Amazon has embedded an automated chatbot design tool into its CCaaS solution, alongside new case management and proactive outreach solutions.
The conversational AI innovation – known as the Amazon Lex Automated Chatbot Designer – allows Amazon Connect users to build, test, and deploy chatbots within the CCaaS platform.
Seemingly, Amazon hopes to differentiate its bot solution through ease of use. By harnessing machine learning, the tool scours contact center transcripts, uncovers everyday customer requests, and develops an initial chatbot design in a couple of hours.
Many other providers have cut down chatbot development time with low-/no-code tools removing the need for dialogue designers and computational linguistic specialists with low-code tools. However, Amazon aims to go one step further and automate more of the process.
The automation uncovers common customer contact reasons, associated phrases, and the necessary information for the chatbot to resolve the query.
Thanks to these capabilities, users access an automated chatbot design through the Lex console, now embedded within the Amazon Connect platform. Developers can then make tweaks, alter the bots' responses, test the solution, and deploy it within a single app.
By offloading the customer conversation analysis section of building chatbots and following this process, companies can accelerate the development of conversational AI. Alongside this, they may cut out errors and automate more customer conversations.
Yet, the latest Amazon announcements included the launch of further enhancements to its CCaaS platform, such as its new Amazon Connect Cases solution.
The tool automatically opens up a new case whenever the customer calls or messages the contact center with a new query. In doing so, it logs all the tasks that agents complete – across any integrated application – when handling the issue.
Such data is helpful for agents when customers make a second contact and can also enhance automated interactions as chatbots may leverage the data to deliver more personalized self-service conversations.
Also, the data may supplement predictive routing technologies, as the case data ensures that customers route through to the best available agent – with the relevant case attached.