Optimizing data from existing bots can reduce failure rate by up to 35% and reduce escalation rate by up to 57%, while uncovering new ways to handle customer conversations.

Businesses are relying on chatbots more than ever.

According to Gartner, 91% of organizations plan to deploy AI in self-service within 3 years and COVID-19 has led to a 65% increase in use of self-service by customers. Data from Straits Research forecast that the global chatbot market size is expected to grow from $526 million in 2021 to over $3.62 Billion by 2030.

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 Cloud, a new platform that lets businesses build and optimize chatbots using conversational data while providing business leaders with a centralized view of all chatbot data, aims to achieve this. 

The Intuit experience

Dashbot is currently working with organizations such as Intuit, Expedia, Geico, Google and Travelers. 

With QuickBooks Assistant having so much unstructured data, Intuit staff 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 Cloud to benchmark the current state of their chatbots, and then identify and prioritize the highest impact tactics to improve.

The Conversational Data Cloud enables these 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.”