12 customer satisfaction metrics examples to measure

Your business success directly reflects your customer’s satisfaction with your company. Yes, many other factors contribute to a successful business, but if your customers are not happy with your offerings or their experience with your company, they’re not likely to keep spending money and time on you.

Customer satisfaction drives a successful business, but how do you know what metrics measure customer satisfaction? Once you know these metrics, how do you turn this data into actionable insights to improve your business and customers’ experience?

In this article, we’ll explore customer satisfaction metrics, including 12 critical metrics (and examples) that successful companies use to make their customers happy.

What are customer satisfaction metrics?

Customer satisfaction metrics are used to understand how customers feel about your offerings, brand, and experience. You gather these metrics from customer feedback and (legally) collected data about your customers and their interactions with your company.

By analyzing data from customer satisfaction metrics, you can learn more about your customers and make changes as needed to increase customer satisfaction. Without these metrics, you’re only guessing how well your company meets customer expectations and needs. You’d have no way to measure the effectiveness of new products, features, resources, business processes, customer service success, or marketing programs.

Why are customer satisfaction metrics important?

Measuring and analyzing customer satisfaction metrics helps you to forecast purchasing behavior and revenue. Without reliable customer satisfaction metrics, you can only speculate what’s in your customers’ heads and if they’re satisfied with your offerings and experience. Measuring customer satisfaction from multiple perspectives and sources helps you:

Provides data-backed insights for actioning: Tracking customer satisfaction metrics provides the evidence you need to support productive change in your organization. You’re no longer guessing what’s working and what’s not. Knowing these metrics is only the first step. Then, you must use that information to take action to improve them.
Build better revenue forecasting models: With customer satisfaction metrics, you can better predict how much your customers will spend every month, quarter, and over their lifetime as a customer. With metrics like customer lifetime value and churn rates, you can better understand what influences customers to keep buying, and what pushes them to leave. You can also use software like Idiomatic to calculate the total cost per support ticket, so you can factor customer support costs into your budget forecasting.
Understand factors influencing customer loyalty or churn: It’s relatively easy to calculate customer loyalty by calculating how long they remain customers. However, that calculation alone doesn’t inform what influences their decisions to stay loyal or leave for your competitor. For example, when you combine customer satisfaction metrics with deeper feedback analysis from multiple sources, you can learn the “why” behind why 50% of your customers don’t renew their service after the first three months.

Direct measurement metrics

Often the most insightful data comes directly from your customers. These can be obtained through sources like surveys and chat transcripts.

Here are three common direct measurement customer service metrics you’ll find helpful:

Customer Satisfaction Score (CSAT)

Customer Satisfaction Scores (CSAT) are gathered by prompting customers with a brief, one-question survey after an interaction or action event (like a purchase). It asks customers to rate their satisfaction regarding their recent interaction using a rating scale, usually 1-5 or 1-10. It can also be done through an emoji scale.

You can also ask additional, open-ended follow-up questions to better understand the reasons for their rating. While not everyone will answer the open-ended questions in CSAT surveys, some will, and it’s an excellent opportunity to get the valuable details you need to know what needs to be improved.

CSAT example: Instacart

Instacart increased customer satisfaction scores for their customer service department by better understanding their customer’s needs during the customer support process. They discovered that when customers self-selected their ticket issues, ticket routing lacked the precision to specialize their agents.

After using Idiomatic’s machine learning algorithms and data from the Zendesk integration, they could more effectively route tickets, which was proven by a 35% increase in positive CSAT scores. It also saved them $445k annually in support costs.

CSAT example: Google

Google is great at measuring customer satisfaction levels throughout its websites and platforms. They regularly collect contextual feedback from customers as they’re in the middle of their experience, such as browsing for search results or skimming map results for the business or address they need.

While browsing, an unobtrusive pop-up will ask them to rate their satisfaction with the search results. This allows them to tag responses to specific pages, experiences, and search results so they can use that to inform changes to their algorithms to produce more accurate results for the searchers.

