(Beginner) Tutorials, Guides and FAQs on Chatbots, Voice Bots, Dialogflow Essentials and Dialogflow CX.
It seems that most organizations that use chat and / or voice bots still make little use of conversational analytics. A missed opportunity, given the smart use of conversational analytics can help to organize relevant data and improve the customer experience.
While setting up conversational analytics, there are three specific categories of metrics relevant to designing a voice bot: conversation-related metrics, chat session & funnel metrics, and bot health metrics. Conversation-related metrics can help understanding conversations and shining a light on questions like what’s been said, by who, when, and where? To effectively monitor conversation-related metrics, data could be stored in a data warehouse: an enormous database to which several data sources can be connected. Here, you can store as much structured data as you want, whether it’s website data, website logs, login data, advertising data, or Dialogflow chatbot conversations. The more data you gather, the better you can understand and help your customers.
Do you use chat or voice to communicate with customers? Then it is important that you have your conversational analytics in order and you collect the right data. This is the only way you can optimize your channel as well as possible and improve the customer experience. In this white paper, the DDMA Committee Voice tells you all about it. Download it now from: https://ddma.nl/ca/
Lee Boonstra is a conversational AI developer advocate and applied AI engineer at Google. In this role, she is focusing on Dialogflow, Contact Center AI & Speech technology. She is a public speaker and a published author.
Lee wrote a book for O’Reilly: Hands-on Sencha Touch 2 and lately: the Definitive Guide to Conversational AI with Dialogflow and Google Cloud for Apress.
Lee lives in Amsterdam, the Netherlands, and is a rainbow mommy.