic tagging analytics can help automate parts of the customer service journey—ensuring the customer gets better, faster service.

James Villas, a travel brand which owns more than 3,000 hotel properties, reduced first reply time by a whopping 46% and overall resolution time by 51% with topic tag-based triage. (Here’s the interview with their Head of Digital Transformation).

Real-time customer service analytics was able to identify urgent tickets amongst the high daily volumes, ensuring customers in dire need were prioritised in the queue. The results speak for themselves, as James Villas’ CSAT increased by 11%.

There are many more interesting and creative use cases we’ve come across with our clients. For example, an increasing number of companies want to leverage AI conversation analytics to help train their service agents.

By running sentiment analysis across CSAT feedback and agent-customer conversations, we’re now able to identify the behaviours and language used that leads to poor customer satisfaction. 

3. Tackle rising customer service costs

Having a clear understanding of why customers are contacting customer service, and the issues that are trending up, is a necessary step towards reducing support ticket volume and their associated costs.

This was exactly how this leading e-commerce tyre retailer used their service analytics capabilities.

“Topic volume analysis allowed the retailer to quantify the problem and its cost to their business. Armed with quantitative evidence, the contact centre manager was able to direct the web team to prioritise fixing the booking page without the effort of manual analysis.”—From the case study here.

In short, your analytics capabilities are what helps build evidence and drive the change needed to reduce ticket volume.

In our podcast episode with Loom’s customer service leadership team, we discussed several other ways to reduce support ticket volume.

Over the pandemic, Loom saw their product usership explode past 10 million (from 700k). At that time their contact rate was 4%, which meant huge growth in their support ticket volumes each month.

By analysing their support tickets, the Loom team identified the most frequent topics driving contact and built a targeted knowledge-base to preemptively answer those common customer queries.

Using tactics like these—better for both the user and the company—Loom brought their contact rate down to 1%. Saving the company millions in the process.

4. Drive actual revenue outcomes

Perhaps one of the most important use cases of customer service analytics is fuelling support-driven growth.

In their podcast episode with us, the ethical clothing brand, Organic Basics, gave us a brilliant example of doing just that. The customer service team ran an in-depth analytics project and, as a result, built a revenue-focused ticket prioritisation algorithm.

Organic Basics’ analysis revealed that some customers, at specific points in time, would approach customer service to ask questions right at the moment before making a purchase. By auto-prioritising customer tickets at that moment in the buying funnel, they could help those ‘leads’ quickly before their attention turned elsewhere—increasing sales.

Our AI-based topic analysis takes this one step further by prioritisation support conversations and tickets based on importance, profitability or ‘risky’ words and phrases.

Learn more: 9 Ways to Properly Prioritise Customer Service Requests

Importance of customer service analytics…comes down to your tagging capabilities

These examples and use cases show that customer service analytics is clearly a valuable investment. 

A word of caution, however. The impact your analytics has is dependent on the speed and quality you can do it. When your monthly customer service conversations drift into the thousands—both speed and quality become difficult to achieve.

Manual or semi-automated conversation tagging is one option for getting regular insights—although, results are regularly inaccurate or too surface-level to open up the full number of use cases possible.

At SentiSum, we build AI-based analytics engines for support teams like Gousto, Hopin, Nestlé and HotJar. They chose SentiSum because of the accurate and detailed ticket tagging, real-time sentiment analysis and time-saving automations.

Book a product tour with us here to see how we can help you.

Importance of Customer Service Analytics FAQs

What is customer service analytics?

Customer service analytics is the process of analysing your customer support calls, texts, chats and emails to discover valuable customer insights. Customer experiences converge at the customer service touchpoint, making it an abundant place to understand CX issues faced in other departments. Customer service analytics brings those issues to the surface in a fast and reliable way—usually led by data tagging to get quantitative insights from qualitative data sources.

What to do with customer service analytics?

Customer service analytics has many valuable use cases. The most obvious is to understand customer sentiment on different issues, which can help identify which touchpoints and experiences need improving if you want to tackle churn and inspire more customer loyalty.

Join a community of 2139+ customer-focused professionals and receive bi-weekly articles, podcasts, webinars, and more!