Published On: June 6th, 202410.6 min read

People will forget what you said, people will forget what you did, but people will never forget how you made them feel.

– Maya Angelou, American poet and activist

If this saying holds true, grasping how customers feel is crucial for business triumph. When a company doesn’t leave a positive mark, customers are likely to walk away. Studies indicate that acquiring a new customer costs six to seven times more than retaining an existing one, making understanding sentiment appear as a more cost-effective route to prosperity.

You can find out what customers think in various ways—like checking online reviews, seeing what people say on social media, reading news articles, looking at survey answers, or listening to calls to customer service. So, getting the info you want is pretty straightforward.

The real challenge comes in sifting through heaps of data and grasping what customers feel. Take recording calls and listening to every single one, for instance—that eats up a lot of time. This is where AI sentiment analysis tools come into play, making things much easier.

Today, let’s talk about why sentiment analysis matters in business nowadays, how it actually works, and the part that Artificial Intelligence plays in it. Ready to explore?

What is sentiment analysis?

Sentiment analysis is about figuring out how people feel about what they say or write. It helps understand if someone’s opinion is good, bad, or somewhere in between. You can use it for conversations or written messages. Basically, sentiment analysis looks at a bunch of words and decides if they’re positive, negative, or neutral. Here are a few examples:

I’m really liking my new software” — feeling good about it.

Just got a new software” — not really feeling anything in particular.

My software’s kinda slow at processing information” — not too happy about it.

Sentiments can range from super happy, happy, just okay, not so happy, to really not happy. 

Basically, what happens is we figure out how people feel from what they write, and then we look at the whole conversation to understand it better. Like, for example, take these two sentences:”

The dinner service was exceptional!

Exceptional! I’ve been waiting for a table for an hour, and it will still be a few more minutes.

In both instances, “exceptional” serves as the emotional keyword, yet the interpretation differs: the first statement conveys positivity, whereas the sarcastic context of the second statement implies a negative sentiment.

AI and sentiment analysis

Bet your customer service squad tackles over a hundred calls daily. Checking out customer vibes through call recordings? With that many calls flooding in every day, good luck keeping up with the flow.

Companies aiming for impartial insights into customer opinions favor using Natural Language Processing (NLP) technology. It helps them automate and accelerate the process while providing more dependable outcomes.

AI tools like these use special algorithms to figure out important words and check if they’re positive, negative, or neutral. They then match these words with a bunch of data to see how customers feel about stuff. If a customer is happy, it gets a +1. If they’re meh, it’s a 0. And if they’re unhappy, it’s a -1. These tools can even pick up on specific feelings like frustration or excitement, and tell how interested a customer is.

What is an example of sentiment analysis at work?

For instance, consider a women’s fashion brand that sells 100 units of a dress. By analyzing sentiments expressed in reviews and customer service calls, it becomes evident that customers particularly admire the dress’s length. This insight becomes invaluable when shaping the design of future collections.

How does sentiment analysis work?

Sentiment analysis tools typically operate through one of three methodologies to evaluate text:

1. Rule-based analysis 

It employs predefined libraries of scored keywords and manually configured parameters. Keywords are assigned scores, such as:

Great = 1

Very good = 0.9

Good = 0.7

Okay = 0

Bad = -0.5

Very bad = -0.8

Initially, the tool identifies the number of keywords present in the text. Subsequently, it computes a sentiment score for the content based on these established rules.

Setting up rule-based analysis is straightforward and provides small and medium-sized businesses (SMBs) with a solid overview of whether customers are feeling positive or negative. However, there’s a drawback: rule-based systems struggle with understanding sarcasm and metaphors, which can affect the accuracy of analytics. Take, for instance, a scenario where a customer says, “you’re killing it.” In this case, rule-based analysis might interpret it as negative because of the word “killing,” when in reality, it’s meant as a compliment!

2. Machine-learning analysis

In this method, sentiment analysis models use a list of words and learn which ones express positive or negative feelings. Once the model has learned enough, it can check out new sets of data. These deep-learning models are more complicated but better at guessing how people feel about something. 

People like this method because it can figure out if someone is being sarcastic or ironic, making its results more accurate. However, it’s not great for looking at small sets of data.

3. Hybrid analysis

This method blends together rules and machine-learning tricks. Sentiment analysis tools come equipped with a bunch of scored keywords in their database, and they keep learning and updating it as they process more data. Hybrid models give you the freedom to customize as you like, but they don’t need as much training.

What is sentiment analysis used for?

