How to Use Customer Sentiment Analysis to Grow Your Business

In face-to-face interactions with customers, you have the advantage of observing eye contact, tone of voice, body language, and other cues that go along with their spoken words.

But when you move to a world of digital or omnichannel customer support, the human language element goes away and it becomes much more difficult to gauge how customers really feel about our company, brand, products, and experiences.

For example, does “it works fine” and “customer support was ok” mean customers are happy, upset, or indifferent?

Measuring how happy (or unhappy) customers are with your brand, service, support, and products — also known as customer sentiment — is challenging in an omnichannel contact center. How do you “read the customer” in texts and chats? In addition, the myriad of customer interactions across dozens of platforms can leave you overwhelmed by information and unable to distill and act on feedback from your customers.

Despite its challenges, it’s critical that contact centers conduct customer sentiment analysis to gain a deeper understanding and meaningful insights into how their brand, products, and service delivery are perceived. Thankfully AI-driven tools are automating capabilities and processes that help contact center analysts and leaders identify tone, intent, and feelings that impact customer experiences and loyalty.

What is Customer Sentiment Analysis?

To better understand what is meant by customer sentiment analysis and why it’s useful, let’s break down some common industry terms.

Customer Sentiment

Customer sentiment, also referred to as user sentiment, is a qualitative measure of how customers feel and think about their brand based on their thoughts, opinions, and attitudes.

There are three types of sentiment:

1. Positive
2. Neutral
3. Negative

It’s well established that negative sentiment or bad experiences drive customers right out the door. On the flip side, according to a Zendesk CX 2023 Trends report, 70% of consumers spend more with companies that offer fluid, personalized, and seamless customer experiences.

So if positive sentiment matters greatly, the next question is how do you bring customer sentiment analysis, or emotion detection, into your contact center or improve upon it if it already exists?

Customer Sentiment Analysis

Customer sentiment analysis is a data-based, automated way of measuring positive, neutral, or negative feelings and feedback your customers have about your company, product, brand, and service across every interaction, every channel.

Part of natural language processing (NLP) and machine learning, automated sentiment analysis goes beyond simple praise or criticism to analyze huge amounts of data from various sources — emails, chats, text, social media, customer reviews, and other interactions to interpret attitudes and emotions. It assigns a sentiment score to these experiences, with a value ranging from +1 (extremely positive) to -1 (extremely negative) based on certain words or phrases customers might use.

The devil’s in the detail, or in this case, the sentiment, when it comes to analyzing customer interactions and extracting meaningful insights.

For example, an online review that says “I love my new laptop,” is easily interpreted as positive. But what about a review that uses slang like “This new laptop is straight fire” or a sarcastic “I love my new laptop — it’s so fun to wait two minutes for it to boot up”?

A simple sentiment analysis tool that’s programmed to only look for certain words or phrases might miss the point of the second and third examples. However, newer AI-powered customer sentiment analysis tools help your analysts with scoring to drive a more efficient scoring and feedback process by analyzing sentiment across the full conversation and reports at different intervals, but also allows contact center leaders and business managers to gain confidence in the results.

In the context of contact centers, leveraging customer sentiment analysis not only helps get a more accurate real-time “read” on customers but also allows agents to gain a better understanding of customer satisfaction levels, flag dissatisfied customers and identify specific pain points that can lead to product or service improvements.

Customer Sentiment Score

As mentioned above, a customer sentiment score is a value or number used to gauge customers’ opinions of a company’s customer service and products. With the help of artificial intelligence and through the use of algorithms, phases and words are measured and assigned values. Those values are then added up which leads to an overall score.

A positive sentiment score indicates exactly what it describes — customers are satisfied with their experience of the company’s product, brand, or service. A negative sentiment score indicates the opposite. And a neutral sentiment shows they are indifferent. 

Obviously companies strive for all positive sentiment scores, but in reality negative scores are also important for learning and improvement opportunities.

Why Do We Need Sentiment Analysis?

Delivering an outstanding customer experience (CX) has become mission-critical to businesses. By better understanding what customers need and want, companies are better able to create experiences that give them better control and confidence in the brand, products, and services.

The ability to analyze customer sentiment helps companies and service and support centers learn more about their customers. It can then be used to create plans and tactics to:

• Dig in deeper as to why customers feel a particular way
• Identify and close gaps in / provide feedback on products and service delivery
• Predict buying patterns and future purchases from existing and new customers
• Build loyalty and marketing programs

Benefits of Customer Sentiment Analysis

You’ve heard all of the statistics:

• It costs up to 7 times more to acquire a new customer than retain an existing one
• Loyal customers spend 67% more than new ones

And the list goes on.

