Customer Experience Metrics: From Customer Satisfaction to Business Outcomes

Customer experience metrics help businesses better understand customer behavior, identify points of frustration, and evaluate customer support efficiency. As customer experience unfolds across many interactions, companies use various data sources, such as surveys, support interactions, website activity, reviews, and business outcomes.

This article covers key metrics for evaluating customer experience and customer satisfaction with a company’s products and services. It also explains the tools and methods for measuring CX and which metrics to track based on a company’s CX maturity and the stage of the customer journey.

Key customer experience metrics: Summary table

Survey-based customer experience metrics

• Customer Effort Score (CES)
• Net Promoter Score (NPS)
• Customer Satisfaction Score (CSAT)

Customer Effort Score (CES)

CES measures the level of effort customers make to complete a specific interaction, such as resolving an issue through customer support or completing an order. This metric can be measured on a scale from 1 to 9, where 1 means “very difficult” and 9 means “very easy.” A company may also use emojis or open-ended questions in its surveys, which can still be converted into a numerical value.

The higher the CES, the more likely customers are to continue using a product or service. Meanwhile, a low CES indicates difficulties or misunderstandings in the customer experience, which can lead to customer churn.

Surveys to determine the CES are often conducted in the following situations:

• after interacting with customer support;
• after communicating with a chatbot;
• after purchasing a product or service;
• after signing up for a subscription.

Typically, companies ask customers to rate the effort they made immediately after the interaction, while they still remember the details and are ready to share their feedback. For example, as soon as a live chat with a support agent ends, a survey may appear in the chat window.

How to calculate CES

CES is calculated by averaging the customer responses: the total sum of scores is divided by the number of responses received.

What qualifies as a normal CES level depends on the industry, the nature of the company’s business, and consumer expectations. Ideally, the score should be higher than 80% on the scale used by the company.

Net Promoter Score (NPS)

The Net Promoter Score is a metric based on a single survey question in which customers are asked to rate how likely they are to recommend a company, product, or service to others.

Developed by business strategist Fred Reichheld in 2003, the question “Would you recommend this company?” was recognized as the best indicator of word-of-mouth marketing and was given equal importance to questions such as “How satisfied are you with the product?”, “Does this company deserve your loyalty?”, and “Do you intend to make a repeat purchase?”

How to сalculate NPS

NPS involves dividing respondents into “promoters,” who provided scores of 9 or 10; “passives,” who provided scores of 7 or 8; and “detractors,” who gave a score of 6 or lower. The Net Promoter Score is then calculated as the percentage of promoters minus the percentage of detractors among all respondents.

Customer Satisfaction Score (CSAT)

CSAT surveys typically measure how satisfied a customer is with a specific transaction or the use of a product at a specific point in time. CSAT is also measured after an onboarding process to understand how intuitive it was, or after a conversation with customer support to ensure that the customer is satisfied with the resolution.

To measure an experience that persists over time – such as a streaming service subscription or the use of a tool or electrical appliance – companies give customers several days or weeks before asking for feedback. This way, customers have enough time to evaluate the purchase and see how it performs in various usage scenarios.

However, more often than not, CSAT is a “right here, right now” snapshot that captures emotions at a specific moment rather than a long-term relationship with the customer.

How to calculate CSAT

To calculate the CSAT score, add up the positive responses (for example, ratings of 4 or 5 out of 5), divide the total by the number of responses collected, and multiply the result by 100%.

Operational customer experience metrics

• First Response Time (FRT)
• Time to Resolution (TTR)
• First Contact Resolution (FCR)
• Self-service success rate
• Time-to-Value (TTV)

First Response Time (FRT)

First Response Time, also known as First Reaction Time, is the time that elapses between a customer creating a ticket and a support agent responding to it for the first time.

This metric doesn't count automatic confirmations that a company may send via email or chat. It measures how quickly someone on your support team actually interacts with a consumer.

Although customers don’t necessarily resolve their issue on the first contact, the time it takes to connect with a human is still a crucial metric.