Net Promoter Score (NPS)

Net Promoter Score (NPS) surveys collect a more overarching opinion of your brand, not necessarily related to a specific customer service interaction or user event. For example, your company would strategically place these rating-scale, 1-question surveys at specific points in the customer journey to measure their satisfaction.

The question usually asks, “How likely are you to recommend us to a friend?” Then users are given a scale from 1-10:

Customers who score 9 or 10 are your PROMOTERS (most loyal customers)
Customers who score 7 or 8 are your PASSIVES (mostly happy customers, but could be easily swayed)
Customers who score a 0-6 are your DETRACTORS (Unsatisfied customers are likely to jump to a competitor or talk unkindly about your company to others).

NPS example: Slack

Slack has long been known as a customer-centric organization that listens to its customers to optimize their offerings. They use insights from their NPS surveys to ensure every interaction their prospects and customers have with the brand is working to their customer’s highest satisfaction.

Customer Effort Score (CES)

Customer Effort Scores (CES) measure how much effort a customer puts into specific tasks or interactions. Common interactions include contacting a customer service rep or requesting a return or refund. Customer effort score surveys are best targeted to specific events and sent to the customer immediately after that interaction or event has concluded.

Indirect measurement metrics

Getting feedback directly from customers is helpful in getting a higher level understanding of customer satisfaction. However, combining this voice of customer data with indirect customer satisfaction metrics can uncover more of the why behind the satisfaction or dissatisfaction.

Here are three indirect customer satisfaction metrics you can track to start filling in the gaps:

Customer Churn Rate

The churn rate is the percentage of customers who leave or unsubscribe within a given period. Higher churn rates usually correlate with low satisfaction or no longer meeting your customer’s needs.

To calculate this, you divide the number of customers you lost during the period with the total customers at the start of the period. Then, multiply that by 100 to get your customer churn rate percentage. You want as low of a churn rate as possible, but most companies can expect an average “good” churn rate of 3-7%, on average.

Customer Churn Rate example: FabFitFun

Subscription box company FabFitFun struggled to manually tag their customer data, causing inconsistencies and too much time spent deep-diving to get only surface-level customer insights on the customer experience and satisfaction.

They used Idiomatic to analyze their data and do the tagging for them using sophisticated machine learning. The result was a decrease in customer churn, a 49% decrease in customer complaints, and a 250% increase in product satisfaction.

👉 Learn more about predicting customer churn rate.

Customer Health Score (CHS)

Your customer health score (CHS) is an indicator of customer loyalty and longevity. It tracks several metrics including product usage period, money spent, number of support interactions, and how often they complete surveys. Based on these metrics, each customer is assigned a score of:

Weak: Not likely to remain loyal or recommend you to others. They are likely dissatisfied with your brand and experience, as may be indicated by low spend or a high number of support tickets. These customers are usually likely to provide low-score feedback or talk about your brand negatively to others.
At-risk: These customers have an inconsistent or unreliable experience with your brand. They may not use the product as often, not purchase upgrades or upsells, and may contact support when they need help. They may sometimes complete your surveys, and their rankings and sentiment wanes.
Healthy: These customers usually spend the most, use your product often, and have few customer service interactions (or interactions that end positively). The happiest of your healthy customers are most likely to provide regular and detailed feedback to sing your praise.

To increase customer satisfaction levels, you need to work hard to improve the customer experience for the weak and at-risk customers, and keep doing what the healthy customers need. Don’t forget to take into consideration customer sentiment to include customer feelings towards your brand as part of your customer health score.

👉 Learn more about doing customer sentiment analysis online.

Customer Lifetime Value (CLV)

Customer Lifetime Value (CLV), also known as average customer value, is a measure of how much you can expect to earn for each customer over the course of their loyalty to your brand. Here is the formula to calculate customer lifetime value:

[Average Transaction ($)] X [# of Transactions] X [Retention time period] = Customer Lifetime Value

The higher your average customer lifetime value across your customer base, the higher likelihood that your customers are happy. This metric is often referenced with customer loyalty, churn, and health results.

In addition to using CLV to understand your customers, you can also use it as a predictor and estimator of future customer revenues or revenue goals.

