The Importance of Sentiment Analysis in Understanding Customer Feedback

Contributing Author
8 min read
Dec 20, 2023
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Sentiment analysis, or opinion mining, uses natural language processing (NLP) and machine learning (ML) to determine the emotional tone behind online conversations. This is helpful for customer feedback.

Depending on the amount of data to be analyzed and the required level of accuracy, different algorithms can be implemented in sentiment analysis models.

Sentiment analysis algorithms are categorized into three systems:

In this post, we'll discuss using automatic systems to gather customer feedback and what makes sentiment analysis important in understanding customer feedback.

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What is sentiment analysis?

When branding, having a great product or service is not enough. You must collect customer feedback data to understand their emotions toward your business.

It allows you to gather customer feedback and understand your brand's impact. It shows the market perception of your products or services and identifies negative feedback.

While quantitative feedback in metrics like net promoter scores can provide information about brand performance, qualitative feedback in unstructured data can offer more nuanced insights into how people feel about your brand.

However, analyzing textual data can be a very time-consuming and challenging task.

Whether it's customer feedback received through channels like surveys or unsolicited feedback found on social media and online forums, it's difficult to comprehensively identify and integrate data on brand sentiment through manual processes alone.

Here is how NLP comes in handy.

Why is collecting customer feedback important?

Collecting customer feedback is vital for any business. It gives you an idea of how customers feel about your products or services.

It helps you understand what they like about your business and shows you how to improve, allowing you to keep loyal customers.

You need feedback to know whether you are providing excellent customer support, if you meet expectations, or if they are satisfied with your business.

You may not know if they are using your product as intended or want to continue doing business with you. Consider a useful tool like this to improve customer service.

Sentiment analysis provides companies with nuanced insights

Have you ever asked your friend, "How do I look?" and they say, "Fine"? That is a standard response, and you may have wanted honest feedback.

If they responded, "Your hair is nice, but those shoes don't match," you would have actionable insights on improving what already works. 

Those small details they mentioned can help your results, similar to collecting customer feedback and the nuanced insights that sentiment analysis can offer.

It provides companies with insights that go beyond superficial feedback.

A survey from 2020 found that 54% of companies recognized the significance of sentiment analysis and included it in their business strategy.

The companies evaluated social media and customer reviews to understand the sentiment towards their brand.

Bain & Company predicted a substantial increase in its adoption, with an estimated 81% of companies expected to incorporate sentiment analysis into their customer feedback strategy by 2023.

Benefits of sentiment analysis 

Collecting customer feedback is challenging. Some customers may be unwilling to fill out surveys or answer feedback calls.

In such cases, extracting insights from conversations that may not be considered direct feedback is essential.

Luckily, there is a way to analyze all customer conversations and gain valuable insights using sentiment analysis.

There are several benefits to using it to improve customer satisfaction. 

Brand sentiment analysis 

Brand sentiment analysis determines the overall feelings towards your brand, products, or services.

Businesses can use sentiment analysis to monitor their reputation metrics, such as sentiment score, net promoter score, customer satisfaction score, etc. 

Using analysis can help you understand your audience's most relevant feedback, which you can use to create a customer feedback loop used for: 

Improving prompt issue redressal and timely resolution can significantly enhance brand image.

You can scale these features with AI sentiment analysis tools, which can sift through a substantial amount of data in minutes.

It can be beneficial in crises where the need to address issues quickly is crucial.

It can improve customer service 

Customer support and business go hand in hand. Poor service can lead to loss of customers, while excellent service can result in more revenue and referrals.

Most companies now use sentiment analysis to understand customer feedback and needs, which can help them improve service.

Sentiment analysis helps businesses capture customers' voices, identify pain points, and address them to improve customer satisfaction and retention.

Implementing a customer-first mindset is essential to empathize with the customers' needs while enhancing a brand's reputation. Make unhappy customers happy!

For example, Airbnb uses an AI-based sentiment model and Net Promoter Score (NPS) to understand customer feedback better.

By combining NPS with sentiment analysis, Airbnb generates real-time customer insights to reach a wider audience than just relying on feedback forms.

It can help resolve cases to increase

Real-time sentiment analysis helps businesses quickly respond to customer feedback, making them more agile and flexible. Perfect assisting unhappy customers.

A significant problem associated with customer feedback is case backlog, which occurs when issues take too long to resolve and remain on the list of pending tasks.

Customers prioritize receiving help promptly, with 82% of them expecting responses in 10 minutes or less. Sentiment analysis can help with this problem as well.

Sentiment analysis tools can help you quickly identify the customer feedback of each support ticket, allowing you to shorten the resolution time.

How sentiment analysis helps strategic decision-making

Sentiment analysis can help make more strategic decisions by improving products, services, and communication strategies that help improve customer support and meet expectations.

It can help improve products

Product teams can use sentiment analysis to gain insights from unstructured data and improve their products.

Companies can identify differences between their offerings and the competition by analyzing customer feedback.

The data can help the company invest in improving its own app and website layout to meet customer expectations.

Sentiment analysis can detect defects in the current product.

For instance, if complaints arise about the checkout process, companies can quickly identify the reasons for poor reviews and assign the issue to the appropriate team to address the problem.

Personalized communications in customer feedback

Using sentiment analysis, you can provide excellent customer support and tailor communication to enhance their experience.

These tools use historical data to find dissatisfaction among buyers. It makes you aware of similar situations and takes steps to help potential customers.

For example, let's say that you operate an online store that sells various products.

Since you have a large customer base, you would like to provide a personalized experience to each individual.

Or maybe an unhappy customer leaves a bad review of a product they bought, saying it didn't match the description or meet their expectations.

Sentiment analysis can quickly identify the negative tone. This helps you talk to the customer and offer a solution, such as an exchange. 

It also shows that you focus on customer satisfaction and care about their experience, which can help you build a stronger relationship with them. 

Tips for implementing sentiment analysis tools

There is no universal approach to sentiment analysis in customer feedback. In this section, you'll learn a few steps to use sentiment analysis.

Always make a plan 

It's essential to have a plan before beginning and ensure that your measurement goals align with the company's business goals to provide actionable insights.

Identify the various scenarios where measuring customer sentiment is useful

Collecting omnichannel feedback

It helps you understand customer feedback and how it supports your goals and use cases. Since customers have different preferences for providing feedback, it's essential to consider as many channels as possible to ensure you don't miss any crucial input for sentiment analysis.

Keep in mind the importance of proper tagging in sentiment analysis.

Simply collecting data is insufficient to enhance customer experience in your contact or call center.

You need to analyze the data accurately. Tagging is a sentiment classification method that helps you classify customer feedback for better analysis and detailed insights. 

Don't overlook the importance of collecting direct customer feedback.

Although analysis tools offer faster and easier ways to gather and synthesize data across various channels, customer feedback surveys are still important.

Asking customers for their opinions, thoughts, and feelings about your brand, products, and service shows you value what they say. 

Take action using the information provided by the analysis.

You can do this by adding training and coaching sessions for customer service teams or sharing feedback on product reviews and usability data with business partners. The key is to implement the findings from the analysis over time to drive improvements.

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While customer feedback surveys are still helpful, sentiment analysis adds more benefits by allowing businesses to make strategic decisions and improve customer experience by finding the emotions and nuances in customer feedback.

It is more advanced than basic metrics, such as net promoter scores, as it reveals the genuine emotions and opinions behind customer feedback.

It also provides actionable insights, which help businesses identify areas for improvement, fix problems, and improve overall customer satisfaction.

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