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Imagine a conversation between a disappointed customer and a customer service representative:
I’m sure we have all experienced such conversations but what is evident here? ‘’Empathy’’ injected by the human agent into the conversation. That took a mere two responses to figure out, right? This ability to converse with others through emotions is ingrained in our language instincts. ‘’Humans’’ detect the emotional tone behind the conversations by gauging the Polarity and Magnitude of the conversations. Thus, enabling us to respond with emotional intelligence, empathy and deliver a flawless customer experience.
Polarity indicates whether the sentiments are positive, neutral, or negative and Magnitude expresses how strong the sentiments exhibited by the customer are.
So, the question arises: Humans understand emotions — does AI?
To delve into this, let's explore what sentiment analysis is all about.
It is the branch of machine learning that tries to decode the emotional tone of conversations powered by advanced language algorithms. It works by sensing and quantifying the positive, neutral, and negative feelings within our conversations.
But what makes sentiment analysis so valuable? Well, human communication is more than an exchange of words. It includes the intricate expression of feelings, way beyond simple semantics. Sentiment analysis provides insights into the actual mood behind the texts and goes deep into complex statements. In a nutshell, it models social interaction which creates a whole heap of value that could be leveraged.
Emotional triggers drive our daily decisions. With sentiment analysis, you can spot which conversations act as triggers that alter customer attitude. Like the all-time cliché phrase “Please wait”, often makes the customer cringe. Realizing what messages evoke certain emotions in your customers can assist you to elevate customer service.
With sentiment analysis, one can train the chatbot to identify and reciprocate the customer's mood. If the chatbot detects the customer is infuriated, irritated, or displeased, it can escalate to a human agent at the right time. This helps you to deliver solutions at the earliest moment.
During a hectic day at the contact center, customer service representatives can find themselves tackling multiple customers simultaneously. Keeping track of how each customer is feeling can be a challenge for the Manager – especially during busy hours. Sentiment analysis gives you a glimpse of which chats are going smoothly, and which need further attention. Thereby, improving the support request quality.
Sentiment analysis can be used to identify the happiest customers. But the real benefit lies in recognizing negative statements about your brand which might need some attention to satisfy the disgruntled customers. It helps you to create a powerful brand with a flawless customer experience.
We, humans, are great at saying the same statement with joy, sarcasm, or regret but it can a hard nut to crack for our chatbot. With sentiment analysis, it is easier for your Virtual Agents to adapt to the tone of the customer conversations and reciprocate accordingly. This makes conversational AI more natural and engaging.
By now, it is apparent that there are numerous advantages of sentiment analysis. To raise the customer experience to another level let's look at how sentiment analysis & conversational AI can completely transform your customer experience.
Most people, even when they know they are interacting with a Conversational AI, will convey feelings almost as if they were facing a human counterpart.
As AI continues to gain traction in modern customer service, bots are becoming a vital part of the user experience. After all, the purpose is not to just provide the correct answers, rather as an organization, you want to create a delightful customer experience. Therefore, the focus should be to build a better Virtual Agent which understands the complex and distinct human sentiments and adapts its responses accordingly. And this is possible via sentiment analysis.
Thus, a Virtual Agent with ‘’sentiment analysis’’ capabilities can provide more human-like responses by deciphering sentence structure clues. It can navigate the conversation in the right direction, capitalize on customer delight, and even let a human agent takeover at the right time.
The outcome: Conversational experiences that are more natural and engaging than ever.
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