Customer sentiment: What it is and why you need to measure it

Most businesses understand that customer expectations have risen sharply over the past few years, and that when a company fails to meet those demands, the effects on the bottom line can be grievous. As companies wrangle with delivering compelling customer experiences, they face a difficult yet essential task: figuring out exactly what customers feel when they interact with their business.

This metric—customer sentiment—can be captured and analyzed in a host of ways, from traditional tools such as CSAT and Net Promoter Score (NPS)® to AI-driven programs that parse large amounts of consumer language to identify tone and intent. In this article we’ll explore what customer sentiment is, how it’s analyzed, and why tracking and understanding it is so important to business success.

What is customer sentiment?

Customer sentiment is a metric that businesses use to measure how their customers think and feel about their brand. Despite being a simple measurement tool—it tracks whether users have positive, negative, or neutral views of the company—customer sentiment can be like a canary in a coalmine. Are your customers frustrated and angry? Or are they harboring warm, pleasant feelings about your products and services?

Also known as user sentiment, these feelings about a business play an outsize role in how well a company retains customers and its ability to grow revenue. But beyond that, companies have an obligation to pay attention to customer sentiment metrics not simply because doing so contributes to the bottom line. Upsetting interactions with a company’s support team can cause real emotional damage to customers—and as Zendesk’s research shows, 66 percent of consumers say that a bad experience can ruin their day.

And what’s especially troubling about customer sentiment is that about 20 percent of all support interactions end up being stressful and downright infuriating for consumers. If one-fifth of customers have bad experiences, that translates into legions of angry people. Research has clearly shown that a little more than half of customers who have a single poor experience with a company will leave for a competitor. When they face multiple negative interactions, customer churn skyrockets to 73 percent.

Not convinced? Here are some other data points about customer sentiment that illustrate just how important it is to create positive experiences for customers.

• Nearly half of all customers say their stress levels have increased over the past year

• 55 percent feel increasingly stressed

• 52 percent state that interacting with support leaves them feeling exhausted

• 60 percent of consumers buy products based on the support they expect to receive

Once a company truly grasps how important it is to track customer sentiment, it’s vitally important to engage in customer sentiment analysis, which we’ll explore next.

What is customer sentiment analysis?

Customer sentiment analysis is when a company uses automation to examine feedback left by customers in surveys, social media posts, and so on. The process employs machine learning to analyze huge amounts of customer data, scanning interactions to identify negative, positive, and neutral language to gauge how consumers view your business and level of support.

Customer sentiment analysis helps businesses triage tickets, ensuring that support requests go to the right agents, and it can also group tickets by type. Meanwhile, this analysis can provide an overview of the types of tickets being submitted, which helps support teams identify common issues.

What is a customer sentiment score?

Once you’ve measured and analyzed customer sentiment, it’s helpful to put that information into context so you can act on it. This is where a customer sentiment score can help.

The way most customer sentiment scores are generated comes from algorithms that measure phrases and words and then assign values to those events. Adding up those measurements leads to an overall score, which can be expressed on a range of one to 100, for example (with one being very positive and 100 hopping mad).

The resulting score plays a big role in triage. For example, it can help route tickets to more experienced agents, ones who have solid soft skills and proven experience defusing tense help desk interactions. It can also serve as a warning—if you’re seeing a large number of negative customer sentiment scores, it could point to a recurring problem with your products or services.

Benefits and use cases of customer sentiment analysis

Here’s an eye-opening statistic: Zendesk research discovered that two-thirds of consumers will become repeat customers if the business takes their feelings into consideration. Customer sentiment analysis use cases extended beyond creating return customers, however. Here are some of the many use cases for customer sentiment analysis:

• Measuring the efficacy of recent marketing efforts

• To gain a deeper understanding of customer demographics by age, region, etc.

• If customer satisfaction drops, it can help you identify and target the root causes

• Track down customers who harbor very negative views of your business for remediation

There are significant benefits to customer sentiment analysis, including reduced customer churn (given the costs of customer acquisition, this is an especially powerful benefit), more cross-selling and up-selling opportunities, and the process can even help chatbots perform better. And if negative customer sentiment seems to result from interactions with specific agents, support team leaders can use the analysis to re-train employees.

Customer sentiment analysis can help your support team do better

If you’ve ever seen the movie Office Space, the phrase, “Hey Peter, what’s happening” likely triggers your own memories of overbearing bosses. But what if your support agents are using language that, unbeknownst to them, makes your customers want to crawl under their desks (or tables)? Customer sentiment analysis can help you ferret out language your customers find off-putting, whether that language comes naturally or is built into macros agents use to in email and text exchanges.

