Sentiment Analysis: Enhancing Your Virtual Assistant’s Emotional Intelligence

1. Sentiment Analysis: Enhancing Your Virtual Assistant’s Emotional Intelligence Earlier, brands used to believe in traditional call centres to understand human emotion. But, it’s the time that businesses should switch to humans like Virtual assistants or chatbots. Just like human beings, VAs need to understand the feelings of the customer and what exactly do they want. Nevertheless, the customer is the king and it is us who can win their hearts. At this point in time, Sentiment comes into play.

2. As per ​stats​, ​56% ​of the customers worldwide stop doing business due to bad customer experience while some switch to other competitors for a better choice. These AI enabled VA works as a game-changer for your business and helps you understand the emotional state of the customers. Also, at the same point, brands do not want to lose the human touch. Here, we will see how AI-based VAs work on sentiment analysis and why a hybrid approach is still required even with the inception of VA chatbots. What is Sentiment Analysis? Sentiment Analysis is a subset of ​NLU (Natural Language Understanding) and Machine Learning​. With the help of these two technologies, it determines the emotion behind the customer’s input and delivers a solution accordingly. A virtual assistant perceives and ponders the customer information with the help of NLU and ML enhances the VA’s performance and its working based on the past conversational data.

3. Sentiment Analysis analyses the virtual assistant’s mood and emotional intelligence. With the help of NLU, VA decodes the brain of the user by analysing the speech pattern and sentence structure within the text. Let’ See How a VA Detects and Interprets Customer Sentiments: A large number of annotated sentences and sentiments from the users’ input are examined to build a sentiment analysis module. This module classifies live messages into one or more sentiments. The AI-based virtual assistant detects the mood and sentiment of the user from their messages. A person can have different emotions and moods at different times. Keeping this in mind the VAs analyses the users’ emotions, utterances and classify into sentiment types viz. happy, sad, frustrated, angry, optimistic etc. These AI-enabled VAs have a tone algorithm and platform intelligence that accounts for reality and gives sentiments scores based on it. ​It ​is the sentiment through which the VA can predict the mood behind the conversation whether it is positive, negative or neutral. Before we process other steps of how VA decodes human sentiments, it is quite important to know the term ​Sentiment Score. We know that Sentiment Analysis is functionality that allows a VA chatbot to understand the mood of the user.

4. The mood can be either of these mentioned above. But let’s just see how it scores the sentiment on a scale. An AI-powered VA’s sentiment scoring is developed on any scale which you prefer to have. The Scale can rate the scoring either form 1 to 10 or from 1 to 5. It can even give negative scores as well. It is the NL engine that gives scoring and ranks the sentiment on a, particularly designed scale. For example - the user inputs - What the hell is wrong with your service, my order no. 2452743 was supposed to be delivered last day, but it's still on the way. The VA just tracks the word “hell” and gives the sentiment score like this: Tone name:​ “Anger”

5. Conditional Transitions This allows ​AI developers ​to use these scores to steer user-bot conversations or seamlessly invoke escalation to a live agent. Sentiment scores play a great role in the entire workflow process and eliminate the number of executives in between. The scores are stored as context and help the VA to drive the conversation in the appropriate direction via conditional transition statements. Let’s make it simple for you to understand. If a user score indicates a negative sentiment or a tone name ​“ Anger”, the VA will seamlessly escalate it to a live agent in order to mitigate the situation. This will help in improving the user experience. How Sentiment Analysis Helps eCommerce Business Understand User Experience? #1. ​Provides real-time assistance to the users What happens when a human customer care executive deals with the customer at the other end. It analyses the tone of the person and checks if the communication is going likely or not. Based on the assessment of the customer mood, the executive tries to keep them calm and in a positive frame of mind.

6. The same thing goes with the AI enabled VA chatbots. A VA chatbot or an assistant detects the sentiment of the user and adjusts the responses of the user according to their tone. For example, if the assistant sees that the user is angry or unhappy with the service, the VA easily modifies the responses and delivers the solution by resolving the user’s issue. VAs correct the responses in real-time and effectively turn negative and unsatisfactory customer experience into a positive one. #2. Escalating the issues to a human agent AI-based bots or VAs is not the ultimate solution in this case. They can handle routine customer interactions but cannot tackle complex customer issues. eCommerce and many other brands are using a hybrid approach for this that requires a combination of both AI and human interaction for human engagement. Sometimes, it becomes difficult for the AI-VAs to understand the human’s sentiments, so at this point of time, brands handover issues to the human agent, In the hybrid approach, the virtual assistant handles the bulk of interactions whereas the human customer care executive takes over the critical conversations like the complex customer issues. But, here the question arises: how does a business decide the prioritization of thousands of issues. What issues should be solved by this and what should be escalated to the human agent.

7. At this stage, customer conversations with negative sentiments like “angry or frustrated” are given top priority. They analyse the magnitude of the negative sentiments and auto-routes the issues to the human agents for careful handling. #3. Evaluating customer care staff and improving processes When a human customer care representative uses the Conversational AI platform to get in touch with the customers, sentiment analysis provides real-time feedback to the VA so that it can help the customers facing negative experiences. In this way, brands can also evaluate the effectiveness of their staff and better monitor their business. It helps them in evaluating the overall performance of the customer executives, their insights and learning capabilities. If required, they can conduct training for their existing staff so that they can handle customer interactions seamlessly. Sentiment Analysis in VA alerts the business about the negative feedback by the users and will help them in resolving the issues in real-time. #4. Breaking up customers into different buckets and measuring satisfaction Sentiment Analysis acts as a powerful tool for the businesses as it helps them in segmenting the customers into different buckets based on their satisfaction level. After this, brands can

8. easily identify the categories of happy, sad and demanding customers. All this with the help of the conversational data garnered by the VA from the previous interaction from the users. Organisations having AI-based VAs can get insights about the customer sentiments and reshape their strategies in order to provide better customer service. In this way, you can reward the “happy” and “satisfied” customers with various offers in order to build long-lasting brand royalty in the hearts of the customers. And, customers with ​negative sentiments ​like “unhappy” and “angry” customers, ​are prioritized first and escalated to human customer care representatives. #5. Supplementing conversational commerce

9. It aids in pitching up your conversational commerce, and all the credit goes to sentiment analysis. A virtual assistant tracks the tone of the conversation and helps in giving recommendations. If suppose, it detects a positive sentiment from the customer reviews, it will push the customer to make the purchase and generate revenue for your brand. While, at the same time, if it detects a negative sentiment, it will adjust the tone and offer alternative products and services as per the customer’s interest and perception. Summing Up Artificial VAs can improve the customer experience and increase the conversion rate with sentiment analysis as it provides flexible customer service, seamless routing, generates upselling opportunities, and tracks overall customer satisfaction. If you as a brand want to improve the customer experience of your users, VAs with sentiment analysis factor can identify the happiest customers and welcome sale pitches for you. Want to switch your human customer care representative with AI-based VAs, ​hire developers in India who can develop AI VAs or chatbots to enhance your customer experience and build a long-term relationship with them.