What is customer service analytics?

Customer service analytics is the process of capturing and analysing data that is generated by your customer service teams.

Companies use customer service analytics to identify areas of improvement, guide email reply time policies, and help bring general improvements to the level of customer service your teams provide.

The importance of customer service analytics

Customer service analytics is essential to any company that wants to improve and optimise their customer service capabilities.

If you want to deliver an exceptional customer service experience, you need to measure the performance of your customer service teams against a set of accurate metrics. Once you can measure your customer service performance, you will be in a better position to improve that performance.

But knowing which customer service analytics to track isn’t always clear. How do you ensure you are tracking the correct aspects of your customer service team’s performance?

We’ve put together a handy guide to help you understand the most important customer service metrics you need to track – and explain what tools you have to make that a little easier.

Types of customer service analytics

Descriptive Analytics

Descriptive analytics is like looking in the rearview mirror. It focuses on summarizing past customer service data to understand what has happened. For instance, it can reveal trends in customer complaints over the past year or highlight the most common issues faced by customers.

Predictive Analytics

Predictive analytics is all about forecasting future events based on historical data. It can predict customer behavior, such as which products are likely to be popular next season.

Prescriptive Analytics

Prescriptive analytics goes a step further by recommending specific actions to achieve desired outcomes. It’s like having a GPS for decision-making. For example, if predictive analytics shows an increase in customer churn, prescriptive analytics can suggest targeted retention strategies.

Diagnostic Analytics

Diagnostic analytics digs deeper into data to understand the root causes of issues. It’s like being a detective, solving the mystery of why something happened. For example, if there’s a sudden drop in customer satisfaction, diagnostic analytics can identify whether it was due to a product defect, poor customer service, or other factors.

10 Customer service analytics metrics that you should track

1 Customer Satisfaction Score (CSAT)

Customer satisfaction is one of the most important metrics for any customer service team. It measures your customers’ level of satisfaction with the service and general experience with your business.

CSAT is measured by asking your customers specific questions about their experience, and giving them the option to rate that experience against a sliding scale. For example, you could ask customers to ‘rate the experience with your business on a scale of 1 to 5, with 1 being poor and 5 being excellent’.

Ask enough customers this question, and you can compile a score to show the average level of customer satisfaction. You can calculate it using the following formula:

CSAT = Sum of Positive Responses / Total Responses * 100

With this information, you can see an average level of satisfaction with your services, and challenge your own assumptions about the experience customers have when interacting with your business.

Consider asking CSAT questions to customers once their support ticket is closed to get a quick view into their levels of satisfaction. Or you could ask long-term customers about their experience to get a sense of where improvements need to be made.

2 Net Promoter Score (NPS)

A Net Promoter Score (NPS) indicates how likely a customer is to recommend your company, its products or services to other people. A typical NPS question asks: “On a scale of 0 to 10 – with 0 being unlikely and 10 being highly likely – how likely are you to recommend our company to a friend?”

Generally, anyone that gives you a score of 8 or above is considered a promoter. A customer that scores 0 to 6 is considered a detractor.

You can calculate your NPS using a simple formula:

NPS = (% of answers that were Promoters) – (% of answers that were Detractors)

A low NPS can point to problems. When customers aren’t happy with the product or service you provide, it’s more likely that they’ll leave for a competitor, leading to higher churn and lower revenue.

It’s a good idea to use NPS to get feedback on customers’ experience with your product. For example, you could ask them how happy they to recommend your product after they’ve used it for a month. Or you could run NPS surveys after interacting with your customer service teams to see how they felt about that experience.

3 Customer Effort Score (CES)

A Customer Effort Score (CES) measures the level of effort your customer needed to exert to get a resolution to their support ticket or sales query. It’s really important to track CES: if a customer feels it’s too much effort to solve problems using your service, they’ll most likely leave for a competitor.

A typical CES question is “How easy was it to resolve your issue?” Answers should be provided on a sliding scale ranging from very difficult to very easy.

Once you have a suitable number of responses, you can use the following formula to calculate your CES:

CES = (% of positive replies) – (% of negative replies)

The score you get from this formula is a valuable indication of how well your customer service system is working.

Leave customer service queries unanswered or neglected for too long, and the score will be low. Provide quick, accurate and helpful responses to resolve customer issues, and you’ll see a higher Customer Effort Score.

CES is a valuable tool for companies to understand how effective their customer service teams are at solving customer problems. By having a benchmark of CES, companies can identify where problem areas exist and implement suitable corrective measures.

4 Customer lifetime value (CLV)

CLV predicts the total value a business can expect from a single customer account. Increasing CLV is about enhancing the customer experience over time.