Here are some other ways customer sentiment analysis can help your support team improve its performance:

• It can alert a support team that a customer is experiencing stress or anger, which leads to better prepared agents

• Improves efficiency through informed routing, which can lead to faster resolution times

• Can help reduce customer churn

• Provides opportunities for personalization, a key demand of customers

• Relieves stress placed on agents, which means better employee retention and morale

Customer sentiment can guide improvements to products and services

User sentiment can also show you ways to improve your products and services. For example, if your customer sentiment analysis points toward consumer anger about your ecommerce site’s shopping cart, you can share that data with your product team so bug fixes and user enhancements can be prioritized.

That’s a powerful reason to engage in customer sentiment analysis, because consumer emotions aren’t just something the support team needs to handle. Customer sentiment touches every aspect of your business, from how your product works to the overall view of your brand. If you listen closely, customers will tell you—implicitly or simply via their anger—what’s working and what’s not meeting their expectations.

How to measure customer sentiment

If this all seems overwhelming, take a breath. There are a host of ways that your business can track and analyze customer sentiment. That said, the question of how to measure customer sentiment can’t be answered with a single approach.

Customer service support software

For example, Zendesk’s Support Suite includes the Intelligent Triage and Smart Assist features, which employs machine learning to help companies measure sentiment. A key component of customer service support software is the ability to also intelligently route tickets to the best-equipped agents.

Social media monitoring

While this can be done manually, using customer service support software can be quite helpful here. Your software should allow agents to convert social media conversation into tickets, which then can then undergo customer sentiment analysis.

Elicit direct feedback

This is a tried and true method of gauging customer sentiment. This includes in-depth surveys, comment fields, or the classic metric used in customer support: CSAT.

The key is to not just rely on one input for customer sentiment. It’s important to seek out information in a variety of ways so your business can put together a complete picture of how your customers feel about your brand, products, and support efforts.

Gauge whether your customers would recommend your product

As mentioned earlier in this article, NPS is a simple way to understand customer sentiment. It asks a straightforward question: how likely is it that you would recommend this business to a friend or colleague?

Tips on creating actionable insights with your customer sentiment data

Tracking and analyzing customer sentiment is all well and good, but the effort your business spends on this process will be wasted if all that data isn’t put to use. You need actionable insights that will provide a clear roadmap of how to fix the issues with your product or service. That said, not all customer sentiment data will point toward problems—it might even prove that your plans were well-formed and successful.

Here are some tips for creating actionable insights with customer sentiment data:

Map problems revealed by the data. As you analyze your customer sentiment data, look for every type of issue that’s plaguing your customers. As Zendesk’s research has revealed, customer sentiment data can be narrowed down to about 20 issues. By conducting this exercise, you’ll be able to prioritize the fires that need to be put out immediately and which ones are outliers.

Sort the data by customer type. This too can be a productive way to sort data into actionable insights. Are newer customers showing the least amount of patience with your company? Are long-term consumers starting to complain? Can you break the information down into demographics such as age, gender, and region? All of that can provide insights that will help you target what’s working and what’s not meeting customer expectations.

Examine data over time. By taking customer sentiment data and looking at it over time, you can plot where spikes in negative (or positive) sentiment occur. That can reveal whether a spike occurred right after a new product launch, a brand redesign, or significant changes to your support organization.

Customer sentiment and the move toward immersive CX

As discovered in its 2023 Zendesk Customer Experience Trends report, consumers increasingly expect immersive experiences that are conversational and personalized. They want businesses to use the large amount of personal data they control to provide better products and services, and that includes how they’re feeling about the companies they patronize.

As the marketplace becomes increasingly competitive, the differentiator will be the customer experience. Companies that want to stay ahead of the competition and grow revenue must make customer sentiment analysis a key component of their business strategy.

Zendesk AI is an intelligent layer that sits on top of your CX solution and generates insights to make your business more efficient. The combined power of AI with human support helps customer service teams work smarter. For example, intelligent triage automatically classifies and categorizes incoming customer conversations based on their intent, sentiment, and language. This allows support teams to automatically prioritize and route those issues to the right agent. Zendesk AI also integrates with your CRM to gather the context from customer conversations, so agents have all the relevant information at their fingertips to jump in and help.

Learn more about Zendesk AI.

Net Promoter and NPS are registered U.S. trademarks, and Net Promoter Score and Net Promoter System are service marks, of Bain & Company, Inc., Satmetrix Systems, Inc. and Fred Reichheld.