Conversational Analytics


Conversational Analytics refers to the process of capturing, analyzing, and interpreting data from customer conversations. These conversations can occur across various channels such as live chat, email, social media, and phone calls. The goal is to gain insights into customer behavior, sentiment, preferences, and feedback to improve customer service and product offerings.


Conversational Analytics is crucial in customer success and support as it helps businesses understand their customers better. It provides valuable insights into customer needs, pain points, and satisfaction levels. This data can be used to improve customer service, enhance product features, and make informed business decisions. It also aids in proactive support by identifying potential issues before they escalate.


Conversational Analytics involves the use of AI and machine learning tools to analyze customer conversations. It includes metrics like sentiment analysis, topic categorization, and conversation length. The exact calculation depends on the specific tool and metrics used.


For instance, a B2B SaaS company uses conversational analytics to analyze customer support chats. They find that a common topic is confusion about a specific feature. The company can then improve their product documentation and training around this feature, reducing customer confusion and support requests.

Best Practices

  1. Use a comprehensive tool that can analyze conversations across multiple channels. 2. Regularly review and update the topics or keywords your tool is tracking. 3. Combine conversational analytics with other data sources for a holistic view of the customer. 4. Act on the insights gained, whether it’s improving a product feature or training your support team.