10 metrics that matter when you look at your chat support analytics

Sara Yonker

November 30, 2022

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. 

Here are 10 metrics to pay attention to: 

1. 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. 

2. 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. 

3. 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. 

4. 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. 

5. 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. 

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6. 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.

7. 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.

8. 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. 

9. Agent performance

Conversation intelligence platforms can score 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. This can also identify top performers in sales teams and support teams. 

10. 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. 

Tethr is a conversation intelligence platform that analyzes every type of customer conversation, from traditional call center voice channels to omnichannel experiences such as chats, chatbot, and email support. 

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