What is Customer Sentiment Analysis and 7 Ways It Improves the Customer Experience
Chief Marketing Officer of SupportLogic
customer experiencecustomer sentiment analysis
We often discuss the value you can extract from unstructured customer data. But how do you use that data to improve the customer experience (CX) in immediate, tangible ways? The answer lies in customer sentiment analysis.
Customer sentiment analytics is a trending topic in the CX space, and for good reason. “Sentiment analysis,” Bain & Company recently reported, “is a powerful way to keep a pulse on customers and even the broader population to understand changing needs and anxieties, and new moments of truth.”
In today’s competitive marketplace, companies must leverage customer sentiment analysis to capture valuable, actionable customer insights that will improve the customer experience and benefit the entire business.
Why Customer Sentiment Analysis Benefits the Customer Experience
If you’re running a modern support team, you probably have a good amount of customer data on your hands. Your reps are connecting with customers frequently across so many touchpoints—from chat to video calls to emails—leaving the voice of the customer sitting in the discussion or transcripts, full of valuable information about your audience’s preferences.
But how do you use that customer data, the customer sentiment, to improve the customer experience (CX) in immediate, tangible ways? The answer lies in customer sentiment analysis.
Customer sentiment analytics is a trending topic in the CX space, and for good reason. “Sentiment analysis,” Bain & Company reports, “is a powerful way to keep a pulse on customers and even the broader population to understand changing needs and anxieties, and new moments of truth.”
With over 60% of customers reporting that they now have higher customer service standards, there’s no better time to leverage customer sentiment analysis. Learn how to capture valuable, actionable insights that will improve the customer experience and benefit your entire business.
What is customer sentiment analysis?
Customer sentiment analysis refers to the automated process of discovering and measuring how customers feel about your product, brand, or service.
There are several types of customer sentiment analysis:
Fine-grained analysis measures sentiment by determining the polarity score of words in a text, from very positive to very negative.
Emotion detection identifies and analyzes customers’ emotions and moods within the text. While fine-grained analysis measures whether the customer seems positive or negative, emotion detection labels specific emotions.
Aspect-based sentiment analysis breaks text into parts—phrases, sentences, and tokens—and measures the accompanying sentiment.
With customer sentiment analysis, businesses can capture customers’ feelings, understand their actions, and make informed decisions that improve customer experience.
The benefits of customer sentiment analysis
From personalized customer interactions to reducing escalations, customer sentiment analysis benefits support teams in a myriad of ways.
1. Predict and prevent escalations
According to a PwC survey, 80% of customers say that an essential element of positive customer experiences is “knowledgeable help and friendly service.” Customer sentiment analysis enables agents to provide this informed support—which, in turn, helps prevent escalations.
A tool like SupportLogic uses Natural Language Processing (NLP) and Artificial Intelligence (AI) to extract, analyze, and provide a statistical correlation of a customer’s real-time feelings. Is the customer frustrated or confused? Did they ask to speak to a manager about an unresolved issue that is likely to escalate? Is the customer happy with their support experience but has an issue with the product?
Customer sentiment analysis “takes the temperature” of a customer in real-time to help agents serve customers in the moment.
The business analytics platform Qlik used SupportLogic to conduct customer sentiment analysis, and they saw escalations reduce by 30% in just six months.
2. Resolve cases faster and reduce backlog
Customer sentiment analysis also helps agents tackle the dreaded case backlog.
By better capturing the topic and nuances of each support ticket, customer sentiment scores help streamline the case assignment process and reduce resolution time. The scores help managers route tickets to the right agent or agents, which ultimately leads to faster resolutions and smaller backlogs.
Customers care about speed when it comes to support. In a 2020 Zendesk survey, 72.5% of consumers said that a speedy resolution matters the most when they have an issue to resolve with a company.
Using an advanced analytics tool like SupportLogic’s SX Case Assignment can reduce resolution time by 25%. And the time-to-value for clients is fast. In fact, customers typically start seeing significant improvements with backlog management and case resolution in a matter of weeks—not months.