Sentiment analysis can be useful in many ways: 

  • Customer support management: Customer service teams often get swamped with loads of calls, leading to a bunch of them landing in voicemail. Instead of having to go through each voicemail to figure out what’s up, sentiment analysis tools can step in. They help spot problems and decide which ones need attention pronto. Plus, they can sort out tickets and send them to the right folks for action.
  • Navigate a potential PR crisis: When someone complains once, it’s usually not a big deal. But when a thousand customers start complaining about the same thing to different agents, it can turn into a major PR disaster. Using sentiment analysis tools on live calls can catch these situations early, letting companies step in and address the problem before it spirals out of control.
  • Measure customer perception of a product: Analyzing sentiment comes in handy when you’re trying to figure out how people feel about a new product in the market. You can sift through reviews and mentions of the brand to spot certain words and phrases that show the overall sentiment. This way, you can pick out what customers like about it and what areas could use some work.
  • Market research: Every brand needs to stay connected. By using sentiment analysis tools not just for your own brand, but also for keeping an eye on what people are saying about your competitors on social media or in surveys, you can learn a lot about how customers see these brands. This knowledge can help you tweak your communication to meet customer expectations better.
  • Performance assessment: AI sentiment analysis tools are useful for checking how well customer service agents are doing. By figuring out if customers are happy or not from the calls they handle, we can see if agents are solving problems effectively. Plus, by looking at the tone of messages and emails, we can understand how agents are talking to customers and find out if there are any issues slowing them down. All this info helps companies make better training plans and keep their employees engaged.

What are the benefits of sentiment analysis?

The benefits of sentiment analysis extend across various departments, offering notable advantages:

  • Easier identification of high performers: Just looking at basic call stats like duration isn’t enough to really judge how well agents are doing. A long call might mean they’re not great at fixing things, or it could show they’re really good at connecting with customers. Checking the sentiment of calls helps see if customers are happy or not. Agents who consistently make customers feel good should get recognized and rewarded. It’s a smart way to keep them motivated and inspire others to do their best.
  • Efficient quality control: Team leaders are swamped; they can’t tune in to every call their agents take. However by using tools that analyze customer sentiment from recorded calls and transcripts, they can spot negative experiences. This lets them know where to direct their attention and manage their time better.
  • Actionable insights into customer experiences: After chatting with customer service, some folks get asked to take a survey, but not everyone fills it out. This means businesses can’t always fully figure out how customers feel about their experience. AI can help small businesses dig deeper, analyzing each call to see how customers feel. It makes it easier for companies to spot where they need to improve. For instance, if a lot of people complain about waiting too long, it might be time to hire more folks to handle the calls.
  • Insights into product expectations: Even though customer service handles the calls, sentiment analysis tools give valuable insights to the product development team. For instance, let’s consider a furniture brand’s customer service. They often hear customers complain about “chipped polish.” This directs the product team’s attention to polish quality. By enhancing this aspect, they can make the product more attractive to customers, leading to increased conversion rates.
  • Easy tracking of market trends: To keep ahead of the competition, brands need to keep a close watch on what’s trending in the market. They can use surveys, research, and sentiment analysis to spot these trends early on. This helps them stay prepared and handle market shifts effectively.

If necessary, they can tweak their supply chain, adjust launch dates, or rework schedules as needed. By doing so, brands can predict how the market will react to new products or changes in their current offerings.

  • Optimization of resources: No matter how large your customer service team may be, there’s always a limit to the resources at your disposal. Time, too, is limited. Being quick to react to market trends or address customer concerns can greatly benefit your brand. Using sentiment analysis automates feedback collection, freeing up your agents to focus on other tasks, ultimately saving them time.
  • Unbiased brand analysis: When we chat, it’s not just the words we say, but also how we say them and what’s going on around us that shapes what we mean. How we see a message can vary depending on our own experiences and views.

Sometimes, we might read something like “Great service, but a bit pricey” and think it’s all negative because of the cost. But if we look closer, there’s actually a lot of good there too. Using AI tools to check the overall feeling can help us see beyond our own biases and focus on what really matters.

Choosing a Sentiment Analysis Tool

Selecting the right sentiment analysis tool is crucial for harnessing the advantages of automated text sentiment analysis tailored to your business needs. The market offers many options, each with its features and functionalities. Besides the monetary aspect, several factors merit consideration:

  1. User-Friendliness: Prioritize Natural Language Processing (NLP) tools that boast easy setup procedures and demand minimal training to unleash their capabilities. Additionally, assess their computational prowess, especially when dealing with sizable datasets.
  1. Analytical Breadth: Given the prevalence of Voice over Internet Protocol (VoIP) platforms in business communications, fundamental analytics are readily accessible. Your AI tool should complement this by furnishing a comprehensive perspective on service quality.
  1. Precision: The dynamism of human language introduces constant fluctuations, with new colloquialisms emerging regularly. Consequently, the NLP tool of your choice must adapt to evolving vocabularies and refine its analysis to accurately gauge customer sentiments.
  1. Compatibility: For optimal efficiency, seamless integration with your preferred communication platform is imperative. This streamlines the system, facilitating the automated analysis of every customer interaction.

On final note

Analyzing customer sentiment yields numerous advantages, providing insight into their perceptions of your product and enabling refinement of your offerings. Assessing sentiment towards competitors facilitates emulation of their successes and avoidance of their pitfalls.

Leveraging AI tools for sentiment analysis offers organizations an impartial and comprehensive understanding of customer perceptions towards the brand and its products. Machine learning holds potential benefits across all departments, from product development to customer service. Hence, early integration of the appropriate tool into your communication platform is paramount.

Interested in leveraging AI-based Sentiment Analysis for your business?

If you’re looking to integrate sentiment analysis tools into your business strategy, we’re here to help! At Nascenia, we specialize in developing custom AI solutions tailored to your needs. Get in touch with us to explore how we can empower your business with advanced sentiment analysis technology.

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