As this century progresses, businesses are discovering that the most incredible way to gain the best customer service is to know them deeply. With AI advancing at an exponential rate, it’s become possible for companies to use artificial intelligence (AI) to gain valuable insight into their customers. In particular, advances in artificial intelligence are leading to increased efficiency in customer service throughout different industry vertices.
Using Sentiment Analysis
Machine learning and AI-based interactive voice response systems have created a new paradigm for what customers and customer service agents can expect from these technologies. When applied correctly, artificial intelligence will enhance the customer experience in various ways, from identifying their interests through sentiment analysis to gathering data about their preferences.
AI is the production and display of intelligence by computers and machines instead of humans. Various forms of artificial intelligence exist. General AI and narrow AI are typically the two types of AI. It is the narrow AI that we see in our daily lives on computers and smartphones. In contrast to traditional computers, they are intelligent automated systems that perform specific tasks without the need for programming.
AI systems that recognize speech and voice, such as Siri, Alexa, and self-driving cars, or medical AI that scans MRI results are all examples of narrow AI. In contrast, general AI appears more frequently in movies, such as AI that can learn to perform anything humans do. Integrating Natural Language Processing (NLP) into e-commerce settings can help perform sentiment analysis on conversational texts enabling businesses to get a real insight into customers’/users’ online behavior.
There are broader applications for narrow AI in customer service than you might think. Customers are guided and assisted throughout their journey by this service. Furthermore, it can be embodied in chatbots, self-service, machine learning for analyzing customer data, natural language processing for voice recognition, and many other applications.
AI, Machine Learning, and Sentiment Analysis
AI branches like machine learning and deep learning actually learn from the data they analyze instead of relying on explicit predefined rules. As a result of being trained, machine learning algorithms produce models that comprehend the underlying data. The model will predict the outcome when fed data based on the data it was trained with. It is these models that enable Sentiment Analysis to be applied. Over time, machine learning models become more accurate when data is continuously fed to them.
The sentiment analysis process determines the text’s positive, negative, or neutral sentiment through an automated process. Companies use it to analyze text, such as surveys, customer support issues, online reviews, and live chats, in order to keep track of customer satisfaction. Social media monitoring, customer service analysis, and customer feedback analysis are among the many sentiment analysis uses. The use of machine learning software and natural language processing can be a useful method of sorting
Comprehending Voice of Customers with Sentiment Analysis
Analyzing customer feedback in order to improve your product, solution, or service is known as Voice of the Customer (VoC). Customer feedback systems and survey tools are typically used to measure customer satisfaction. Almost all companies recognize how vital customer sentiment analysis can be to their customer service initiatives. The data organizations need to better understand their customers’ satisfaction is still extracted using archaic methods.
The best approach to engage your customer is to focus on them and listen to what they have to say. By discovering recurring themes and topics that otherwise go unnoticed in data sources, the sentiment analysis solution uses the Text Analytics API to extract information. As a result, you can gain competitive intelligence, help make better product quality decisions, improve product segregations, and improve customer service.
Adapting your business to your market base is what ultimately determines a company’s success, so Voice of Customer (VOC) must be measured regularly. A Voice of the Customer method can provide valuable information about where an organization is doing a good job and where it needs some improvement.
Enhancing Customer Service through Sentiment Analysis
Your agents can use Sentiment Analysis to understand your customers’ current moods to take appropriate measures. Customers look for support when they have issues with the service they use or the products they use. if the customers are not served with the needful assistance on time, they may leave the online platform, forum, and social media channels with negative feedback. Sentiment analysis can help businesses take a sneak peek into the customers’ reactions to take wise decisions further to improve their customer service.
Customers these days have access to different channels, e.g., chats, calls, and emails, to get to their seller and service providers to resolve their problems regarding the brand’s services or products. An analysis of the sentiment of such interactions between the customer and support agent can provide insight into service gaps that the customer encounters.
To effectively assign the right case to the right agent, sentiment analysis can be used to evaluate nuances in support tickets.
It helps improve Customer Experience when they deal with challenging situations or angry customers with the right agent. This leads to a faster resolution to the issue and improved customer satisfaction with the service.
Understanding Customers’ Sentiments to Improve Product/ Service Offering
The comment sections, particularly on business or e-commerce websites, can be found with a bulk of negative remarks from dissatisfied customers. Not just that, but the online discussion forums also tend to receive numerous notions of disappointment of unhappy customers. Sentiment Analysis can help you apprehend the fundamental reasons that lead to customers’ disappointment while also helping businesses track the change in customers’ attitudes, approaches, and behavior toward products and services.
The AI-based NLP system trained with the set of sentiment analysis datasets is potent enough to read the customers’ sentiments hidden between the texts, i.e., the online reviews, comments, social media posts, surveys, and feedback shared on discussion forums. Businesses can get insights into their potential customers’ changing buying perspectives and the reasons behind their disappointment in improving products and services offering. Moreover, it can be harnessed to enhance product quality and improve service delivery with the insights received through customers’ sentiments.
Sentiment Analysis for Personalizing Customer Interaction
Buyers these days prefer personalized services from their services vendor and product seller. Customers expect the organization to know what they want and feel regarding vendors’ products and services. Sentiment Analysis can assess past and live interactions between buyers and sellers to give businesses the insight to personalize the communication. In general, customers want experiences tailored to their interests.
It’s not over saying, but sentiment analysis can decode customers' emotions and experiences which can help businesses personalize the inter-communication to enrich customer experience through uninterrupted communication between buyers and sellers.
When businesses have access to the right analytics of customers’ sentiments about their products or/and services, commercial entities and organizations can discover the type of users’ text triggers that reflect in customers’ online reviews, feedback, and questions & answers left in discussion forums. In both cases, businesses can get their agents pre-equipped to provide customers with authentic responses to customers’ queries and concerns that positively influence customers’ sense about products and services.
Final Thought
Sentiment Analysis enables an organization to truly determine what customers sense about a brand, its products, and service offerings. In a broader way, it can offer businesses all the insights from all over the internet to support delivering an exceptional customer experience and enhance customer engagement.