As we see in many PissedConsumer reviews, people get frustrated when they can’t get a response from a real representative quickly enough. Consumers believe humans can understand nuances and exceptions that machines may struggle with; therefore, they expect humans to provide a resolution faster.

Many companies set a standard First Response Time in their policies, requiring agents to respond to customers within a specified time frame.

How to calculate the average FRT

The average FRT is calculated as the sum of first response times for all tickets divided by the number of tickets and is typically quoted in minutes, hours, or days.

If the support service doesn't operate 24/7, the average first response time should be measured in business hours. This way, requests received at night or on weekends will not affect the average.

Time to Resolution (TTR)

Time to Resolution measures the average time it takes for a support team to resolve a customer’s issue. Unlike first response time, which only tracks how quickly the team acknowledges a problem, TTR covers the entire request handling cycle — from initial contact and diagnosis to troubleshooting and confirming that the issue is resolved.

A quick and effective resolution can turn a negative experience into a more positive one.

In contrast, a high TTR can indicate disjointed tools and siloed customer interaction data. If agents don’t have a complete picture of the customer’s journey – including order history, page views, and previous conversations – it’s more difficult for them to investigate the issue. Moreover, the lack of a unified customer profile means that customers have to repeat the same information whenever they contact support.

How to calculate the average TTR

The average resolution time is calculated using a simple formula: (total time taken to resolve all support requests over a given period) / (number of requests resolved during that same period).

As with the first response time, support teams that don't operate on weekends or at night should calculate TTR based on working hours.

First Call Resolution (FCR)

First Call Resolution is a metric that measures a call center’s efficiency in resolving customer issues during the first call or contact, thereby eliminating the need for follow-up.

A high FCR rate is appealing because all companies strive to provide high-quality customer service while reducing operating costs.

However, a high FCR rate may also indicate a lack of self-service resources. If customers frequently contact the call center with simple issues or basic questions – such as how to return an item, track an order, or reset a password – you may need to add more information to your website or expand your product’s functionality so they can resolve these issues on their own.

How to calculate the FCR

To calculate FCR, take the number of tickets resolved in a single interaction over a given period, divide it by the total number of tickets received during the same period, and multiply the result by 100%.

Self-service success rate

Self-service is one of the most powerful tools for several reasons:

• requests resolved through self-service don't consume a support agent’s time;
• customers don't have to wait in line;
• customers don't need to wait until the weekend is over if support isn't available 24/7.

Measuring the success of self-service is more challenging than measuring issue resolution by agents. Automated systems can indicate resolution, but customer confirmation is the most reliable way to know whether a problem has been resolved.

Companies use several approaches to measure this metric. For example, you can track whether a customer who viewed a help center article subsequently called support (if not, then self-service was likely successful). You can also include a survey at the end of the article asking, “Did this article help resolve your issue?” And if your product has a built-in support chatbot, you can count the number of conversations that ended without being forwarded to a live agent.

A high number of help center article views combined with a low self-service success rate indicates that customers are trying to solve the problem on their own, but the articles aren’t helping. In this case, the Customer Effort Score drops – after unsuccessful attempts to decipher the documentation, people are forced to spend additional time explaining the problem to an agent.

A balanced set of metrics would include a high number of help center views combined with a high self-service success rate: customers easily find and effectively use self-service resources.

How to calculate the self-service success rate

The metric is calculated as the percentage of support inquiries resolved using self-service out of the total number of inquiries handled through both self-service and agent-assisted support.

Time-to-Value (TTV)

Time-to-Value is a digital customer experience metric that measures how long it takes a new customer to realize the value of a product.

This metric helps companies understand how easily customers can get started with a product and identify friction points in the onboarding process.

Here are some examples of key milestones for measuring Time-to-Value:

• For an email builder: The time from account creation to the moment the user designs and exports their first email.
• For a project management and collaboration tool: The time it takes a team to create its first shared project board and assign tasks to team members.