5 First contact resolution (FCR)

FCR tracks the percentage of customer inquiries resolved during the first interaction. High FCR rates often lead to higher customer satisfaction.

6 Customer retention rate (CRR)

CRR measures how well a company keeps its customers over a specific period. Higher retention rates often indicate a healthy business.

7 Customer churn rate

This metric calculates the rate at which customers stop doing business with a company. Lower churn rates are better, as they indicate customer loyalty.

8 Average ticket resolution time

This one is pretty self-explanatory. Average ticket resolution time refers to the time it takes for your customer service teams to successfully close a support team.

To do that, you’ll need a tool like timetoreply or a customer service system like Zendesk to keep track of all ticket resolution times.

Once you have that data, you can use a simple formula to calculate your average ticket resolution time:

Average Ticket Resolution Time = Total time to resolve all tickets / Total number of tickets resolved

You can use this formula to measure the performance of individual customer service agents or get a better sense of the entire team’s performance.

We recommend both. Measure the individual customer service agents’ average ticket resolution time to see which team members are being overwhelmed with too many tickets. By measuring the entire team’s performance, you can also see if there are any bottlenecks in your customer service processes that hold up the team’s workflow.

Want to lower your average ticket resolution time? Consider implementing a standard email reply time policy and using a tool such as timetoreply to keep track of customer service emails and the time it takes to resolve the issue.

9 Average Time to First Reply

This metric is closely aligned to the Average Ticket Resolution Time, and tracks how long it takes your customer service team to provide a first reply to incoming customer service requests.

Cutting down on average time to first reply is essential to building a successful customer service team. Eighty percent of customers will continue to use a business – and spend 67% more – if the service is fast, convenient and knowledgeable.

Improving your average time to first reply also gives you a handy competitive advantage: only 36% of companies reply to incoming customer queries within an hour.

You can calculate your average time to first reply manually by using the formula:

Average Time to First Reply = Total time of first replies / Number of queries replied to

Measuring and improving this metric is essential to building a winning customer service team. You don’t need to necessarily resolve the customer’s issue in your first reply.

So long as the customer knows you are attending to their problem, you will at least put them at ease that you are not about to ignore your loyal customers or their concerns.

10 Average Time to Reply

Average time to reply is a vital metric that measures how long it takes your teams to reply to all incoming emails, whether they are first-time queries or part of an ongoing conversation.

This builds on the Average Time to First Reply by ensuring every email that customers send your business is attended to, and that your customer service teams remain responsive.

If your first time to reply is quick but you take hours – or days – to respond to subsequent queries, you’ll still end up with unhappy, dissatisfied customers. And you’ll have to face the likelihood that you’ll lose those customers to more responsive competitors.

Improving your average time to reply holds a number of benefits. It helps you understand how well your customer service teams are doing in meeting the KPIs you’ve set.

For example, you’ll want to know that your teams and customer service agents can respond to incoming emails within 30 minutes – at least during office hours.

By tracking your average time to reply on group mailboxes, you’ll also be able to identify problem areas and bottlenecks that negatively impact your reply times.

You can calculate your time to reply by using the formula:

Average Time to Reply = Total sum of time to replay to all emails / Total emails replied to

However, instead of manually measuring this important customer service metric, you can use timetoreply. Once installed to your email platform of choice, timetoreply gives you a dashboard from where you can see all your important customer service and email metrics at a glance.

timetoreply allows customer service teams and managers to easily track the average Time to Reply, the average First Time to Reply, and a host of other metrics that can guide improvements in your customer service teams.

How customer service email software can help you

Email is a weak spot for companies if they lack visibility into their teams’ performance. This will lead to lower levels of customer satisfaction, increase customer churn and affect your revenue.

Using a customer service email software tool will bring much-needed support to how you manage your customer service teams and agents, offering a number of benefits that include:

1. Better workload management

The data produced by customer service email software is there to highlight important aspects of your team’s performance, helping you identify areas of improvement. Gaining visibility over peak email traffic times, individual workloads, average email response times and average first reply times helps you determine where you need to allocate additional resources, or where more training and support is required.

2. Never miss important emails

Keeping track of large volumes of incoming emails can be tricky without some clever technology tools to support you. Despite your teams’ best efforts, there’s always a chance that an important email goes amiss, leading to unhappy customers and lower levels of customer satisfaction. Customer service emails software tools such as timetoreply will help keep track of emails and remind users when an email is about to reach its SLA threshold. This way, you’ll never miss an important email again.

