As AI continues to gain traction in modern customer service, virtual assistants are becoming an integrated part of user experience. While the flagships like Cortana and Siri have become the pinnacle of how humans interact with this technology, services like chatbots use a simple algorithm to streamline communication with customers.
Chatbots are typically used for frontline support, as they relay information about products and services. Customers enjoy chatbots because they provide instantaneous responses and improve the support experience through an efficient, conversational interface.
Over time, chatbots have become more sophisticated and have adapted new AI features that improve customer experience. One of these features is sentiment analysis, which allows the bot to determine the emotion behind a customer's message. With this tool, you'll know whether or not customer conversations are going well with your bots.
In this post, let's review some sentiment analysis tools and explain how these features can be used to increase customer satisfaction at your business.
Sentiment Analysis Features
Before we dive into how sentiment analysis influences customer satisfaction, let's break down the two major tools occupying this space.
Machine learning is an algorithm that improves the chatbot's performance during conversations. When a conversation is triggered, the algorithm observes previous messages and responds accordingly. This makes the conversation flow smoothly and create a more personalized feel for the customer.
Natural Language Processing (NLP)
Natural Language Processing, commonly known as NLP, perceives and evaluates customer information. With this tool, scientists can program the chatbot to react differently to messages throughout the interaction. If the bot recognizes negative language, it'll adapt it's responses accordingly or route the conversation to a live agent.
Now that we've covered the tools used for sentiment analysis, let's discuss how this technology has revolutionized chatbots.
How Chatbots Use Artificial Intelligence
Before AI, chatbots were very simple. They could only respond with a few answers and couldn't process any data outside their programmed parameters. As a result, interacting with chatbots was less engaging than speaking with a human rep.
AI chatbots are programmed to stimulate conversation and recognize the underlying intentions behind a customer's message. They learn from previous interactions which enhances their ability to provide relevant answers and information.
Sentiment analysis has taken this ability one step further by allowing bots to interpret emotion. Let's review how this works in the section below.
How Chatbots Use Sentiment Analysis
Once chatbots could communicate effectively, the next step was to improve user experience. After all, it isn't enough to just provide the right answers, you want to create a delightful experience for your customers. With the help of sentiment analysis, chatbots could understand whether the conversation was going well and respond to customer emotions accordingly.
What makes sentimental analysis so valuable is its ability to conceptualize social interactions. Imagine an algorithm that determines what customers think about your product, why they think that way and what can be done to make their experience better.
To show you how this works in action, here are some ways sentimental analysis can enhance user experience with chatbots.
1. Adaptable Customer Assistance
With sentiment analysis, chatbots can modify their responses so that they're aligned with the customer's emotions. These appropriated responses make for excellent, engaging experiences with customers.
2. Routing Frustrated or Angry Customers
Customers who are clearly upset at the start of a conversation are quickly recognized and routed to a live rep. That way, the customer will receive personalized support quickly and efficiently.
3. Customer Categorization
Chatbot data is awesome because it records the entire customer conversation. And, with sentiment analysis, chatbots can identify your happiest and unhappiest users within your customer base. By segmenting your audience based on customer satisfaction, you can prioritize support for users at risk of churn and reward customers who have demonstrated long-term loyalty.
4. Record Overall Customer Satisfaction
In addition to audience segmentation, sentiment analysis can recognize your customers' overall perception of your service, brand, and products. This provides the chatbot with insight into how customers are feeling before they interact with them.
Sentiment analysis and other AI tools will continue to be popular in customer service. Adopting them is a great way to give your chatbots an edge and improve user experience for your customers.