For businesses and contact centers, measuring customer sentiment analysis (and acting upon the results) becomes a must have, offering a competitive advantage and many benefits.

1. Improves the customer experience
2. Drives customer loyalty and retention
3. Builds better brand reputation
4. Improves products and customer service delivery
5. Boosts agent retention and employee experience (EX)

Seven Ways to Improve Customer Sentiment Analysis

There’s not a one-size-fits all when it comes to how contact centers tackle customer sentiment analysis. The important part is to get started. We’ve curated a list of seven ways to improve customer sentiment.

1. Start with a plan – Before you jump in, you need a plan. Make sure your measurement goals align with the company’s business goals and provide actionable insights. 
2. Outline the use cases for measuring customer sentiment.
   • Are you gauging the effectiveness of marketing efforts and campaigns?
   • Is your company on a journey to improve overall CX or positive customer experiences in the contact center?
   • Are striving for insights for product improvements for your product team?
   • Is it a goal to reduce customer churn?
3. It’s important to collect omnichannel feedback and aggregate it in a way that gives you a clearer picture in support of your goals and use cases. Customers’ preferences for some feedback methods mean that if you only look at certain channels, you’ll miss key input for customer sentiment analysis. Gather data from as many sources as possible, including social media platforms, emails, customer surveys, positive comments, negative comments, etc. 
4. Don’t forget the importance of proper tagging in sentiment analysis. All the data-gathering in the world isn’t going to help your contact or call center improve customer experience if you can’t analyze it the right way. Tagging, an approach to sentiment classification, helps you categorize customer feedback for better analysis and in-depth insights. While manual tagging is fine for smaller customer support centers, growing contact centers will want to implement an automated tagging solution.
5. Don’t neglect soliciting direct feedback from customers. While analysis tools allow you to gather and synthesize omnichannel data faster and easier than ever before, it’s still important to directly ask customers their thoughts, feelings, and opinions on your brand, products and service delivery. Add online surveys, comments fields, and customer satisfaction surveys (CSAT) to your wheelhouse to provide more color to your sentiment analysis.
6. Act on the analysis! As mentioned above, have a plan to leverage these insights across the organization. Whether it’s providing training and coaching moments for customer service teams or sharing product reviews and usability data with business partners, the key is to act on the analysis to make improvements over a period of time.
7. Manual analysis has given way to automated customer sentiment analysis software tools — ideally driven by AI — that continue to learn the intricacies of how customers talk to your agents over time and how you’re meeting customer expectations. An AI solution can find similar qualitative feedback and tag it accordingly for faster analysis — and faster action and better business decisions. 

Leveraging Customer Sentiment Analysis to Boost Agent Retention and Productivity

As the phrase suggests, much of the conversation around customer sentiment analysis focuses on the customer — as it should. But there’s significant value to be had beyond the customer.

Contact center leaders have an opportunity to leverage customer sentiment analysis to refine the organization’s support strategy and execution.

1. Enhance agent training – Customer sentiment analysis provides important insights that managers can use to enhance agent knowledge and provide coaching opportunities to improve performance and ultimately boost CX and yield more positive experiences. Contact center leaders have an opportunity to turn negative experiences, unhappy customers, and sentiment insights into actionable coaching moments and training programs for agents.
2. Boost agent productivity – Contact center managers can glean deeper insights into opportunities to improve agent productivity. Are the right agents with the right skill sets assigned to the right customers? Are there opportunities to gain efficiencies through additional support channels?
3. Improve agent retention – Sentiment analysis provides a way to track agent engagement and reduce employee churn. When agents are frustrated and overwhelmed, customers can detect this, which is then reflected in sentiment. Contact center managers who focus on boosting agent engagement and improving EX by making customers happy will ultimately help the company deliver better CX. Happier agents stay and lead to happy customers.

Move Toward Better CX and EX

Want to move your organization toward delivering gold-standard CX and EX? The business benefits of customer sentiment analysis are clear. It can help you better understand — and act upon — the way customers perceive your company, brand, product and service as well as boosts agent productivity and retention.

The best time to jump in is now. The good news is that AI-powered software tools exist to help contact center analysts and leaders drive a more efficient, accurate, and valuable program.

Learn more about how Playvox AutoQA lets you get the full picture of what is driving customer interactions with your business and identify the underlying sentiment.