3. Make smarter decisions

Want to ensure timely and effective replies to customer queries? Using a customer service email software tool will equip you with accurate data on the email performance of your customer service teams. When applied smartly, these data insights will help you achieve higher levels of customer satisfaction and deliver better bottom-line results.

Use Cases of Customer Service Analytics

Customer service analytics provides a wide range of applications to improve business operations and customer experience. Here are some key use cases based on comprehensive data analytics:

1. Enhancing Customer Feedback Analysis
   • Analyzing customer feedback deeply lets companies uncover actionable insights to improve products or services. For example, companies can determine customer satisfaction levels or identify necessary updates to digital products like mobile apps based on user comments and issues reported.
2. Improving Decision-Making and Reducing Costs
   • Customer service analytics allow businesses to forecast and plan more effectively, identifying the most frequented customer support channels. This foresight helps allocate resources wisely and create knowledge bases to reduce the frequency and need for customer contacts, thus saving costs.
3. Optimizing Customer Support Operations
   • Detailed analytics help monitor and improve support team performance. Metrics such as average response time, ticket volume, and customer satisfaction scores are tracked to ensure team members perform up to standard and identify improvement areas.
4. Prioritizing and Managing Customer Interactions
   • Advanced AI and machine learning tools can analyze customer interactions to prioritize urgent tickets and manage workload. This helps better handle customer issues, ensuring critical problems are addressed promptly and efficiently.
5. Building Self-Service Tools
   • Data-driven insights can guide the development of self-service options such as FAQ sections, chatbots, and other digital aids that empower customers to find solutions independently, leading to quicker resolutions and enhanced customer satisfaction.
6. Personalizing Customer Experiences
   • By understanding customer behaviors and preferences, companies can tailor their interactions and services to meet individual needs, enhancing the overall customer experience and boosting loyalty.

These use cases enhance the efficiency of customer service departments and contribute significantly to a better understanding of customer needs, leading to improved product development, marketing strategies, and overall business growth.

Future Trends in Customer Service Analytics

The customer service landscape is quickly changing due to significant advancements in technology and analytics. Here’s what you can expect in the near future:

1. Increased Adoption of AI and Machine Learning
   • AI and machine learning are set to transform customer service by automating responses and providing real-time insights. Companies will leverage AI to handle a vast majority of customer interactions, significantly reducing the need for human intervention in standard queries.
2. Focus on Self-Service and Digital First Strategies
   • Self-service options will continue to grow, as customers increasingly prefer to resolve issues independently. Tools like AI chatbots will become more sophisticated, offering more accurate and helpful responses, leading to a rise in customer satisfaction and operational efficiency.
3. Enhanced Personalization Through Predictive Analytics
   • Predictive analytics will play a crucial role in personalizing customer interactions. By analyzing past behavior, businesses can predict future needs and tailor their communications accordingly. This approach will not only improve customer satisfaction but also enhance loyalty and retention.
4. Integration of Omnichannel Customer Journeys
   • The focus will shift towards seamless integration of multiple service channels to deliver a unified customer experience. Businesses will strive to ensure that interactions are consistent across all platforms, enhancing the overall customer journey.
5. Proactive Customer Service Initiatives
   • Businesses will move from reactive to proactive service models. Advanced analytics will enable companies to anticipate customer issues and address them before the customer reaches out. This proactive approach is expected to reduce customer effort and increase satisfaction.
6. Rise of Conversational User Interfaces
   • Conversational interfaces, such as voice and chatbots, will become more refined, making interactions more natural and efficient. These tools will be capable of handling complex customer needs, thereby reducing the dependency on human agents for such issues.
7. Commitment to Privacy and Ethical Data Use
   • With increasing scrutiny on data usage and privacy, companies will need to prioritize transparent and ethical use of customer data. This will involve implementing robust data protection measures and ensuring compliance with global data privacy regulations.

Key takeaways

Companies have access to several measurement tools to help them understand the success or otherwise of their customer service teams. Tools such as Net Promoter Score, Customer Satisfaction Score, Customer Effort Score and Average Time to Reply will give customer service teams insight into where their efforts are succeeding, and where they can still improve.

While there are manual ways to track and measure many of the metrics available to customer service teams, companies can also use technology tools to automate some of this work. This frees up precious internal resources while ensuring full visibility over important customer service metrics.

timetoreply is an invaluable tool for customer service teams seeking higher levels of customer satisfaction. Our platform easily integrates with any email service and delivers valuable insights into the performance of customer service teams.

You can see what timetoreply can do for your customer service teams with a no-obligation 15-day free trial. Get in touch today to unlock the next level of customer satisfaction success with the best customer service email analytics tool.