Tethr and Awaken Intelligence join forces as Creovai
Tethr and Awaken Intelligence are becoming Creovai, bringing together best-in-class conversation analytics and real-time agent assistance.
Robert Beasley
June 3, 2024
Sara Yonker
December 6, 2022
Chatbots save businesses time and money, and they make it easier for customers to get the answers they need. But chatbots aren't perfect. Read enough transcripts and you’ll find them riddled with misunderstandings, frustration, or ineffective help. Luckily, there's an easy way to fix that using AI (artificial intelligence).
Chatbots run on artificial intelligence, which means that they can learn from past interactions with customers and make better decisions in future conversations. They also use analytics tools like machine learning libraries to collect data about how often certain types of questions come up or what kinds of responses tend to be most helpful. Using this information allows you to make your bot smarter over time without having to write new code every time something changes.
To make significant improvements to your chatbot, you’ll also need to analyze chatbot performance and invest time in chatbot training. AI-powered analysis of your chat conversations can tell you which questions your chatbot doesn’t understand, which chatbot responses frustrate customers, and digest raw data from live chat transcripts as well to help build your library of chatbot responses.
Conversational analytics give you a foundation to identify customers and their behaviors, an essential piece to create a more personalized experience. For example, if your conversation intelligence software tells you that a high number of customers contact you to change their contact information, you can create a script in your chatbot to accept payments without requiring them to talk to a live agent.
By using AI in combination with chatbot analytics data, you can use this information to train your bot to respond appropriately.
Your chatbot isn't doing its job if it's not answering customers’ questions. Here’s what to look at to understand where your chatbot has content gaps.
We've all been there: you're trying to get answers from a chatbot, but it doesn't understand the question or provides an answer that does not address your needs. This creates more effort for your customers and likely leads them to switch channels to phone support or live chat.
Your job is to find out what makes conversations with your bot difficult for customers. You can do this by collecting data about how people interact with your bots, doing research into why they're having problems communicating with it, and then using this information to improve customer support chat.
Here are some questions that might make conversations with your bot difficult:
Now that you’ve collected a lot of data, you can use it to improve your chatbot’s customer support.
Tethr, an AI-enabled conversation intelligence platform, analyzes chats for businesses so they can better understand and improve their customer experience. But the platform has more uses than analyzing chat conversations and agents’ behavior.
Companies also use Tethr’s conversational analysis to improve chatbot performance so they can deliver better, faster answers to customers.
Here’s an example: One customer, a U.S. telecommunications giant with hundreds of millions of subscribers, used Tethr to evaluate chat conversations of both live agents and chatbots. They wanted to find ways for their chatbot to resolve issues quickly without requiring a live agent to take over the conversation.
Using Tethr’s AI-powered analysis of all conversations over phone, live chat, and chatbot, they identified more than 600 reasons customers contacted them - and started making a plan for which of those could be solved by a chatbot.
Once they had these reasons, they also made sure to identify all the ways different people described the same issue. This way, their chatbot would recognize customers requests no matter how they described it.
Then, they looked at parts of conversations their chatbot didn’t understand. When this happened in conversations, the chatbot would ask customers to rephrase their request, which they knew would be a point of friction for customers. This allowed them to expand the chatbot’s understanding.
Want to see how AI analysis of your chat conversations can give you the information about your business that you need? Set up a call with our product experts who can talk about Tethr’s chat analytics solutions.