Customer service data provides valuable insights about your clients that you can — and should — use to improve the experience your business provides. But since this data comes from various sources, you need a reliable customer service analytics tool that reveals your customers’ true experience to different departments within your company. Luckily, the market offers plenty of reliable tools.
We’ll walk you through a few options in this article. Plus, we’ll cover why customer service analytics matter and what metrics to track. Let’s start with defining what customer service analytics is.
What Is Customer Service Analytics?
As an integral part of customer analytics, customer service analytics offer insights from data generated by your customer support team. In many cases, customers themselves provided the data, intentionally or unintentionally, when they interact with your brand, whether via email, social media, phone, website, or any other channel.
Customer service analytics is a daunting undertaking if customer support channels are segmented because you may get a fragmented view of the data, and thus, miss important insights. If you want to make the most of your customer support data, you must maintain its integrity. This means making sure it’s complete, accurate, and consistent. Using a help desk platform equipped with advanced analytics features will help you follow an approach that maintains your data’s integrity.
How Does Customer Service Analytics Relate to the Integrity of Your Data?
When approached correctly, customer service analytics keeps businesses and clients focused on positive customer experiences. You’ll know what products and solutions customers want most, what customer support strategies meet customers’ needs, and where your company should go the extra mile to ensure an impeccable customer experience.
| Data Aspect | Description | Importance for Reliable Customer Service Analytics |
| Data Accuracy | Customer service analytics relies on accurate data to provide meaningful insights. If the data used in the analysis is inaccurate or contains errors, it can result in incorrect conclusions and recommendations. Ensuring that your data is accurate and up-to-date is essential for reliable customer service analytics. | High importance |
| Data Consistency | Consistency in data is crucial for meaningful analysis. If data is recorded in different formats or inconsistent units, it can lead to incorrect interpretations and insights. Maintaining consistency in how data is recorded, stored, and analyzed is critical for reliable customer service analytics. | High importance |
| Data Completeness | To gain comprehensive insights, it’s important to have complete data. Incomplete data can result in incomplete or biased conclusions. Ensuring that all relevant data is captured and included in the analysis is crucial for accurate customer service analytics. | High importance |
| Data Quality | The quality of your data is crucial for reliable customer service analytics. Poor data quality, such as duplicate records, outdated information, or data entry errors, can result in unreliable insights and decision-making. Ensuring data quality through regular data cleansing, validation, and verification processes is vital for maintaining the integrity of your data and accurate customer service analytics. | High importance |
| Data Security | Protecting the security and privacy of customer data is essential for maintaining the integrity of your data. Data breaches or unauthorized access to customer data can compromise the accuracy, consistency, and completeness of your data, leading to inaccurate customer service analytics. | High importance |
But where do you find your customer service data? Let’s review the main sources.
8 Sources of Customer Service Data
Customer service analytics use data from all channels you use to interact with clients.
Your ultimate goal is to find a way to keep all that information in one place, find a tool to process the data, and get actionable insights. Here are the major sources of customer service data.
Help desk platform
Your help desk tool is the primary source of customer feedback and other customer data. You can analyze what types of concerns your customers raise most often, which channels they typically use to reach out to you, how often they contact your support team and when, and so on. And then there are the performance metrics of your support team, which we’ll explore in more detail later.
Note that if at any point you decide to switch from one help desk tool to a different one, you should make sure to transfer all your data to maintain the continuity of customer service analytics.
Website and knowledge base (KB)
Insights about how your customers interact with your website (and with your knowledge base, in particular) can help you continuously improve the customer journey.
Engagement rate, average time on page, scroll rate, click-through rate, and other indicators help companies assess how customers interact with the business online and what content they search for more often.
Survey responses
Surveys are an easy way to get customer feedback about your product or service. In addition, surveys are a perfect tool to learn about specific aspects of customer interactions, such as the courtesy of the customer service representative, the speed of resolving the customer’s problem, whether the representative understood the customer’s need, and beyond.
Surveys give you a nearly real-time assessment of customer interactions with your business but from the customers’ perspective. Surveys are also important customer analytics data to understand your support team performance dynamics.
Online reviews
Do your support agents read and respond to customer reviews? You can use this type of customer feedback to evaluate customer satisfaction with, and acceptance of, your products and services, as well as general satisfaction with the company.
Customer Interactions
This includes data generated from customer interactions with customer service representatives through channels such as phone calls, emails, chat logs, social media messages, and other communication channels. This data may include customer inquiries, complaints, feedback, and other interactions.
Social Media
Social media platforms, such as Facebook, X, and LinkedIn, can be a rich source of customer service data. Organizations may monitor social media for customer mentions, comments, and messages, which can provide insights into customer sentiment, issues, and feedback.
Call Recordings and Transcripts
Organizations may record customer service phone calls for quality assurance purposes. These recordings can be transcribed and analyzed to extract insights about customer issues, preferences, and feedback.