Customer feedback metrics

• Review rating
• Sentiment
• Complaint volume

Review rating

Review ratings are customer-generated scores that influence buyers' purchasing decisions. Customers typically post ratings on local discovery platforms such as Yelp or Google Maps, independent review platforms like PissedConsumer, or e-commerce platforms such as Amazon or eBay (if you sell products online).

Google Customer Reviews, PissedConsumer, and other consumer review platforms typically use a 5-star rating system, and the overall review score is the average of published ratings.

Marketplaces and digital distribution platforms such as Amazon or Steam have their own rating systems. On Amazon, the overall rating is determined by machine learning models. Instead of simply averaging all ratings, these models take into account factors such as verified purchase status, rating recency, and how much detail – in the form of text, images, or video – the customer provided in their review.

Steam customers rate products using a binary voting system, and the overall rating is calculated as the number of positive reviews divided by the total number of reviews. The product is then assigned to a specific tier, ranging from “overwhelmingly negative” to “overwhelmingly positive.”

Sentiment

Sentiment is a metric that reflects the overall mood or emotion expressed in reviews, which can be positive, negative, or neutral. Companies use this metric to measure what their customers think and feel about their brand.

Originally, sentiment analysis relied on dictionaries of positive and negative words. The algorithm would count the number of times these words appeared in a review and calculate a final score.

Today, sentiment scores are calculated using natural language processing (NLP), text analysis, and computational linguistics. These technologies can interpret tone and context with high accuracy, determining the intensity of emotions, the level of enthusiasm, and the amount of effort a customer felt they had to put into using a company’s product or service.

Complaint volume

Complaint volume is the total number of consumer complaints recorded through support channels and review platforms over a specific time frame.

This metric helps identify instances when complaint volume rises to a concerning level and determine how unusual spikes might be linked to a specific issue. For example, a home appliance manufacturer might analyze complaints collected over the last quarter and find that reports of water leaks have increased sharply for a particular model of washing machine. This trend may go unnoticed in the brand's overall statistics, but it could point to a problem with the product's design or individual components that deserves further investigation.

However, you shouldn’t rely entirely on the number of complaints when measuring customer experience – a lower number could imply that people have given up complaining or are quietly choosing competitors, having no desire to leave feedback.

Business outcome metrics

The following metrics don’t measure customer experience directly, but they show business outcomes that customer experience contributes to.

Customer churn rate

Customer churn rate (or customer attrition rate) is the proportion of customers or subscribers who stopped using a company’s services or products over a given period. It may indicate customer dissatisfaction, the availability of more affordable or better alternatives, or more effective marketing campaigns and sales outreach efforts by competitors. A company may also lose a customer simply because the customer no longer needs a service subscription for professional or personal use.

Tracking this metric is critical, particularly for businesses with high customer acquisition costs. According to a McKinsey report published in 2023, it may take acquiring three new customers to compensate for the cost of losing one.

Customer churn rate is a key metric for streaming services, membership programs, and companies operating under the software-as-a-service (SaaS) model, which heavily rely on recurring monthly or annual revenue from subscribers.

How to calculate customer churn rate

Churn rate is calculated by dividing the total number of customers lost during a period by the total number of customers during the same period.

Customer lifetime value (CLV)

Customer lifetime value is the total revenue a business expects to generate from a customer over the entire duration of their relationship. Several factors can influence how long the customer stay with a company, including the following:

• how often the customer makes purchases;
• how much value they see in the products they regularly buy from this brand;
• how emotionally attached they are to the brand;
• how satisfied they are with customer support.

Customer lifetime value offers a long-term perspective on customer profitability. When a company knows that a customer will generate $10,000 over the course of their relationship, the $1,000 spent on acquiring and retaining that customer is justified, even if their first purchase is only $100.

Customers who stay with a company for years typically generate more profit, recommend the business to others, and contribute to the brand’s reputation by mentioning it on social media. Companies can also generate additional revenue from long-term customers by offering premium plans or complementary products or services.

How to calculate CLV

CLV can be calculated using the following formula: (average purchase value × purchase frequency over a set period) × customer lifespan.

