What Are Customer Service Analytics? A Guide to Enhancing Customer Experience
This article is part of a larger series on Customer Service.
Customer service analytics involves analyzing customer behavioral data and using it to discover actionable insights. This data gives you an understanding of your customer’s needs and expectations. Analytics also serve as a basis for improving service strategies, enhancing the customer experience, and increasing customer retention and loyalty. In this article, we break down the definition of customer service analytics, detail its benefits to a business, and share popular tools.
Types of Customer Service Analytics
Customer service teams use a combination of KPIs to measure the quality of their performance and identify areas of improvement. The information is also used to make decisions about staff schedules. For instance, if the metrics indicate a high volume of support requests during the weekend, the team manager might decide to add more staff on duty on those days.
Both qualitative and quantitative data are needed to get a clear picture of their customers’ sentiments. Some business surveys ask their customers to rate their level of satisfaction with the support team and a follow-up explanation of why they gave such a rating.
Qualitative vs Quantitative Data
In customer support analytics, quantitative data refers to sets of information that can be expressed in numbers. Examples of these datasets include Net Promoter Score (NPS), Customer Effort Score (CES), and Goal Completion Rate (GCR). Qualitative data refers to unstructured information that is not predefined and is harder to analyze. Examples include opinions on a product design, reasons for shopping cart abandonment, and customer complaints.
Below are some of the common customer service metrics businesses use to assess customer service performance:
Customer service analytics help you monitor aspects of customer interactions, such as calls and chats to measure the effectiveness of support reps’ workflows. Below are examples of essential call metrics that indicate your support team’s performance and efficiency.
- Average first response time: This tells you the average waiting time for each customer before they receive an initial response to their request for support.
- First Contact Resolution (FCR) rate: This metric reflects the ability of your support reps to resolve customer issues in a single interaction.
- Ticket volume: This is the number of issues or support requests your business receives, including the nature of each ticket.
Customer service data analysis gives you a clear idea of how your clients perceive your business as they progress through their customer journey. It is crucial to monitor these metrics because every customer interaction influences future purchases from them or their network. Let’s look at some examples of crucial metrics that measure the customer experience:
- Customer Satisfaction (CSAT) score: This metric measures how satisfied customers are with the quality of your customer service.
- Net Promoter Score (NPS): This measures your customers’ loyalty to your brand and how likely they are to recommend your business to others.
- Customer Effort Score (CES): This score helps you determine how much effort was required to resolve a customer issue and reflects the quality of customer experience during a support interaction.
The Importance of Customer Service Analytics
Customer service analytics gives business owners an accurate picture of the quality of their service. When used effectively, it can have a massive impact on improving an organization’s customer service activities, which could have positive benefits for business performance. It could also greatly contribute to exceeding and managing customer expectations as far as providing support is concerned.
It can show granular details like a particular customer’s purchasing behavior, which agents can use to personalize servicing and offer products that are most likely to sell based on their interests. More than 70% of consumers believe companies should understand their history, so they don’t have to repeat themselves all the time. On the other hand, 68% of customers reported they will spend more money with a brand that treats them like an individual.
Understanding trends like the duration of resolution or types of cases being raised can also provide insights into areas that businesses need to improve on. For example, it can provide insights into the lack of manpower if cases take a while to resolve or point to a particular issue if a specific concern keeps popping up from various customers. These insights are crucial as 76% of customers say they would switch to a competitor due to poor customer service experiences.
Customer Service Analytics Use Cases
Businesses need customer service data analytics to evaluate the quality of support provided to customers and other key stakeholders. The data you collect helps identify tactics working well and pinpoint issues you can improve upon. Below are some of the most important use cases of customer support analytics.
Analysis of content from social media and other publicly available websites can uncover valuable insights. These include consumers’ attitudes or sentiments toward certain products, services, or customer communication channels. If you discover negative sentiments, you can improve your branding or email marketing campaigns to make your product more appealing to customers.
