Customer service data can be a lot, and it’s crucial to segment the helpful information from the junk to analyse it.  

If there’s one department in an organisation that holds the key to improved customer experience, it’s customer service. With customer service across organisations generating vast volumes of data, it’s no wonder that businesses are prioritising analysing this data for key insights into customer behaviour, preferences, and so much more.  

The key benefit of customer service analytics, then, is to sift through this sea of data and separate the wheat from the chaff. Customer support software is increasingly enabling businesses to navigate the ever-changing nature of customer journeys and improve the overall customer experience they offer.  

If your organisation is collecting such vast amounts of data and you want to capitalise on it to gain actionable insights, this guide is for you. 

We take you through the various use cases for customer service analytics, the metrics to measure, and some of the best tools you can consider integrating into your tech stack. However, we’ll first look at the different types of customer service analytics.  

Team-Analytics- customer service analysis

Types of Customer Service Analytics  

While customer service analytics is a broader term, there are several specific types based on the information or analysis they provide and the data they need to offer them. These are briefly explained below.  

  • Prescriptive analytics: Prescriptive analyses present recommendations to solve customer service issues.  
  • Predictive analytics: Predictive analyses forecasts customer behaviour based on historical patterns or trends. These prove to be helpful to mitigate risks, particularly in the financial sector.  
  • Descriptive analytics: Descriptive analyses summarise historical trends to enable businesses to decide on a future plan of action. 
  • Customer journey analytics: Customer journey analyses give businesses a bird’s eye view of opportunities to engage and generate revenue in the overall customer journey.  
  • Diagnostic analytics: Diagnostic analytics analyses customer data to identify problems or challenges. It can be particularly helpful after a product or service is launched or updated.  
  • Customer experience analytics: Customer experience analyses offer you an overview of the overall customer experience by analysing metrics such as the Customer Satisfaction Rate (CSAT) and First Response Time (FRT), among others.  
  • Customer retention analytics: These analyses cover metrics related to customer retention, such as the Net Promoter Score (NPS), Customer Lifetime Value (CLV), and Customer Effort Score (CES).  

In the next section, we dive into the various use cases of customer service analytics.  

Customer Service Analytics Use Cases 

There are various scenarios in which customer service analytics can come in handy.  

1. Analysing customer suggestions and feedback  

With stiff competition across sectors, businesses go to great lengths to gather feedback from their customers to identify areas for improvement. In fact, statistics show that 3 out of 4 customers will likely spend more money on businesses that provide them with a good customer experience (CX).  

This shows the extent to which gathering and analysing data from customer feedback is crucial – it enables businesses to make improvements to steadily improve their CX. Customer service analytics can help you take the raw data gathered from customers and turn it into actionable data over time.  

Customer Service CRM, like LeadSquared, lets you collect customer feedback in one click and analyze it thoroughly. 

Analysing customer suggestions and feedback

2. Discover the customer’s pain points  

As mentioned earlier, the customer service department holds a wealth of information about customer preferences, challenges, and pain points. Leveraging this data and analysing it gives businesses the opportunity to find solutions to these problems, particularly so if it affects a large segment of their customers.  

For example, if the form for your newsletter is faulty, customers might contact you for a solution. The data gathered will give your tech team the signal to fix the problem or bug immediately and ensure provisions that prevent the same error from repeating.  

The following screenshot from LeadSquared’s Service CRM dashboard showcases how you can track customer conversations and previous interactions to discover their pain points.  

Ticket Details View- customer service analytics

3. Improving your ability to prioritise tickets  

Customer service teams generate tickets for a whole range of issues or challenges their customers face. Prioritising these tickets often depends on the complexity of the issue and how strongly your customers feel about the problem. Sentiment analysis allows businesses to gauge how customers feel about their customer experience (positive or negative).  

With access to such data by way of customer service analysis, customer service teams can segregate tickets between high and low priority. This ensures those issues that demand immediate attention are dealt with at the earliest.  

4. Measuring employee performance

While customer service analytics can give you an overview of the customer’s experience with your business, it can also shine the mirror inwards or within your organisation. With access to a comprehensive customer service analytics database, you can evaluate which members of your customer service team have the fastest response times and success with resolving customers’ issues.  

With this data, you can evaluate the performance of each member of your team, rewarding those who perform well and implementing Performance Improvement Plans (PIP) for those who need to pull up their socks.  

Key Customer Service Metrics to Consider  

With the various use cases of customer service analytics out of the way, let’s dive into some of the key metrics you must evaluate to monitor your customer experience.  

1. Average Response Time  

average response time

The amount of time a customer service agent takes to respond to a customer query refers to the average response time. The quicker the response, the more satisfied a customer is when compared to businesses that take much longer to respond.  

