Feedback is a gift for businesses. Most of the time, unhappy customers churn without bothering to complain to customer support, and happy customers rarely take the time to rave about their experience. Unless companies know what their customers think about their products or customer service, they can’t reduce pain points or lean into features that customers love.
So when companies do receive feedback, it’s crucial to listen and act, but analyzing unstructured data like feedback can be more challenging than it seems. Combining different feedback sources, understanding context, and interpreting huge data sets can be difficult.
Sentiment analysis can help companies make sense of all these variables. It’s a useful tool for any business looking to understand their customers better, and improve their products or services accordingly. By understanding how customers feel about your brand, you can craft more effective strategies for engagement and retention.
Let's dive deeper into what sentiment analysis is and how it can help your business.
Sentiment analysis relies on natural language processing (NLP) techniques to analyze text data such as reviews, comments, tweets, and surveys. The goal of this type of analysis is to determine whether a piece of text expresses positive or negative feelings towards a company.
By using NLP algorithms, businesses can classify the sentiment of each piece of text data into broad categories, such as positive, neutral, or negative. This provides an accurate picture of customer sentiment that can be used to inform decision-making within the organization.
The most popular applications include analyzing chat bot interactions, customer support (via email or phone calls), and social media monitoring. For example, if a business generates one hundred comments on a social media post advertising one of its products, a sentiment analysis tool will categorize the comments based on feelings about the product. This allows the company to gain a high-level understanding of how its target audience views a product, without having to sift through individual comments.
However, determining consumer sentiment can be tricky when context is overlooked.
Let’s say your company rolled out a new mobile application, and advertised it via a post on social media.
One comment says the following: “The app crashed right after I filled out the form. Incredible.”
While another one says this: “The sleek design of the new app is incredible!”
Clearly, these two commenters are expressing different sentiments. Even though they both use the word “incredible,” the first one is using it sarcastically and is unhappy with the new app.
Determining sentiment in context is critical for companies to truly understand how their customers feel about a product or service. Losing context becomes especially problematic when attempting to analyze large amounts of data, because it can lead to inaccurate results that don’t provide any real insight into customer sentiment. If companies are relying on inaccurate conclusions when making decisions about products or services, they run the risk of making poor decisions.
Understanding your customers requires true analysis that runs much deeper than word clouds. The best sentiment analysis tools are able to take context into consideration when categorizing customer feedback, rather than simply relying on positive or negative words.
In today’s fast-paced business environment, making decisions quickly is essential. Companies can use sentiment analysis to gain real-time insights into customer sentiment and then adjust their strategies accordingly. This could include identifying areas where customers are having a positive or negative experience, targeting specific demographics with tailored messaging, or even improving customer service by responding more quickly to complaints. Additionally, sentiment analysis can be used as a predictive tool for forecasting upcoming trends and anticipating future customer needs.
Tracking interactions with customer service agents is a common use case for sentiment analysis. By observing customer sentiment during interactions with agents, companies can measure agent performance and identify areas for improvement in customer service protocols. This helps provide better customer service experiences that are tailored to each individual customer's needs and desires.
Another way businesses can use this tool to their advantage is through product development. By analyzing customer feedback on existing products and services, product managers can get a better understanding of what customers like or dislike about their products. This helps them make informed decisions when it comes to product development and improvements.
Increasing customer satisfaction and loyalty is something all businesses strive for. By leveraging sentiment analysis tools to better understand their customers, and tailor their offerings accordingly, companies can achieve this goal. Plus, it enables them to respond more quickly and efficiently to any potential issues as they arise.
Sentiment analysis is an invaluable tool for any business looking to understand their customers better and improve their products or services accordingly. By leveraging NLP algorithms to analyze customer feedback across various channels, you can get a comprehensive view of how people feel about your brand and use those insights to inform decision-making within the organization.
Want to see how Viable’s AI-powered tools can get you a deeper understanding of your customers? Try out Viable’s Generative Analysis.