Customer Service Analytics: Types, Uses, and Benefits Explained
Do you ever wonder, what is customer service analytics? Just like a more general field of data analytics, it’s a process of extracting insights from data. When we are talking about customer service analytics, it’s a process of collecting data from various service touchpoints and using it to get actionable information on how to improve customer relationships and have more satisfied customers.
Businesses provide customer service over multiple channels - support tickets, email, phone, live chat, and social media. When an agent completes a chat or closes a ticket, their job is done. For analysts, however, it’s just beginning. Every customer interaction creates data that can - and should - be broken down into multiple metrics and analyzed for actionable insights.
Types of customer service analytics and metrics
There are different approaches to measuring the performance of customer service with different sets of metrics - there may be as many as 23! However, you don’t need to use all of them to find ways of improving your customer service. What you need, though, is to build and implement a working analytics strategy. This is what this post is going to be about.
Customer journey analytics
With this type of analytics, you get a view of all interactions your customer has with you as they progress across multiple touchpoints. At each touchpoint, you can collect data to analyze customer service experience and identify behavior trends and patterns.
For example, you can see where you tend to lose customers and determine why they abandon you at those points. With this knowledge, you can work out ways to improve experience and retain customers.
Customer engagement analytics
To analyze the level of customer engagement, you can gather data on how customers connect with you over various channels. In the process, you get valuable insights about how they use your product and how they interact with you.
Customer engagement analytics provides such important metrics as Customer Effort Score, showing how easy it is for customers to succeed in their interactions with the brand, and Customer Engagement Rate, measuring how effective your content and other marketing methods are.
Customer experience analytics
This type of analytics uses customer feedback and other data to measure the customer satisfaction level and identify pain points. For customer service, this is a great source of insights into the quality and performance of support teams.
Using customer experience analytics data, you can pinpoint issues in your support flows and design improvement strategies. For example, if you note a suboptimal First Contact Resolution Rate, you might need to plan additional training for your agents to empower them with sufficient knowledge.
Customer retention analytics
This type of data analytics shows why you may lose customers. Based on such metrics as Net Promoter Score, Customer Lifetime Value, and Customer Effort Score, it determines how long customers remain loyal to the brand and what makes them churn.
For example, if data proves that interactions with the brand require high effort from the customer, that may be the cause of low retention. On the contrary, easy and intuitive contacts boost customer loyalty and encourage brand referrals.
Benefits of customer service analytics
Knowledge is power, and knowing the drivers of customer behavior empowers you with insights into what you need to do to improve your customer service. Customer service analytics gives you a broader view into that and into what, basically, drives your business.
Achieve data-driven decision-making
Gathering raw customer service data is of little practical use. However, when analytical algorithms are applied, data yields insights into customer behavior that allow drawing conclusions and planning improvements.
Such trends as a rising volume of support requests after an update or too many “connect to an agent” messages in a chatbot may indicate that changes are necessary. You may need to find more effective ways of communicating product updates to your audience, for example, via advanced knowledge base solutions, or review your chatbot script for higher customer satisfaction.
Improve customer service quality
Bringing order and clarity to customer data can improve the quality of the service itself. By identifying the issues, you discover ways of eliminating them. On the other hand, you may see what works well and retains customers, turning them into loyal brand advocates.
Customer service analytics allows support agents to understand customer needs and expectations better and provide a level of service ensuring customer loyalty and retention.
Measure the performance of customer service teams
Metrics generated by the analytical tools give insights not only into customer behavior trends but also into the performance of your support agents. Based on these metrics, you can identify issues and implement improvement measures.
For example, if your analytics show long First Response Times, it may mean that your team is understaffed and should be expanded. Or, if you see poor First Contact Resolution Rates, it may be time for training.
Optimize customer support costs
By raising the efficiency of support teams, you get better control over your service costs. When analytical processes reveal deficiencies in your support flows, you see where excessive spending may occur. Streamlining your support may allow you to decrease the related costs.
There are many ways you can use analytical data to optimize costs. For example, a closer view of incoming tickets can suggest prioritization approaches that focus the team’s attention on the most critical requests. Alternatively, you may enhance your customer support representatives with technology channels, such as a chatbot or knowledge base, to reduce the load on your agents.
Practice proactive customer service
Trends revealed during the analysis of customer service data and the corresponding marketing metrics can help you anticipate their needs and be better prepared for requests. Using analytical data accumulated over time can provide ideas for switching to a more proactive and personalized customer service.
A growing number of support requests after an update may suggest such measures as adding detailed information about new releases to your knowledge base or FAQ page with simultaneous sharing of these resources in release announcements.
Increase customer loyalty and retention
By analyzing customer data, you can see what makes them abandon you and initiate the necessary improvements. Moreover, your improvement steps may even boost retention, as, statistically, for 83% of customers, a proper response to their complaints is an additional loyalty driver.
At the same time, you may also identify unhappy customers who are still with you but consider leaving. Reaching out to them and resolving their dissatisfaction points might prevent them from choosing your competitors.
