Why is Customer Sentiment Analysis Crucial to your Business

“In a world where products and services are becoming more and more commoditized, customer experience is the only true differentiator.” 

Customer expectations are changing every day. They want instant resolutions, personalized support, and for businesses to be available on all support platforms. Similar to how people show emotions towards fellow human beings, customers also have emotions towards your products or services. Anger, happiness, rage, excitement are a few to name that your customers feel towards your business. 

A study by Deloitte showed that customers are willing to spend 140% more after having a positive customer experience. By keeping track of customer emotions, you can make data-driven decisions to boost customer satisfaction and customer loyalty. But where do you collect data on customer emotions, and why is it essential for your business? 

Keep reading to get your answers. 

What is customer sentiment? 

Customer sentiment is a Key Performance Indicator (KPI) that indicates customers’ emotions towards your product, service, or brand in general. This emotion ranges from positive sentiment to negative to neutral. Customer sentiment is quite different from Customer Satisfaction (CSAT) and Net Promoter Score (NPS). Both these are quantitative measurements, whereas customer sentiment is a qualitative measurement. 

Measuring these customer emotions is called customer sentiment analysis. To be more precise, customer sentiment analysis refers to using Natural Language Processing (NLP) or specific algorithms to detect customer emotions when interacting with your brand. The data gathered from customer sentiment analysis is crucial for customer support and also to enhance your sales and marketing campaigns. 

What are the different mediums to track customer sentiment? 

1. Social Media

Social media platforms like Twitter, Facebook, and Instagram have evolved from being a platform for just personal interactions to customer interactions. People are very expressive on these social media platforms, and it is now extensively being used by customers to connect with businesses. By keeping track of your customer comments on these platforms, you can understand your customer’s emotions. 

Customer social review Glossier customer feedback 

But it might be overwhelming to keep track of social media comments and brand mentions. Freshdesk Social Signals uses artificial intelligence to find relevant tweets and notify you, thus cutting through the noise on your Twitter account. 

2. Reviews 

Product reviews are available on websites – both your own and dedicated review websites and app stores. They can be framed as a 5-star rating scale or a product/service recommendation question. This review helps understand how your customers feel towards your product or if they would recommend your product/service to others. These reviews play a significant role in influencing the purchase decisions of your visitors and potential leads. So it is crucial to keep track of these customer reviews and also reply to them. You can thank your customers for their positive reviews and never forget to respond to your negative reviews. 

Freshdesk Messaging review

Freshchat review on Capterra 

3. Customer Feedback 

Here customer feedback refers to the feedback that you collect directly from the customers. This can be collected after a support conversation or can also be sent out as surveys or forms. It can be collected on a 5 or 10 point rating scale similar to a NPS with more detailed questions or as written feedback. This feedback offers valuable data which you can leverage to enhance your products and services. 

Chat support review

Freshchat customer feedback 

What does customer sentiment mean for customer service? 

Sentiment analysis is especially beneficial for support conversations, where the business is dependent on a high level of customer satisfaction. Modern customer service solutions are now able to extract crucial learnings from the data received through tickets daily and provide teams actionable insights through advanced analytics. They can even categorize and process this information to provide real-time suggestions and solutions to agents and supervisors.

Through sentiment analysis, data received from various departments in the company can be compiled and made sense of as a whole instead of remaining disjointed and disorganized. Below are some of the best examples of the ways customer sentiment analysis can strengthen your customer support workforce.

1. Resolve customer queries in real-time 

Sentiment analysis can help identify distressed or unhappy customers. By combing through the available information from conversations with the customer, the support team can develop suggestions and solutions based on customer trends and previous interactions.

2. Personalize your responses 

Nearly 70% of the customers want personalized communication from customers. Sentiment analysis provides information on customers’ expectations and previous reactions, which helps the agent personalize their responses and provide a better customer experience. This can significantly boost customer satisfaction and loyalty to the company. 

3. Round the clock customer engagement 

Not all companies can afford to hire hundreds of agents to respond to all incoming conversations 24/7. Enter sentiment analysis and artificial intelligence, which allows businesses to run non-stop without hiring more people. 

Chatbots are no longer limited to automated and generic responses. They come with artificial intelligence that can analyze customer sentiment. It senses the intent and tone of the customer to offer contextual and precise information while the agent is not around. With every conversation, the bot will understand the mood of the customer and will answer accordingly. If the customer were to use harsh words that detect anger, it will directly hand over the conversation to an agent.

4. Data-driven support conversations 

Sentiment analysis can help you understand which agents have had the most positive customer interactions and collate their conversations. Your support teams can leverage this information to improve or alter under-performing chat scripts. Furthermore, sentiment analysis can monitor the way employees deliver their chat scripts by detecting if they are using the correct tone, intonation, and choice of words. This sentiment data is invaluable in improving your customer experience. 

What does customer sentiment mean for businesses?  

Businesses that have adopted sentiment analysis have seen an immense improvement in the quality of their customer service. Here are a few benefits customer sentiment analysis data offers to your business. 

Gain audience insights: Customer Sentiment Analysis gives you a better understanding of your customer emotions, which will help you offer a better customer service experience. The feedback from your customers will also help your product teams to upgrade products based on customer needs. 

Build brand reputation: Customer sentiment analysis provides an overall view of how customers feel about your business. The reviews available on public platforms like product reviews and social media contribute to your online reputation. Understanding the customer emotions available on these forums will help you align your messaging and build your brand image. 

Enhance your products and services: Understanding your customer sentiments gives you a better understanding of how your customers feel towards your products and services. You can bring new feature releases, upgrades, or other improvements to your offering from this data. 

How live chat helps understand customer sentiment 

1. Collect instant customer feedback and reviews

Live chat facilitates real-time customer interaction, which helps you understand customer emotions while they interact with you. You can collect feedback from customers after you end a support conversation or resolve an incoming ticket. With live chat, you can also proactively reach out to understand customers’ feelings towards your support or product experience. This will also give you data on the negative experiences which need to be addressed and help you plan your retention strategies. 

2. Deploy a chatbot for interactive customer feedback 

Chatbots are now being increasingly used in customer support. These smart chatbots powered with AI and machine learning can mimic human conversations and engage in small talk with customers. You can deploy chat to collect feedback from customers interactively instead of making them fill out a feedback or survey form. This data can be used to train your chatbots and improve with each customer interaction. 

Freshchat chatbots can provide instant resolutions to customer queries through intelligent automation. Instead of sending out scripted robotic answers, they can engage with customers and bring in happy customer advocates for your brand. 

3. Monitor your support tickets 

Your support tickets are one of the primary sources where your customers raise their issues and concerns. By monitoring your incoming support tickets, you can get insights into your customers’ real issues with your products and services. With Freshchat, you can keep track of your customer queries coming in from your in-app support, website, and messaging channels, including WhatsApp, Facebook Messenger, and Apple Business Chat. This will give you centralized data on your customer sentiment. 

Final thoughts 

Overall, there is no doubt that sentiment analysis is the future of the customer support workforce. The vital role agents play cannot be underestimated, and the insights that sentiment analysis can provide will reinforce their skills and interactions. Businesses dependent on customer satisfaction who haven’t invested in reliable sentiment analysis programs should integrate them into their marketing strategies as soon as they can. Companies who do this will have improved operations, loyal customers, effective agents, and a seat at the forefront of the customer service industry.

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