The most important customer support metrics for web3 project and how to improve them


Customer support is becoming increasingly crucial as web3 enters a mainstream adoption phase. Quality customer support increases referrals, customer satisfaction, and lifetime value. 

Effective customer support teams have a robust, scalable, data-driven approach to helping customers. 

To understand how effective your support currently is and to help identify areas of improvement, gathering data and tracking customer support metrics is vital. It will also enable you to visualize how your customer support team improves over time and help with resource planning. 

This article will look at some of the most important metrics you should be monitoring and how to interpret them.

What are customer support metrics?

There are three main customer support metric categories:

  • Productivity: the amount of work being done
  • Performance: how long it takes to resolve issues
  • Quality: the impact you are having on customers

Setting up a data-driven approach covering all three categories from day one will enable your customer support team to flourish and help the broader business grow. 

Median vs. Average

Before we jump in, let's just recap the difference between a median and an average number. 

The average is the number you get by dividing the sum of a set of values by the number of values in the data set. In contrast, the median is the middle number in a set of values when those values are arranged from smallest to largest.

The advantage of the median is that it removes outlier (very high/low) values from a data set. This is particularly relevant when you only have a small data set. 

So what metrics should you track, and how can you use them?

Productivity Metrics

1) Ticket volume


Ticket volume is the total number of tickets that have been created by customers needing support.

Tracking this metric will give you a good idea of the demand for customer support and how that changes over time. You will see how often customers interact with support and whether they regularly encounter problems. It will also help you to identify changes in support needs following key business events, such as changes in the product (e.g., a new feature release) or a new NFT drop, for example.

When looking at ticket volume over time, you can also focus on specific periods, days of the week, and time ranges to identify the busiest/quietest periods and manage your team schedules accordingly. This volume analysis will save money and improve your customer service.

2) Ticket backlog 

You can think of a ticket backlog as a snapshot of the work a customer support team has outstanding.


A backlog can be broken down into a few different key metrics.

A) number of tickets per status.

At Mava we’re using the following statuses:

  • Open = ticket is waiting for a response from a customer support agent
  • Pending = waiting for the customer to respond
  • Waiting = waiting for internal action/information before responding to the customer
  • Resolved = query resolved, no further action required
  • Spam = query is spam, no other action required

Looking at tickets across different categories helps understand  general workloads and identifying if there are any significant backlogs.

B) Number of unresolved tickets

The number of unresolved tickets adds up all the tickets across open, pending or waiting statuses.

This metric summarizes all the outstanding work the customer support team has in one number. If this number keeps growing over time, it's likely that the team either needs to improve its efficiency or that the team requires more resources. 

c) Waiting for first response: 

The number of new customer queries that are waiting for their first response.

Understanding how many tickets are waiting for their first response is critical to help the team respond and manage new inquiries.

Performance metrics

1) Median first response times:

Median reply time shows the median time it takes for a customer to receive an initial reply from a customer support agent. 

Customers have increasingly high expectations for reply times, especially with live chats. 

It's helpful to set a target for this metric internally and communicate that with users to manage their expectations. We see large teams targeting between 1-30 minutes for their first response or within 24 hours for smaller ones. 

You can help manage customer expectations by communicating your internal target with users  when they create a ticket. This can be easily done when setting up the Mava dashboard.

In the Mava dashboard, you can also set notifications to help keep your team on top of new customer inquiries as they come in.

2) Median resolution time:

Median resolution time shows the median amount of time it takes to completely resolve a customer's issue. (You could also look at average resolution  times, but if you have one or two tricky cases that take a long time to resolve, it will skew all of your data if you use average instead of median).

Customers want their issues resolved quickly, so this is a critical metric to focus on. Teams should monitor this metric over time and aim to improve it.

Getting a complete picture of the customer issue in as much detail as possible and writing clear support responses will help.  

With Mava, you can create automations to help gather as much relevant information about a customer as possible to answer the query more quickly. For example, you could automatically request a user's crypto wallet address or transaction ID to view their on-chain activity.

3) Number of interactions per ticket

This metric shows the total number of interactions between the customer and the support agent.

As a rule of thumb, the fewer customer interactions required, the better. 

Understanding the number of customer and agent responses can provide insights into the performance of a support agent and the complexity of the problem being solved. 

Suppose the average number of customer responses is much higher than an agent. In that case, it could suggest you have unresponsive support agents or a more serious underlying issue with your customer support strategy or product.

Getting a complete understanding of the customer problem and providing a clear answer can help reduce the number of interactions required. 

Support agent training and automations like 'boiler plate' answers are vital to ensure prompt, high-quality replies. 

Quality metrics

1) Customer satisfaction score (CSAT): 

CSAT refers to post-support customer feedback based on the quality of support received.

A CSAT request could be a 'good' or 'bad' binary question or a small scale like 1-5 stars. Asking users for feedback can also prove helpful when trying to gain deeper insights.

The CSAT score is calculated by adding the number of positive responses received from the survey and dividing it by the total number of responses.

CSAT provides a great metric to track your community's general 'vibe' following a support experience, and the metric can be measured over time with the aim of improving it. An 80% satisfaction score is often regarded as excellent. 

It can be helpful to regularly review support cases where a user has provided very good or very bad feedback to help understand what went well/wrong and then incorporate those insights into future training.

2) Customer Effort Score (CES) 

CES indicates the amount of effort the customer required to resolve their issue. 

Companies with low customer effort scores have been shown to have better customer loyalty and more repeat customers. 

Providing omnichannel support can help improve this metric as it avoids customer context switching, makes it easier to find support, and enables them to give context to the issue they are facing more easily. 

Using the Mava dashboard enables you to provide consistent omnichannel support wherever your web3 community is.

3) Net Promoter Scores (NPS) 

NPS measures how likely your customers are to recommend your product or service to someone else.


The NPS is one of the most accurate (and simple) indicators of the impact the customer support experience has had on the customer's overall perception of your organization. 

The information is gathered from your customers by asking them to rate how likely they are to recommend your product/service, usually on a 1-10 scale. After all the information has been collected, you can categorize users into specific groups depending on their scores as follows;

Promoters: Customers who selected 9 or 10 on their NPS survey.

Passives: Customers who selected 7 or 8 on their NPS survey.

Detractors: Customers who selected 0-6 on their NPS survey.

To get your NPS, you can subtract the percentage of detractors from the percentage of promoters for a given period to reveal NPS.

The formula looks like this:

4) Number of tickets reopened 

The number of resolved tickets that have been reopened.

It's normal to have customers reopen tickets either because their inquiry hasn't been fully resolved, they have stopped responding for a long time, or they have a new inquiry. 

You want to keep reopened tickets to a minimum. A large number of reopened tickets could be a sign that your team is closing tickets before they are fully resolved, which often frustrates customers. Sending reminders to customers and checking if they need more help is a helpful way to ensure issues really are resolved before closing a ticket. 


As we have seen, quality customer support is increasingly viewed as necessary as web3 adoption gathers pace mainstream. It's important for the whole organization, whether you are a DAO, NFT project or a DEX, it can help retain customers, increase customer value and increase referrals.

Being data-driven is vital, and tracking support metrics from day one is important to ensure you are maximizing your overall chances of business success. That said, metrics alone won't satisfy customers; you need to monitor, interpret and implement your insights continuously.

Setting key performance indicators (KPIs) for some key metrics and working with your team to improve them is a great starting point.

If you would like to learn more about Mava or need some advice on how to improve your customer experience metrics, don't hesitate to reach out to the team at [book demo link].


Team Mava



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