The limitations of traditional contact centers
Modern customers want and expect more – from simpler ways to engage with companies to faster methods of resolving issues. Interactive voice response (IVR) systems of traditional contact center make it easier than ever to provide customers with phone-based self-service, but they often end up becoming the escalation channel of choice for complex issues, disputes, and irate customers. Plus, traditional touch-tone and dialog-based IVR systems with complicated and long-winded menu trees typically fail to meet the needs of savvy consumers – often frustrating more than helping. This results in more calls being routed to live agents and higher contact center costs – despite being deployed for the exact opposite reason.
To further complicate this, social media gives individual voices and experiences great power to influence the purchasing decisions of others – such that a single bad experience can reach thousands, or even millions, of current and potential customers. Your customer experience and service can thus make or break you.
of customers report that it’s easier than ever to take their business elsewhere.
of B2C customers purchased more after a good customer service experience.
of all customer interactions will be handled without a human agent AI by 2020.
Conversational IVR: the future of the contact center
Conversational IVR replaces complicated menu mazes and other time-wasting frustrations with natural language processing, machine learning, and other forms of intelligence. This allows your system to understand the content and context of spoken requests, allowing for more free-form and dynamic customer experiences. With conversational IVR, customers can interact with your system using their voice and words that come naturally to them, rather than their fingers, without being tied to a strict script. Unlike traditional menu-based interactions, customers get convenient, personalized, and frustration-free self-service that rarely requires a live agent, allowing you to minimize churn, maximize labor productivity, and save money.
How Kore.ai enables contact center automation
Kore.ai allows organizations to automate customer service by leveraging voice enabled, natural language powered conversational virtual assistants. Assistants built on our platform can be easily integrated with virtually any conversational IVR by simply enabling an IVR channel for your bot. The Kore.ai dialog builder provides comprehensive support for generating all the elements used in your IVR VXML documents. Plus, Kore.ai IVR-enabled virtual assistants can handle alternate flows like timeouts, no matches, and no inputs, and can seamlessly transfer calls to agents based on a user’s request, sentiment, or your business logic.
Kore.ai virtual assistant platform offers an IVR sandbox environment for its cloud version. This built-in IVR sandbox experience enables the developers to instantly launch the assistants for interactions over voice calls.
You can quickly preview how the virtual assistants works on IVR Channels. Just enable the IVR sandbox option within the IVR channel and call on the temporary number to interact with and test the assistant with your team.
Virtual assistants built on Kore.ai can be easily integrated with popular IVR’s available in the market like Cisco, Avaya, Genesys and USAN.
Native VXML Support
Kore.ai virtual assistants generate W3C compliant VXML documents. Our platform allows developers to configure VXML elements and attributes at various nodes in the dialog tree and within standard responses.
Kore.ai’s discourse analyzer helps enterprises generate conversation flows using historical chat or call transcripts. Chat and call transcripts are analyzed using neural network-based machine learning models to identify intents and discourse patterns to fulfill a specific intent.
Granular Call Flow Support
Define all call flow elements such as grammar, prompts, retry, and time-out periods. Kore.ai supports call termination handlers, allowing you to end calls or invoke dialogs in case of exceptions.