3. Reduce churn
Sentiment scores provide clear signals and alerts when a customer is angry, frustrated, or in danger of churning. With a tool like SX Case Assignment support teams can see every case attributed to a customer, so the whole support story is clear. An organization can immediately prioritize the ticket, better understand where a customer needs help, and send it to the right support engineer.
As a result of positive sentiment, Fivetran was able to increase CSAT from 90-95% in just six months and also saw a significant increase in their Net Promoter Score (NPS).
5. Improve products and services
A company needs to understand customer feelings about its products and services—not just now but over time. By continuously monitoring customer reactions, sentiment analysis helps track significant shifts in consumer behaviors and attitudes around products. That information can galvanize important business tweaks such as pricing adjustments, enhanced product launches, and better marketing strategies.
Customer sentiment analysis helps identify the root causes of customer issues when using a product or service, enabling support teams to both retain at-risk customers and prevent others from having similar negative experiences. If your real-time analytics reveal that customers are making the same recurring error, for instance, your engineering team can be alerted to fix the bug immediately. Similarly, if sentiment scores show user frustration with the software updating process, the support team could alert the engineering team to redesign the process.
This personalization pays off. According to a Gartner study on the impact of personalization, companies that “focus their personalized messaging around helping consumers can expect 16% more impact on commercial outcomes than those that don’t.”
Sentiment analysis provides support agents with context around individual customers—including past interactions, frustrations, and expectations—so they can customize communications accordingly.
Imagine a customer is having issues with the latest release of a cloud security application. The product isn’t working properly, and when they call customer service, they are greeted with:
“Hello, thanks for calling Acme. How may I help you?”
“Good afternoon, John. Glad you called! I see you’re having a software provisioning issue, and you’ve had no luck with the latest version. I’m looping in one of my engineers as we speak to get this resolved right now. I also see you have a question about renewal next month, so let me help clarify that while we’re on the phone.”
7. Improve agent training
If you think your support agents don’t need training, think again. According to Zendesk CX Trends Report 2022, 68% of customers think agents need more training and only 20% of agents say they are unsatisfied with their training. And less than 30% of agents feel empowered to do their work well.
Customer sentiment analytics provides important insights managers can use to coach agents and improve support team performance. Sentiment analysis can be used to identify positive or negative sentiments in customer interactions, explains Charles Monnett, Director of Data Engineering at SupportLogic. Using these insights, managers can prioritize feedback and provide training in the right areas.
Say an agent is often impatient with customers. A manager will be able to catch this problem when they look at the dashboard and see high negative sentiment from customers. When the manager looks through the tickets, they can spot signs like snapping at customers or hurrying them along, and they can then provide training to improve the skill.
8. Improve agent retention
Although 78% of senior leadership agrees that agents play a virtual role in customer service, 38% of agents say the customer service team is not treated as well as others in the organization. When employees don’t feel valued, employee engagement tends to drop, and turnover increases.
Sentiment analysis provides a way to track agent engagement and reduce employee churn.
“An AI analytics tool like Support Logic can analyze agent interactions with customers and can detect emotions like frustration and impatience, which could be a result of reduced engagement,” says Charles. It can also help managers identify agents’ skill matrices. With these actionable insights, you can make changes like assigning agents to the work that engages them.
How to measure customer sentiment
Customer sentiment is measured using Artificial Intelligence (AI) and Natural Language Processing (NLP) to extract actionable meaning from text. This text could be in the form of structured or unstructured data contained within support tickets, phone calls, feedback forms, social media, chatbots, and other channels.
The AI is trained to understand words and sentiments using either a lexicon-based approach or an ML model.
At SupportLogic, we use ML models because it’s more accurate than the lexicon-based approach. Rather than assessing sentiment from a set of rules, our models are always learning and evolving with your customers. The models can extract recurring words and assign attributes to them based on the order and structure of language.
