If you’re talking to your customer over chat, pay attention to two types of metrics: transactional data and conversational analysis. You need to understand both.
The first, transactional data, tells you basic metrics about your chat activity. Most chat platforms provide analytics that measure the chat interactions, volume, and handling times.
Conversational analysis provides additional metrics that provide you with valuable insight into the content of those chat conversations. Understanding those metrics can give you real insight into business needs. It can help you and your team resolve more issues through chat instead of escalating them to the voice channel: a huge win, considering that live chat interactions cost the contact center less than voice interactions.
Here are 10 metrics to pay attention to:
What do you need to know about the 10 most important metrics for chat support?
User engagement
If you use chat to help sell your products, you should monitor the conversion rate of total users on a page compared to how many users begin a chat. Even in a customer service setting, this metric can be valuable. For example, you can measure how many users visit a help article and then start a chat conversation after reading an article, giving you insight into if the articles effectively answer questions.
Chat volume
Similar to the total number of calls you get, chat volumes show you how many messages were sent back and forth between agents and customers. Some chat platforms call this metric by different names, but they measure the conversation length to help show you how much interaction takes place before a conversation concludes.
Abandon rate
This refers to the number of chats that start but are abandoned by customers before the conversation naturally ends with a resolution, purchase, or further instructions.
Completion rate
The number of chats that end with an issue resolved. Although this metric can be used for sales chats, it’s especially important in customer support chats. Some chat platforms allow the support team to indicate if the conversations were completed. This can lead to some false positives. Conversation intelligence software can analyze the same conversations and show you if the issues were really resolved.
Escalation rate or agent takeover rate
This refers to the percentage of chats that start in a chatbot, but the chatbot responses are not sufficient to resolve the question. The chats escalate to a live agent chat or refer the customer to another channel, such as asking them to make a phone call or send an email.
Fallback rate
A fallback rate is the percentage of chatbot conversations where the chatbot fails to understand the user's query. Higher rates indicate that the chatbot fails to meet customer needs and that there are potential issues with the conversation flow.
Customer effort
A decade of research on customer experience has shown one clear thing: customers prefer low-effort interactions with the companies they do business with. You can measure customer effort by looking at the combination of incidents that we know contribute to customer effort. These include asking the customer to repeat information, wait, or get transferred from one agent to another. Other indications of customer effort include when companies miss a customer’s expectations, when agents fail to help resolve issues, or when customers express frustration.
Customer sentiment
Sentiment analysis requires the use of computational linguistics - which is the science that allows computers to understand language. Just like in speech analytics, you can measure customer sentiment in chats by analyzing the phrases used and if the customer expresses delight or frustration. In chat messages, the tone of voice can’t be taken into account, so an advanced AI-powered system that understands word choice and nuanced meanings becomes more important. Some chat platforms also request customer satisfaction scores at the end of a chat, which can also give you insight into how customer interactions went.
Agent performance
Conversation intelligence platforms can score chat support agent performance by measuring how well they followed set scripts and how they responded to customer questions and requests. Conversation analytics can automate the traditional QA role that requires supervisors to read individual chat transcripts, saving significant time and allowing supervisors to focus on the most impactful areas for improvement. As one of our customers put it, “If I had to read each chat manually, it would take a ton of time to group together different aspects of soft skills. Creovai does that for us.”
Key conversation trends
Conversation analytics can show you specific reasons for contact based on what happens in the conversation and monitor changes and trends over time. This conversational data provides a wealth of information about your business, and you can use this information to drive meaningful business changes. With chat conversations specifically, you can identify the top reasons your customers are contacting you over chat and whether your agents or chatbot are successfully resolving those issues. This helps you improve the chat experience, ideally deflecting calls from your contact center and reducing your operational costs.
You can use Creovai Conversation Intelligence to analyze every type of customer conversation, from traditional call center voice channels to omnichannel experiences such as chats, chatbot, and email support.
Creovai helps you unlock deeper insights from your chat data with advanced conversational analytics. Request a demo
FAQs
How often should we review chat analytics?
Review operational metrics like chat volume, abandonment rate, and escalation rate daily to identify immediate issues that could impact customer experience. Customer sentiment and completion rates should be monitored weekly to spot emerging trends. Save deeper analysis of conversation trends, agent performance, and customer effort for monthly reviews when you can identify patterns and make strategic improvements to processes and training.
Which chat metrics have the biggest impact on overall customer satisfaction?
Customer effort and completion rate are the strongest predictors of satisfaction in chat support. When customers can resolve their issues quickly without repeating information or being transferred multiple times, satisfaction scores increase significantly. Monitor these metrics alongside sentiment analysis to get a complete picture of the customer experience and identify specific friction points in your chat processes.
What’s the best way to manage agent performance in chat without micromanaging?
Focus on outcome-based metrics rather than activity metrics. Measure completion rates, customer sentiment in their conversations, and adherence to key processes rather than response speed alone. Use conversation analytics to identify coaching opportunities and recognize top performers who can mentor others. Set clear expectations around quality standards and provide regular feedback based on actual customer interactions rather than arbitrary quotas.
How can I use conversation trends data to make business improvements that go beyond chat support?
Conversation trends reveal valuable insights about product issues, service gaps, and customer needs that extend far beyond support operations. Share frequently discussed topics with product development teams, use common complaints to inform quality improvements, and identify opportunities for proactive communication or self-service content. This data can also inform marketing messaging and sales training by highlighting what customers care about most.
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