Other helpful customer satisfaction measurement metrics

The more detailed voice of customer and customer behavior details you have, the more complete picture you can get for individual customers and your total customer base. Here are several other helpful metrics that can help you paint a clearer picture and help you understand the “why” behind customer satisfaction so you can make the necessary business decisions.

Abandonment Rate

The abandonment rate calculates the percentage of customers who start an action or request but never complete it. High abandonment rates may indicate poor customer service, difficulties using self-service tools, or difficulty navigating your website or online checkout system. Examine this rate to see where customers stop looking for possible reasons for the drop so you can fix them or improve their chances of successfully completing their action or task.

First Response Time

First response time measures how long it takes a customer service rep to acknowledge a ticket or call. Even if the customer query is non-urgent, their satisfaction greatly depends on how quickly their problem can be acknowledged.

First Contact Resolution

First contact resolution rate measures how often tickets are solved with only one touchpoint with your team. Being passed to multiple customer support team members or departments can be frustrating for customers who often have to re-explain their issue multiple times, increasing their frustration (and your costs) and adding to their dissatisfaction and a negative customer experience.

Ticket Resolution Time

Ticket resolution time is a calculation of the average time it takes to solve a ticket from the time the ticket is entered into the system to a positive outcome for the customer. This time is linear and the “timer” doesn’t stop until the ticket is fully closed.

Ideally, your goal is to make this as quick as possible. Consider categorizing customer tickets by type or topic for a more accurate representation, as some tickets will inherently take longer to resolve than others, which can skew your averages.

Average Ticket Time

The average ticket time calculates how much of your customer service team time is dedicated to actively working on tickets. It doesn’t include any time it sits in the queue or when it’s waiting for another agent to take the next action. The timer runs when an agent picks up the task to work on it, and ends when they put it down, regardless of if it’s been resolved or not. This helps you evaluate workload, staffing, and individual employee performance.

Customer Retention Rate

The customer retention rate calculates how many customers remain customers during a specific period. The higher your customer retention rate, the higher likelihood your customers are satisfied with their experience.

👉 Read this article for more tips on measuring customer satisfaction

Implementing customer satisfaction metrics into your business strategy

Every business can benefit from knowing their customers better. Customers’ needs will evolve, especially as your offerings change. This is why it’s essential to do this “gut check” to make sure your customers are receiving the changes you’re making in your organization well. Tracking customer satisfaction metrics, like the ones mentioned in this article, is pivotal to measuring the customer experience and predicting your revenue.

The most successful companies collect these customer satisfaction metrics and use them as data-backed insights to change business processes, outputs, and offerings to meet their customers’ evolving needs. This requires a commitment to analyzing data from all perspectives and taking action based on the insights you learn.

The problem with measuring multiple customer satisfaction metrics is that it’s not scalable through human-powered means alone. Analyzing mountains of data from multiple sources with consistency and depth to come to meaningful, actionable next steps based on that data is not possible–at least for humans. That’s where AI can be a huge help.

Using AI in your customer satisfaction analysis

Idiomatic is a customer experience management platform that takes all your direct (voice of customer) and indirect customer satisfaction data and metrics, categorizes them, and shows you the actionable insights you need to predict future success. What would have taken days (or likely months) of manual analysis now takes seconds and is updated regularly as new customer data is automatically fed into the platform.

With Idiomatic as your customer feedback analysis software, you can uncover the following:

Which issues cause the most customer dissatisfaction?
Which issues are costing you the most money?
What roadblocks are hindering your business growth or customer retention goals?
The “why” behind your measurable customer experience metrics.

This “why” can tell you why your customer satisfaction metrics are not higher, why they went down, and what actionable steps you can do to increase customer satisfaction and earn higher scores. This necessary detail can’t be determined from scores alone. You need to also look at customer comments, written feedback, and other sources of qualitative feedback.

Truly customer-focused companies use Idiomatic to analyze all sources of qualitative and quantitative customer satisfaction feedback efficiently, reliably, and effectively. It provides the ‘Why” insights you need to support change.

Request your custom demo of Idiomatic today to learn how it can generate the insights you need to grow your business and increase customer satisfaction.

12 customer satisfaction metrics examples to measure in 2023