Let’s say you integrate support experience software like SupportLogic into your ticketing system and you receive an email from a customer that reads:
We reported an outage 4 hours ago. This is a significant blocker for our team, and it took you 2 hours to escalate the issue. A lot of critical reporting tasks are being delayed, and we have only a few hours to remedy this problem or we’ll disappoint our clients.
We need this solved ASAP, and we’re waiting on you for the next steps to resolve. Your slow responses have made this the worst customer service experience I’ve ever had.
I’ve notified the head of product about the problem, and if this is not resolved soon, we’ll take our business elsewhere.
Our solution identifies and pulls out domain-specific keywords—the phrases customers frequently use in support interactions. It then determines the sentiment behind each word. The words “disappoint,” “dislike,” “outage,” and “delayed” all express negative sentiments.
SupportLogic SX also has up to 30 signal categories to give you deeper insight into customer problems. For example, the words “critical” and “disappoint our clients” fall under customer impact, while “take our business elsewhere” indicates churn risk. This intelligence helps you act on a ticket that should be prioritized immediately.
Customer sentiment analysis use cases
From customer service to market research, businesses use customer sentiment analysis in many ways:
Social media listening
People constantly express how they feel about brands on social media. This honest feedback is helpful, but it can also be damaging (consider a negative tweet that goes viral).
Sentiment analysis helps support teams find and gauge customer feelings on social media, so they can intervene before an issue spirals out of control.
These sentiment insights from social media are especially valuable because they can contain feelings you might not find in support tickets, Charles, Director of Data Engineering at SupportLogic, explains. Brands can gain context of how a customer—and people in general—perceive their products and services.
For example, a customer of a network service provider might send out a tweet after experiencing bad reception but might not bother to call support to complain about the issue. If the company is not paying attention to sentiments expressed on social media, it might miss an opportunity to learn what customers are thinking.
Discussion forum and review site
Forums and review websites often hold honest and unbiased customer opinions about your products/services. As Charles explains, using sentiment analysis to extract insights from discussion forums can help you “connect with and understand how some of your most devoted users are reacting to your products or what they think about your products.”
It can also show what issues customers face and knowledge gaps your support organization is not filling. If people are looking for, say, how to enable some kind of sophisticated workflow for your product, sentiment analysis can help you uncover a missed opportunity for revenue.
Or use sentiment analysis to analyze feedback on product review sites and reveal insights a cursory glance at a seemingly great review might not reveal. Let’s say a customer rates your tool and leaves a 5-star online review that goes like this:
Tool X has various great features and helps us detect bugs. It has improved the products we launched and made collecting user feedback so much easier. I recommend it to anyone who asks.
However, there has been some downtime, and some team members have had login issues.
While this review is mostly good, you could miss potential issues that would impact customer loyalty. You can avoid such mistakes with sentiment analysis.
When VoC programs such as NPS or CSAT surveys are used, there tends to be a fixation on quantitative answers because these are easy to measure, but you might be missing the bigger picture. Apply sentiment analysis to open-ended question responses to understand customers’ feelings and opinions.
Businesses can use sentiment analysis to serve customers better.
A sentiment analysis tool can detect negative sentiment from conversations as they happen in real-time and alert customer support teams to possible escalations. They are also useful for prioritizing cases and clearing out backlogs.
Businesses sometimes lose customers who “seem” satisfied. With sentiment analysis, you can detect early warning signs of customer churn and prevent it before it happens.
Sentiment analysis can help product teams improve the product. Insights from unstructured data can show gaps between your product and the competition. It can also detect features that customers want.
Product teams can also prioritize what bugs to handle based on customer feedback.
Get started now with customer sentiment analysis
In a highly competitive market where a buyer can churn at any time, real-time customer sentiment analytics is no longer a “nice-to-have.” It’s essential to gaining a deeper understanding of customers, driving brand loyalty, and empowering your company to compete and thrive far into the future.
Use SupportLogic SX to start tracking customer sentiment and find opportunities for increased customer satisfaction and greater business success.