Dashbot, a conversational AI and data platform, announced the launch of its proprietary Conversational Data Cloud™, letting customers build and optimize their chatbots from their businesses’ own conversational data. Dashbot’s Conversational Data Cloud™ turns unstructured, noisy, interrelated and often tangled conversational data into immediate action.
Across an ever-increasing number of communication channels (contact centers, support tickets, social media, IVR, live chat, etc), a business can get up to three million customer messages per day. The pandemic has significantly accelerated this flood of customer communications. In addition, the complexity of human language makes it impossible to predict every way users will speak with bots. As a result, over 50% of chatbot sessions fail. Optimizing existing bots can reduce failure rate by up to 35% and reduce escalation rate by up to 57%.
Dashbot’s Conversational Data CloudTM enables businesses to:
Centralize all conversational data including chatbot transcripts, Zendesk, email and live agent voice calls.
Decipher tens of thousands of daily conversations and transcripts.
Group similar messages and topics to determine areas of failure and opportunities for new use cases, leveraging its proprietary machine learning algorithms.
“We’re expanding beyond reporting and analytics to be able to ingest raw conversational data which can be difficult, but also very valuable for our customers,” said Andrew Hong, CEO of Dashbot. “We’re on a mission to decipher language, which is one of the most complex types of data that has ever existed. We listened to our customers that are challenged to make sense of all their conversational data, so we built our Conversational Data Cloud™ to help businesses automate, analyze and optimize their conversation channels.”
Dashbot’s Conversational Data Cloud™ is powered by three core features:
Transcript Transformer: Ability to search and categorize thousands of daily transcripts
DashbotML: State of the art machine learning models hypertuned from over 10 billion conversations. Topic Modeling to visualize flow and conversation loops. Phrase clustering (message grouping) to identify new use cases and unhandled topics.
Automated Training Data: Export messages as training data to optimize NLP model.
One Dashbot customer example is Intuit. With QuickBooks Assistant having so much unstructured data, they were spending days trying to manually identify mishandled or unhandled intents. In turn, their customers were getting annoyed with inaccurate responses and escalation to a live agent. They were able to leverage Dashbot’s Conversational Data CloudTM to benchmark the current state of their chatbots, and then identify and prioritize the highest impact tactics to improve.