Further, the shorter your response time, the less churn your business witnesses, and customers feel assured of your willingness to address their pain points.  

2. Ticket Volume

The ticket volume refers to the total number of customer service tickets your business generates within a given period.  

This is a reflection of the challenges customers face when navigating your website or using your product or service.  

This metric proves to help identify trends that may be driving customers to seek your customer support mechanism.  

3. Customer Satisfaction (CSAT)  

Customer satisfaction, or CSAT, refers to how happy customers are with your business or its offerings. It is typically measured on a scale of 1 to 10, with 1 indicating dissatisfaction and 10 indicating maximum satisfaction.  

4. Customer Lifetime Value (CLV)  

Formula for Customer Lifetime Value (CLV)

Customer Lifetime Value refers to a customer’s total spend with your business throughout their engagement with your brand if they’re recurring purchasers or refer your brand to their friends or family, their lifetime value increases, generating more revenue for your business.  

On the other hand, when the CLV decreases, this can indicate a sense of dissatisfaction with your brand or customer experience.  

While we’ve discussed four metrics you can monitor, there are several others that form a key part of analysing your business’s customer service. These include the Customer Effort Score (CES), Customer Churn Rate, Product Adoption Score, and the Net Promoter Score (NPS), among others.  

Here’s a comprehensive list of customer service metrics you should track

The Best Customer Service Analytics Tools Available  

In this section, we explore some of the best customer service analytics tools available in the market.  

1. LeadSquared  

LeadSquared’s Service CRM is among the best customer service analytics tools available today. It offers businesses end-to-end ticket management.  

Team-Analytics- customer service analysis

It offers omnichannel customer support, giving customer service teams an overview of tickets generated from complaints across multiple mediums, such as emails, live chat, and social media. It also offers native chat support and integrated telephonic support.  

What’s more, it offers you a complete overview of a customer’s information at your fingertips. In fact, 6 out of every 10 customer service agents say a lack of customer data leads to negative experiences. LeadSqaured solves this by giving agents a 360-degree overview of a customer.  

Other key features include:  

  • A detailed overview of customer interaction history 
  • The ability to integrate your customer service with third-party apps outside of the LeadSquared suite 
  • The ability to create and share internal notes in real-time 
  • The provision to create child tickets for more granular customer support 
  • Providing overall team analytics to help analyse the performance of your customer support team 

2. Qualaroo  


Qualaroo is a comprehensive customer feedback software that streamlines the process of gathering customer feedback about your business or its offerings. It transforms free-form text into actional data that allows you to analyse mood metrics and customer sentiments to ensure more targeted solutions to customer pain points.  

Some of its key highlights include:  

  • Viewing the total number of customer responses, date and time of the survey, etc.  
  • Sorting customer responses based on browsers and operating systems.  
  • Automatic segregation of NPS surveys based on promoters, detractors, and passives.  

Customer Service Analytics vs Customer Experience Analytics  

The terms customer service and customer experience often seem to be used interchangeably, but there’s a key difference between the two. Customer service is a part of a larger umbrella of customer experience.  

Customer experience refers to how a customer experiences or interacts with your brand, right from the minute they’re onboarded till they make a purchase. This process includes customer service.  

However, some of the metrics discussed above can also be an indicator of overall customer experience while also providing actional insights into your business’s customer service.  

In Conclusion  

Customer service analytics plays a crucial role in today’s competitive market. With customers benefiting from a range of options, businesses must prioritise a seamless and trouble-free customer experience. What’s more, they must analyse their interactions with customers to make key changes and improvements to ensure maximum retention.  

If you’re a business that’s looking to analyse its customer service to gather key insights, you must schedule a demo of LeadSquared. With its comprehensive dashboard, your customer service team will have access to every aspect of the customer’s information, along with a seamless mechanism to create and manage tickets to ensure maximum efficiency.  

Reach out for a demo today!  

More in this series:  


1. What are the different types of customer service analytics?

There are several different types of customer service analytics. Some of them are prescriptive analytics, descriptive analytics, predictive analytics, customer experience analytics, and customer retention analytics, among others.  

2. What are the benefits of customer service analytics? 

Some of the key benefits of customer service analytics are that it helps organisations analyse customers’ suggestions, feedback, and pain points, manage employee performance, and improve ticket prioritisation.  

3.Which are some of the best customer service analytics tools available? 

LeadSquared and Qualaroo are some of the best customer service analytics tools available today. While LeadSquared offers a comprehensive suite of services and end-to-end ticket management, Qualaroo is primarily a customer feedback software.  

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