Other Data Sources
Depending on the organization and industry, customer service data may also come from other sources such as product usage data, warranty claims, returns and exchanges, customer reviews, and third-party data sources.
Let’s recap all the information sources into a table to get a short version.
| Data Source | Description |
| Help Desk Platform | Primary source of customer feedback and other customer data. Can provide insights into customer concerns, channels used for communication, support team performance metrics, etc. |
| Website and Knowledge Base | Provides insights into customer interactions with the website and knowledge base, including engagement rate, average time on page, scroll rate, click-through rate, etc. |
| Survey Responses | Provides feedback from customers through surveys, offering insights into specific aspects of customer interactions, such as satisfaction with service representatives, problem resolution speed, etc. |
| Online Reviews | Customer feedback obtained from online reviews, which can be used to evaluate customer satisfaction with products/services and overall company satisfaction. |
| Customer Interactions | Data generated from customer interactions with customer service representatives through various channels such as phone calls, emails, chat logs, social media messages, etc. Includes customer inquiries, complaints, feedback, etc. |
| Social Media | Data obtained from monitoring social media platforms for customer mentions, comments, and messages, providing insights into customer sentiment, issues, and feedback. |
| Call Recordings and Transcripts | Recordings of customer service phone calls that can be transcribed and analyzed for insights into customer issues, preferences, feedback, etc. |
| Other Data Sources | Other potential sources of customer service data, such as product usage data, warranty claims, returns and exchanges, customer reviews, and third-party data sources. |
Customer Service Metrics You Need to Track for Robust Analytics
When choosing which metrics to track for customer support analytics, you should consider metrics for both customer experience and team performance. This way, you’ll know what clients are unhappy with and how well your team resolves their issues.
Let’s take a closer look at the most common metrics for customer experience and for support team performance.
Team performance metrics
To assess the performance of your customer support team, you can measure the following metrics:
- Ticket volume measures the total number of tickets in your support queue over a certain period of time.
- First response time (FRT) is the time between a customer submitting their request and your team’s first response to it.
- Average response time (ART) is very similar to FRT, but ART calculates the average time a customer waits for a reply, regardless of whether it’s their first message or not.
- Mean time to resolution (MTTR) reflects how much time, on average, it takes a support team to resolve a customer request.
In a nutshell, these metrics show how many requests your customer support team receives and how long it takes them to respond and resolve issues. Understanding these metrics will arm your company with actionable insights to improve support team workflows. For example, you might need to adopt automation tools to reduce manual work and speed up response and resolution times.
Analytics for customer service agents’ performance clue you into what experience your customers receive. For example, if a client systematically waits days to hear from an agent, chances are, they’re probably already considering abandoning your company.
Customer experience metrics
These metrics help you analyze customer support data to give you a comprehensive view of how your customers feel about interacting with your support team, chatbots, knowledge base articles, and with the company in general.
Here are a few useful metrics to keep in mind.
- Net promoter score (NPS) assesses the likelihood that the customer will stick with and recommend your business.
- Customer satisfaction score (CSAT) measures how satisfied customers are with the company’s general performance or with a specific aspect of support.
- Customer effort score (CES) assesses customers’ perception of how easy it is to use your company’s product, service, website, support channels, and beyond.
Most of the metrics you need for customer service analytics are built into the tools already used by various departments in your company, such as marketing, support, and HR.
| Customer Service Metrics | Definition | Key Insights |
| First Response Time (FRT) | Average time taken to respond to customer inquiries or issues | Lower FRT indicates faster response times and better customer service |
| Average Handle Time (AHT) | Average time taken to handle a customer interaction, from initial contact to resolution | Lower AHT indicates more efficient customer service |
| Customer Satisfaction (CSAT) | Level of customer satisfaction with customer service team’s performance | Measured through post-interaction surveys or feedback mechanisms |
| Net Promoter Score (NPS) | Likelihood of customers recommending the company or product to others | Provides insights into customer loyalty and advocacy |
| Customer Effort Score (CES) | Level of effort required by customers to resolve issues with customer service team | Lower CES scores indicate easier interaction and issue resolution |
| Resolution Rate | Percentage of customer inquiries or issues resolved successfully by customer service team | Higher resolution rate indicates better performance in addressing customer needs |
| Service Level Agreement (SLA) Compliance | Extent to which customer service team meets agreed-upon SLAs | Important for meeting performance expectations and delivering quality service |
| Escalation Rate | Percentage of customer inquiries or issues escalated to higher levels of support or management | Higher escalation rate may indicate challenges in resolving customer issues at the frontline |
| Agent Performance Metrics | Individual performance metrics such as call volume, call quality, customer feedback scores, etc. | Helps identify top performers, areas of improvement, and training needs |
| Channel Metrics | Channel-specific metrics such as response time, resolution time, customer satisfaction, etc. | Provides insights into channel effectiveness and customer preferences |
| Customer Retention Rate | Percentage of customers who continue to do business with the company over a specific period of time | Measures customer loyalty and effectiveness of customer service efforts |
| First Contact Resolution (FCR) Rate | Percentage of customer inquiries or issues resolved on the first contact | Indicator of customer service effectiveness and efficiency |
| Resolution Time | Time taken to resolve a customer issue or inquiry | Directly impacts customer satisfaction and loyalty |
| Agent Performance Metrics | Metrics related to agent performance such as call/chat/email volume, quality scores, adherence to scripts/guidelines, etc. | Provides insights into individual agent performance and areas for improvement. |
However, your customer support agents might also use specialized standalone tools designed specifically for support teams. Which one is better for your business? Let’s dig deeper.