Customer service analytics use cases
Let’s look at the practical cases where you can use customer service analytics.
Improve customer satisfaction
By including the Customer Satisfaction (CSAT) metrics in your analytics, you get customer insights into what improves satisfaction and what brings it down. This information will help you to adjust the customer service strategy and achieve a higher customer satisfaction score.
Streamline ticket prioritization
Analytical data, such as customer sentiment analysis, can help you identify support requests that are especially urgent for customers. You can set up your ticketing system to prioritize requests that the analytical tools mark as critical and make sure that they are handled within the shortest time possible.
Select content for a knowledge base
If you set your analytics to track the reasons for customer requests, you will be able to detect repeating or frequent issues that customers have with your product. If these issues can also be resolved independently, they may become topics for your knowledge base.
Optimize service ticket assignment to agents
Processing incoming customer requests, analytical tools can use their content to immediately determine the topic or urgency of the customer’s issue. Based on these details, the system can automatically route tickets to experts in a certain field or more experienced agents for prompt resolution.
Identify customer pain points to accelerate projects
Information gathered from customer conversations and other interactions can contain ideas for further product development. Knowing customers' pain points helps you understand which new features or updates they would like to see. This knowledge can be helpful in streamlining dev processes.
Boost revenue
In addition, customer service data can help to identify visitors who are closer to a purchase than others. For example, analytics can show if a visitor is random or does he come back several times. Next, you can apply some engagement techniques and drive the visitor to become a customer.
Best customer service analytics tools
Customer service analytics becomes increasingly complex, as algorithms evolve, allowing businesses to use data to the maximum, getting insights that propel them to success. Choosing appropriate tools is the first step to harnessing customer data and making it work for you. We have selected some of the most popular customer service analytics tools that you may include in your data integration strategy to ensure effective data flow and processing.
Google Analytics
Google Analytics is a platform that provides multiple website analytics metrics. Using Google Analytics, you can get deep insights into your website performance by several criteria:
- Traffic and customer journeys
- Customer behavior patterns derived from the analysis of customer actions
- Audience segmentation
Google Analytics is offered as a freemium pricing model, where the basic version is free of charge and a paid enterprise version is available upon request.
Sprout Social
Sprout Social is a great tool for getting analytics on your social media presence. It gives you a 360 degree view of all references and mentions of your brand, likes, comments and messages that you get.
AI-powered analytics includes sentiment analysis and insights from years of historical data. Based on the results, Sprout Social provides content recommendations for higher engagement.
Sprout Social pricing plans start at $199 per seat/month, with more advanced features offered at $299 per seat/month (Business) and S$399 per seat/month (Advanced). Enterprise plans are offered upon request. All pricing models include a 30-day free trial.
Kissmetrics
Kissmetrics is another great tool to analyze customer behavior. You can get reports on all user actions, generate insights on customer journeys, feature usage, page visits, and other metrics. All this data shows new revenue opportunities and can help you to drive growth.
Kissmetrics is offered at $299 per month for small teams or $499 per month for medium-sized teams. Custom plans can be requested additionally.
Hotjar
Hotjar is a tool to track customer behavior that gives you insights in a format of infographics. You can see website heatmaps showing how users browse it and where they tend to have issues. Also, the tool gives you visual data of user journey and real-time customer feedback on their experience with your website.
You can get the basic version of Hotjar for free, however, for more advanced features, you will need to subscribe at €32 per month (Plus), €80 per month (Business), or €171 per month (Scale).
Mixpanel
Mixpanel provides real-time data in the format of dashboards. The tool gathers and analyzes multiple user metrics, giving full visibility into their behavior and the effectiveness of your marketing campaigns. Analytics is delivered in an easily comprehensible format accessible by non-technical employees.
You can start with a free plan and upgrade to the Growth subscription plan if you need to track more events. Or you can go straight to the Enterprise plan for more flexibility.
Amplitude
Amplitude tracks and visualizes various types of customer data, providing insights into experiences and journeys, and showing opportunities for increasing conversions and attracting new customers. With Amplitude, you get data on your product and your website together with AI-powered automated reporting.
Amplitude offers a free tier suitable for startups and a Plus plan at US$49 per month. For the full set of features and custom options, you need to contact the company for a Growth plan offer.
Zendesk
Zendesk represents customer service data in a clear and understandable format with insights into interactions and customer journeys. It gives you insights into the performance of all your customer contact channels - email, social media platforms, phone, or chat.
Zendesk offers a basic custom plan at $19 per month which will be the best for small businesses. All-in-one plans from the Suite category cost from $55 to $115 per month per agent. Also, Enterprise plans are available.
Conclusion
Excellent customer service starts with understanding what customers expect from you and how they perceive your brand. Today, getting this knowledge is not possible without powerful customer service analytics tools that track and monitor every step customers make across your website and convert this data into insights. It’s up to you to turn insights into strategies that drive growth. We hope our guide to customer service analytics helps you on the journey to success.