Improve Customer Service using Sentiment Analysis

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Whether they indicate negative or positive sentiments, interactions with customers can be used to the good of the company. By personally contacting your customers, you can collect feedback and customer sentiment data that cannot be collected in any other way. Analysing customer sentiment helps to understand customers better, empathise with them and improve the perception of the product.    

By listening to clients, you understand their feelings and the reasons for their assessment or feelings. With this deep understanding of customer behaviour, you improve your overall customer experience.    

Therefore, whether you are launching a marketing campaign, changing a pricing structure, or launching a new product, sentiment tracking can help you track customer reactions, making Customer Service an essential aspect of any business.

The digital age has increased the number of places where customers can express their experiences, frustrations, and comments. This requires companies to go much further than a traditional call centre to maintain customer relationships. 80% of customers would switch to a competitor after two or more bad experiences. Therefore, the sooner you react, the better chance you have of resolving the problem.

However, the sheer amount of data available may prevent or deter you from going through each comment.  Sentiment analysis allows you to identify the negative comments quickly and why the customers have issues with your product or service and respond to them. It can be used to ease your workload.

 Using customer sentiment analysis can help you determine customer sentiment related to your products and services. By exporting customer responses to a sentiment analysis tool, you can quickly determine how happy/neutral/sad your customers are based on the services you provide.

Customer Sentiment Analysis helps companies benefit from product reviews, social media, NPS responses, and other data and use that information to make smarter decisions that increase customer satisfaction. Customer Sentiment Analysis is a machine learning technique that involves breaking down a customer’s response into compound words, assigning a number to words of a similar nature to reflect the positive, negative, or neutral sound of that word, and then aggregating the ratings for each word to get an overall sentiment score. Sentiment analysis scores serve as a valuable metric for assessing the overall opinion of a company’s products or services.    

When comparing sentiment analysis scores across specific market segments, companies can quickly identify common pain points, areas of better customer service, and overall satisfaction across product or service lines. In brand reputation applications, the general trend of sentiment analysis enables brands to identify the peaks and valleys of changes in overall brand sentiment or attitudes toward products or services, allowing the company to respond to customer requests for improvement quickly. The company can correctly identify negative and positive ratings and translate the results into overall improved customer service with the right resources.    

Using sentiment analysis tools allows customer service agents to contact the customers with the worst opinions first and try to resolve the bad situation as quickly as possible. When it comes to more positive reviews, they enable the company to understand which behaviours will generate positive emotions in customers and serve as guidelines for the future. When a company focuses on actionable metrics (such as customer sentiment) to improve the customer experience, it can also improve CSAT and NPS.    

Likewise, optimising the premium user experience at other stages such as purchase, use, customer service, and loyalty rewards brands with more customer loyalty if brands actively use data to gain deeper insights. When companies better understand their customers’ emotional cues – what makes them happy, anxious, unhappy, or indifferent – they can use that data to make smarter business decisions or fix bad ones to increase customer loyalty.    

An organisation that gains a comprehensive, holistic, and actionable view of its customers and uses customer sentiment analysis to understand their feelings can create an empathic experience that will increase loyalty, retention, and repeat sales. When an organisation determines how to identify positive and negative sentiments in customer expressions, it can improve its engagement. By studying historical data about customer interactions and experiences, a company can predict future actions and customer behaviour and ensure that those actions and behaviour are positive.    

In addition to identifying customer emotions, you will reap real benefits if you also use machine learning to understand the context. The use of sentiment analysis in the interaction between the customer and the service desk agent can help identify gaps in the service received by the customer. Sentiment analysis can help you understand the root causes of customers’ negative reviews of products and services. For example, real-time tracking of customer sentiment on social media can help you immediately identify key issues affecting customers and take immediate action.    

By collecting all the tweets and direct messages that mention your brand, you can comprehensively analyse customer sentiment. By continuously tracking brand mentions on social media and streaming them in text mining algorithms, a general sentiment index can be built to measure customer perception and brand/product reputation on social media.

Kommon Poll allows users to track online customer sentiment in social media and large scale traditional media and analyses the sentiment of each of these mentions. Using complex filters, you can also segment sentiment by sources and get an overall sentiment score for your keyword. It doesn’t end there! We also provide Named Entity Recognition, and other mention analysis features to identify relationships between various mentions better.

Try out Kommon Poll now with a free trial!


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