This article is part of a larger series on Customer Service.
Customer service analytics involves the process of analyzing customer behavioral data and using it to discover actionable insights. This data gives you a deeper understanding of your customers’ needs and expectations. The numbers and analytics also serve as a basis for improving service strategies, enhancing overall customer experience, and increasing customer retention and loyalty.
In this article, we break down the definition of customer service analytics and detail its use cases and benefits to a business.
According to Salesforce’s State of the Connected Customer report (October 2020), more than half of customers expect businesses to understand their unique needs. They also expect to have consistent interactions across a company’s sales, marketing, and service departments. Customer service analytics tell you how satisfied customers are with the support they receive, indicate customers’ needs, and point out areas for improvement.
Defining Customer Service Analytics
Customer service analytics involves collecting and analyzing all information and metrics generated from the customer service team of a business or organization. This data includes how many customers are satisfied with your company’s service and the problems they’ve encountered during their interactions with your reps. You can derive this data from support tickets, live chats, emails, and social media conversations.
Artificial intelligence (AI)-powered tools uncover valuable insights that help formulate new business strategies and design better customer experiences. You can also predict customer behavior based on previous actions. This way, your service team can be better equipped and prepared 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 are tagging tickets, routing tickets to the most appropriate or next available agent, and detecting the most urgent issues that need to be resolved.
Customer Service Analytics Use Cases
Businesses need customer service analytics to evaluate the quality of support being provided to customers and other key stakeholders. The data you collect helps identify tactics that are working well and pinpoint issues you can improve upon. Below are some of the most important use cases of customer service analytics.
1. Gauging Customer Sentiment
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 take steps to improve your branding or email marketing campaigns to make your product more appealing to customers.
2. Determining the Next Best Offer
By looking at customers’ buying history, patterns, and interactions, you can determine the products or offers they would most likely be interested in during their next purchase. The data you collect helps you understand what your customers need at the right time. For example, retailers can identify newly pregnant women based on their changing buying patterns and send them new offers on baby products.
3. Identifying Customer Churn Causes
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 or defect using predictive analytics, which analyzes past behaviors to find patterns and predict a certain outcome. One important indicator of an 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.
4. Measuring Rep Performance
Customer service analytics help you monitor and measure 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 step up their performance.
5. Discovering New Revenue Streams
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.
Customer Service Metrics
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’ sentiment. 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 service 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 of these 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 and gauge customer experience:
Customer Service Analytics for Agent 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 important 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 that your business receives, including the nature of each ticket.
Customer Service Analytics for Customer Experience
Customer service analytics give you a clear idea of how your clients perceive your business as they progress through their customer journey. It is important to monitor these metrics because every customer interaction influences future purchases from them or their network. These are some examples of essential metrics that measure 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 is a measurement of your customers’ loyalty to your brand and how likely they are to recommend your business to other people.
- 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.
Examples of Customer Service Analytics Tools
There are different kinds 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 service analytics tools.
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.
What it costs: Support Desk plans are free or could cost $15 to $79 per agent, per month. Omnichannel support plans cost $29 to $99 per agent, per month (on annual billing).
What it is: A self-service business intelligence and analytics platform that helps you analyze business data, as well as build reports and dashboards.
What 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). The on-premise system is either free (one user) or costs $30 per user, per month (on annual billing).
What it is: A business intelligence and big data analytics platform that helps you explore, analyze, and share business data.
What it costs: Pricing is customized according to your business size and scale of deployment. Quotes are available upon request.
What it is: AI-powered business intelligence reporting software that helps you analyze large volumes of data and track metrics that are relevant to your business.
What it costs: Pricing is customized according to your data size and number of seats. Quotes are available upon request.
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. You can sign up for Freshdesk’s free Support Desk plan or try out its omnichannel support package for 21 days at no cost.
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