An Introduction to Conversational Analytics

We love conversations and designing compelling stories for our customers. Conversations are the basic driving force behind modern businesses. Currently, we are seeing a shift in the way people interact with technologies, brands, and devices: conversation. As messaging apps outpace social media networks in terms of active users and the rise of IoT(Internet of Things), businesses are increasingly aiming at delivering conversational experiences to their customers through chatbots and voice-enabled applications.

However, the shift is not just limited to brands but is also changing the way we look at data. Conversational Analytics is increasingly becoming a critical need for businesses wishing to know their audience better and deliver a super user experience. Specific benefits include sentiment analysis, enhanced social listening and better personalization to meet user’s needs.

So how does conversational analytics fit in the enterprise model? Firstly, what is conversational data?

Conversational data helps you make sense of how users are interacting with your chatbots or other conversational based interfaces. Compared to web analytics, it shows an individual user’s interactions in real-time versus overall aggregated traffic data. Conversational analytics is also better at delivering insights to users. By simply conversing with data through an AI-driven combination of NLP and NLG, users can get the right insights at the right time.

Conversational Analytics: The new fuel

Conversations are also easier to understand, decode and personalized compared to graphs or visual-based insights. They have been proven to drive user adoption of technologies and has the power to significantly improve data literacy across organizations, allowing for better data literacy.

AI-based analytics also allows you to collate information automatically, providing up-to-date reports on your metrics in real-time.