Built-in or Standalone Help Desk Analytics Tools: Which One Is Best for Your Business?
The major difference between built-in service desk analytics tools and standalone tools is that the first is part of the same platform used for customer relationship management, marketing, support, and so on, whereas the latter functions independently for the sole purpose of customer support analytics.
Another distinctive difference is that built-in customer support analytics tools allow multiple departments to use and exchange data through the same platform. In contrast, standalone software isolates the data generated by service desk analytics tools from other departments’ data, and vice versa, which presents a challenge for data integrity.
Let’s review a few examples of each type and describe some use cases.
Built-in tools
Built-in analytics for customer service is a great option if you already use some of the products from this platform. In this case, you’ll have a single source of data shared across various departments to help you better understand customer behavior tendencies and business performance. Plus, integrating and setting up pre-configured solutions is easier than setting up a standalone system.
If you’re considering switching to a new customer relationship management (CRM) tool and help desk, we recommend you choose a solution with features for various departments, as it will be easier to use and maintain. Plus, it ensures data integrity, which is essential for quality customer support analytics.
Built-in tools offered by Zendesk, Freshdesk, Zoho Desk, Intercom, Help Scout, and HubSpot Service Hub are worth checking out.
| Help Desk Software | Key Metrics Tracked | Additional Features |
| Zendesk | FRT, AHT, CSAT, NPS, resolution rate, agent performance metrics, and more | Advanced analytics and reporting options for more in-depth analysis and customization |
| Freshdesk | Ticket volume, resolution time, customer satisfaction, agent performance, and more | Customizable dashboards and reports to track and analyze specific metrics based on business requirements |
| Help Scout | FRT, AHT, resolution time, customer satisfaction, and more | Visualizations and filters to analyze data in-depth and generate custom reports |
| Zoho Desk | Ticket volume, resolution time, customer satisfaction, agent performance, and more | Customizable dashboards and reports to track and analyze specific metrics based on business requirements |
| Intercom | Customer engagement, response time, conversation volume, user activity | Custom reporting options and integrations |
| HubSpot Service Hub | Customer interactions, ticket volume, response time, agent performance | Customizable dashboards, reports, and integrations with other tools |
Their solutions are best for medium-to-large enterprises that already have comprehensive data sources for customer analytics and want an ecosystem that simplifies data collection and processing. However, businesses that have just started mind find the pricing prohibitive.
Standalone tools
Standalone tools—such as Looker, Tableau, Power BI, and Google Analytics—collect the data from whichever customer support channels you use. It’s an ideal solution for startups and small businesses that don’t have many data sources yet but want to make the most of the information they do have. Insights gained from using these tools help you know what changes you need to make to increase customer satisfaction scores.
The major drawback of these standalone service desk analytics tools is that they deal with analytics for customer service separately from the data your marketing team collects. This leads to data fragmentation and interferes with the ability of different teams to combine their data about customers’ behavior, preferences, and pain points.
| Standalone Tools | Features | Pros | Cons |
| Looker | Collects data from customer support channels | Ideal for startups and small businesses | Separate analytics for customer service data |
| Tableau | Collects data from customer support channels | Provides insights for improving customer satisfaction scores | Data fragmentation between customer service and marketing data |
| Power BI | Collects data from customer support channels | Offers advanced and customizable data visualization and analytics | Interference with data integration from different teams |
| Google Analytics | Data visualization and analytics platform | Provides custom reports and analysis for help desk systems | Separate analytics from marketing team data |
In addition, not all standalone tools support a UI-based data migration option. If you need to switch to a different platform, you will likely have issues with transferring data between platforms, disrupting the integrity of your data.
Wrapping Up
The benefits of customer service analytics are impressive: from understanding your customers better and getting quality data to making informed decisions and eliminating spending on what doesn’t work.
Reliable customer service analytics tools with metrics that help evaluate different aspects of customer interactions with your business are crucial. Fortunately, there are plenty of analytics tools for support teams of all sizes and budgets that you can use either as part of a complex CRM system or as a standalone solution.
As your business evolves, you may switch between different CRM and customer analytic tools. When you do, you should always ensure that you bring all of your data with you. And if you need smooth and fast data migration that involves zero manual effort, Help Desk Migration is here to help.