Looking at customers’ buying history, patterns, and interactions helps you determine the products or offers they would most likely be interested in for their next purchase. The data you collect hones in on your customers’ needs at the right time. For example, retailers can identify pregnant women based on their changing buying patterns and send them new offers on baby products.
Data analytics help you understand why customers leave or choose other products over your brand. You can also predict if a customer is about to churn using predictive analytics, which analyzes past behaviors to find patterns and predict a certain outcome. One important indicator of impending churn is the decrease in customer engagement or interest.
Once you identify the root cause of the problem and determine that some of your customers are losing interest in your product, you can take proactive steps to reduce customer churn. For instance, a company with a high churn rate can create personalized offers for each customer segment to help improve customer satisfaction.
Customer service analytics help you monitor key performance indicators (KPIs) against service level agreements (SLAs). This way, you can see which reps are hitting their goals and determine which ones need to improve their numbers. You can also recognize top performers and motivate your team to improve their performance.
Constantly evolving market dynamics and increasing customer service costs are bound to affect the profitability of every business. However, you can use your existing data to find new sources of revenue for your business. Data on your customers’ preferences can be a source of options for services you can offer as add-ons to your existing packages. You can also suggest a new product to complement the customer’s latest purchase.
What is customer churn? Customer churn is the rate or percentage of customers who stopped buying your products or services. In short, these are customers that a business lost in a specific period of time.
How Artificial Intelligence (AI) Supports Customer Service Analytics
When it comes to processing customer service analytics and turning them into actionable insights, using AI can prove useful since it provides recommendations based on actual data. Hence, AI-powered and machine-learning tools uncover valuable, real-time insights that formulate new business strategies and design better customer experiences. You can predict customer behavior based on previous actions, making your team better equipped to assist them in the future.
Role of AI in Customer Service Analytics
AI-powered analytics tools automate service processes, provide relevant insights from massive data sets, and simulate human understanding of the information gathered. Some of the applications of AI in customer service include tagging tickets, routing tickets to the most appropriate or next available agent, and detecting the most urgent issues that need to be resolved. Check out our in-depth guide on how AI can improve customer service.
Examples of Customer Service Analytics Tools
There are different types of customer service analytics tools and software. Some of them are integrated into help desk software, while others come in the form of business intelligence platforms. Click on the tabs below to see examples of customer analytics example tools.
What it is: Zendesk’s analytics and reporting tool allows businesses to connect to their customer service data, turn it into actionable insights, and create visual reports. Interested in learning more about this software? Read our full Zendesk review to learn more.
How much it costs: $49 to $150 per agent, per month (on annual billing)
What it is: Freshdesk’s built-in help desk analytics tool helps you understand customer data and lets you create custom reports based on agent performance and customer satisfaction metrics. Read our Freshdesk review to learn more about this provider.
How much it costs: Support Desk plans are free or could cost $15 to $79 per agent monthly. Omnichannel support plans cost $29 to $99 per agent monthly (on annual billing).
What it is: Zoho Analytics is a self-service business intelligence and analytics platform that helps you analyze business data, as well as build customer service reports and dashboards.
How much it costs: Cloud-based system is free for two users, with paid plans ranging from $24 per month (two users) to $455 per month (50 users).
What it is: Acquire.io provides users with various types of analytics, including chat, email, customer satisfaction, and agent performance.
How much it costs: Price is available upon request; however, published information indicates its pricing ranges from $25 to $250 per month
Do you want to utilize a robust helpdesk tool, but don’t know where to start? Read our in-depth guide on how to choose help desk software for small businesses in eight steps.
Customer service analytics provide businesses with actionable insights on their support reps’ performance and customers’ needs. An effective customer service platform like Freshdesk offers built-in help desk analytics tools, helping you determine areas of improvement in both agent performance and customer experience. Sign up for Freshdesk’s free Support Desk plan or try out its omnichannel support package for 21 days at no cost.