How to choose which CX metrics to track

Choosing CX metrics based on company maturity

You can choose a set of metrics that better suits your company’s maturity level. For example:

Young businesses focus on basic customer perceptions, using metrics such as Customer Satisfaction Score (CSAT), Net Promoter Score (NPS), and Customer Effort Score (CES).

Developing companies measure basic customer experience metrics while also monitoring contact center performance and customer feedback.

Mature companies use sentiment and emotion analysis and track the entire customer journey. Customer Experience teams at these companies link CX metrics to business outcomes such as revenue, Customer Lifetime Value (CLV), Customer Churn Rate, Customer Acquisition Cost (CAC), and Conversion Rate.

Matching CX metrics to the customer journey

You can choose key metrics for different stages of the customer journey, such as:

Onboarding / First interaction

Track Customer Effort Score (CES) to determine how easy the onboarding process was from the customer’s perspective, as well as Time-to-Value (TTV) to measure how long it takes a customer to realize the product value.

Customer service touchpoints

Track Average Resolution Time (ART) and First Response Time (FRT) to measure how quickly live agents respond to customers and resolve their issues, and Customer Satisfaction Score (CSAT) after support interactions to understand whether customers were satisfied with the resolution. 

Post-purchase phase

Track Customer Satisfaction Score (CSAT) to understand how customers feel about product quality, delivery, returns, and other aspects of their experience, and Net Promoter Score (NPS) to assess overall brand perception.

Methods and tools for measuring customer experience

Companies rely on internal or third-party tools for conducting surveys, tracking customer activity within a website or app, and monitoring support interactions. In addition, companies track public reviews and conversations about the brand on social media and web forums, ultimately building a holistic picture of their customer base, their perceptions of the brand, and the critical areas for improvement.

1. Surveys

Email surveys / SMS surveys

When configured as part of an automated workflow, these surveys are typically conducted after an order is placed, a delivery is completed, or a customer interacts with support. They help companies capture customer satisfaction and effort shortly after a specific interaction.

Website surveys

Embedded in product or service pages and help center articles, or displayed as pop-up messages after a certain interaction, these surveys measure the website customer experience.

In-product surveys

Displayed inside an app or SaaS product while someone is using it, these surveys are useful for capturing feedback during the actual product experience.

You can manage and distribute surveys using simple survey tools that are triggered by specific events, or enterprise survey platforms that can handle large-scale survey campaigns and map the replies to the customer’s purchase history and demographic data.

2. Customer service software

Ticketing systems

These systems organize customer inquiries into tickets, store communication history and internal notes, and offer intelligent routing, recommendations for next steps, and cross-team collaboration.

Cross-channel customer support systems

They consolidate customer support interactions across email, social media, online chat, messaging apps, phone calls, and chatbots within a single interface.

CRM tools

CRMs are centralized systems for managing customer data and contacts that track customer interactions with various departments, including sales, marketing, and customer service. Such systems also typically have embedded tools for nurturing contacts, creating calendar events, and setting appointment reminders.

3. Public customer feedback channels

CSAT and NPS surveys are initiated by the company at specific touchpoints, so they may show only part of the customer perception of a brand.

In contrast, public reviews are unprompted feedback in which customers can describe in detail both the positive and negative aspects of a business, as well as specific problems, which they often share in the form of a narrative. By analyzing reviews, as well as metrics such as complaint volume, sentiment, and how often a particular problem appears in customer feedback, companies can more accurately identify the root cause of a problem and the stage at which it occurs.

Typically, companies collect public customer feedback on review platforms, social media, and Reddit.

Final thoughts

The metrics we discussed in this article are far from the only indicators companies can use. We left out the Onboarding Completion Rate, Feature Adoption Rate, Early Churn Rate, and many other metrics that provide a broader perspective on specific processes within the customer journey. As companies refine their approach to customer experience, they move from basic metrics to more precise operational indicators and feedback analysis.

Mature companies also link customer experience to business outcomes. For example, they can analyze how changes in CSAT scores and resolution times correlate with changes in customer retention or repeat purchase rates. Finally, they integrate various data sources, since no single source or metric alone provides a complete picture of the situation.