Customer Service Analytics Guide For Sales and Customer Teams

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 its 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.

6 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 support 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 support 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 Average Ticket Resolution Time

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

To do that, you’ll need a tool like timetoreply or a customer support 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 support agents or get a better sense of the entire team’s performance.

We recommend both. Measure the individual customer support 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 support 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.

5 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 support requests.

Cutting down on average time to first reply is essential to building a successful customer support 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.

6 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 mails, 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 support 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 an incoming mail 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 support and email metrics at a glance.

Timetoreply allows customer service teams and managers to easily track average time to reply, average first time to reply, and a host of other metrics that can guide improvements in your customer service teams.

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 can 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 to 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.

To learn more about how we help sales and customer service managers boost their teams performance on email. Visit timetoreply.com

Originally published at timetoreply.com/blog