Is your business overwhelmed by copious amounts of customer feedback received on a daily basis? ‍

Today, it is easier than ever for customers to communicate with businesses. In fact, a study conducted by Statista in 2020, revealed that 58 percent of Americans surveyed had contacted some form of customer service over the prior one month period.

This is due to the wide variety of communication channels like email, social media, webform, and ticketing systems that have made interacting with businesses dramatically more convenient. To handle this demand, according to Forrester, nearly half of global contact center decision makers project their contact centers to grow 5-10 percent in the next year, and another 14 percent project growth of more than 10 percent.

Thus, it is important to ensure your customer service system is designed to effectively and efficiently accommodate the increasing demand while accumulating customer feedback in a way that provides value to the organization.

Unstructured Data

Due to the increase in customer communication, businesses are accumulating massive amounts of unstructured data such as email and webform messages. In fact, about 90 percent of the world’s data is unstructured. In addition, the volume of unstructured data is expected to grow at a rate of 55-65 percent each year. Fortunately, this increase in data can be used to help meet customer expectations more effectively.

According to Microsoft, 52 percent of respondents around the globe believe that companies need to take action on feedback provided by their customers. Despite this, only 10 percent of companies use this communication from customers to change their processes and improve customer experience. On the other hand, according to Gartner’s research, companies that successfully implement customer experience projects begin by focusing on how they collect and analyze customer feedback.

So, how do you go about analyzing unstructured data in order to benefit from it? With the help of sentiment analysis. Customer sentiment analysis allows you to quantify subjective information in a way that is valuable to your business.

What is Customer Sentiment Analysis?

Sentiment analysis is an AI based feature that uses natural language processing (NLP) technology to determine the emotional tone behind a series of words. By detecting emotions in text, customer sentiment analysis can gauge how upset a customer is over a written message.

For example, customer sentiment analysis measures subjective information like opinions, emotions, and attitudes to determine whether data is positive, negative, or neutral. Based on the emotional tone of the text, the algorithm will assign it a “score”. The score of a text is usually determined by placing it on an emotion spectrum that goes between 0 (positive) and 100 (negative). This allows machines to distinguish between a variety of emotional states. ‍

Customer sentiment analysis is essentially a tool for mining customer opinions that helps customer service managers to quickly understand overall trends in customer satisfaction. This information enables customer service teams to identify areas where improvements are needed. In addition, this gives agents the opportunity to improve their interactions with customers in qualitative ways such as by responding in a more meaningful manner and showing greater empathy. In this way, sentiment analysis can improve customer loyalty and retention by improving the customer experience.

Top Use Cases For Customer Sentiment Analysis

Customer sentiment analysis is an essential tool to discover how customers feel about your brand, product, service, or idea based on their feedback so that action can be taken to improve customer experience. For your businesses, this information can be just as valuable as the feedback you would get from customers who participate in surveys or questionnaires.

Today, there is an abundant amount of data that can be subject to sentiment analysis. If done properly, it can show your business the gaps that you need to fill to meet customer expectations. While there are many customer sentiment analysis applications, here are some of the top use cases in customer service:‍

Routing Consumers to the Right Resolution Channel

This is the most common use case for customer sentiment analysis. Identifying the client sentiment based on the customer's written communication allows quick routing of that customer to a higher place in the queue or to a trained customer service agent that is equipped to handle such cases. Thus, sentiment analysis helps to determine the next best action for the customer. ‍

Identifying Friction Points

Data can be used to identify shortcomings in the customer journey. By collecting and monitoring data, customer sentiment analysis can help to identify what cases are upsetting customers the most. Organizations can then utilize this information to fix those issues and improve the overall customer experience. ‍

Uncovering Agent Challenges

Sentiment analysis helps businesses understand how consumers feel about their interactions with customer service agents. This information can help uncover areas where customer service agents lack knowledge so training can be modified to better equip agents with the resources they need.

Understanding customer feelings has never been more important than it is today. Due to the recent pandemic, emotions are running high and stress levels have increased. The ability to recognize negative customer emotions early in the resolution process is critical when trying to proactively address customer concerns before they spiral beyond repair.

To that end, it is important for customer service teams to prioritize empathy and understanding as well as efficiency and effectiveness to meet customer expectations.

Below is an example of how your business can apply customer sentiment analysis:

Suppose an angry customer sends an email complaining about a negative experience they had with your company. The NLP machine learning model will analyze the text using an algorithm to perform sentiment analysis. Using the email from the customer, the algorithm detects the customer’s emotional state.

This is done with a set of rules, machine learning techniques, or both. Since the customer feels angry, predetermined business rules related to this client sentiment equip the customer service agent with the appropriate response. Due to the customer sentiment being relatively high in terms of angriness and urgency, the case would be flagged as high priority which will trigger a customer service representative to take immediate action.

Finally, since the customer service agent has been made aware of the client sentiment, more empathetic responses will be generated in order to provide a satisfying resolution for the customer.

This is just a brief example of how customer sentiment analysis can improve customer experience. It is important to note that with the support of NLP and machine learning, customer service agents are able to better understand and serve their customers.

Y Meadows’ Sentiment Analysis

Y Meadows’ software utilizes customer sentiment analysis to classify customer queries within a range of emotional intensities. This feature is based on a scale of 0-100. A score of 60 represents neutral emotion while a score of 100 constitutes furious. In addition, it has the ability to classify messages across a range of intensities. For example, if a dissatisfied customer emails your support team, the sentiment analysis tool would be able to determine whether the customer is upset, angry, or even outraged.

Properly dealing with angry or upset customers is an essential skill for customer support agents. But, before being able to properly handle a situation with a disgruntled customer, your agents must be able to assess the customer’s feelings in order to successfully meet their needs.

Y Meadows’ sentiment analysis employs a NLP machine learning model to improve customer experience. This empowers customer service agents with the right information at the right time to ensure a meaningful and empathetic response.

Benefits of Customer Sentiment Analysis

While sentiment analysis can be used for a variety of purposes, one of the most important advantages comes from its ability to sort large amounts of data based on emotional intensity in an effective and efficient manner. At a high level, this allows businesses to monitor trends and help detect changes in overall opinions toward your brand overtime.

This insight can help companies improve the most triggering issues for customers by identifying which cases make customers the angriest. At the customer service level, this allows the customer service team to prioritize critical cases and to prepare the appropriate tone of their response prior to interacting with the customer.

Final Thoughts

With the help of Y Meadows’ NLP machine learning technology, your enterprise can derive meaning from incoming written communications. Y Meadows’ sentiment analysis can help determine the emotional tone of the message. By doing so, your enterprise can make sense of subjective information in a way that is valuable to your business.

Moreover, customer sentiment analysis allows you to obtain value from unstructured data that otherwise would go unused. The ability to analyze large volumes of unstructured data, will help you to maximize the benefits of your efforts to provide a better customer experience. Without the help of sentiment analysis, customer service agents are at a disadvantage at trying to meet, let alone exceed, customer expectations.

Ultimately, customer sentiment analysis allows you to gain new insights with respect to your customer service department, have a better understanding of your customers, and empowers your customer service team to be more effective, successful and productive.