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What is conversational analytics

By Nick Barth

Updated on March 6, 2024

Conversational analytics is an emerging field that sits at the crossroads of artificial intelligence (AI) and data analysis. At its core, conversational analytics refers to the use of AI-driven chatbots to collect, analyze, and interpret large amounts of natural language data generated through user interactions. These interactions can include customer service exchanges, sales conversations, or any other form of communication where a chatbot can be deployed to capture real-time data.


Simply put, conversational analytics involves employing sophisticated algorithms and machine learning techniques embedded in AI chatbots to understand, monitor, and derive insights from conversations. By analyzing the content, context, sentiment, and patterns found in dialogue, organizations can gain a deeper understanding of their customers’ needs, behaviors, and preferences.

Use cases

One significant use case of conversational analytics is retention modeling. Here's an example:

Retention modeling

Organizations can use conversational analytics to predict customer churn and retention rates by examining conversational data over time. By identifying common trends or issues that customers mention before ending their service, a business can proactively make changes to improve the customer experience. For instance, if conversational data repeatedly shows that customers are dissatisfied with the response time to their inquiries, a company may decide to invest in additional resources or training for their customer support team to address this issue.

The insights obtained from conversational analytics allow for:

  • Identifying pain points: Uncover areas where customers commonly experience frustration and seek to rectify these issues before they impact more of the customer base.
  • Understanding preferences: Tailor products and services to better match what customers are looking for, as indicated by their conversational cues.
  • Customizing communications: Personalize marketing and customer engagement strategies according to the preferences shown in customer dialogues.
  • Enhancing products and services: Use customer feedback to inform development strategies and innovate more effectively.

Overall, conversational analytics represents an innovative way to tap into the wealth of unstructured data present in human-to-AI interactions. It provides actionable insights and a competitive edge for businesses that know how to harness it effectively.

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