Context Awareness

Context Awareness

The Inbenta Chatbot also has the ability to dynamically adapt its conversation and behavior based on the context it detects. Several factors can contribute to the Chatbot’s definition of context, including but not limited to where the Chatbot itself is (specific page URL or section of a site), who the user is (authenticated or anonymous, has permissions, or belongs to a defined user profile), connected third-party systems data (weather, geolocation, etc.), user-specific enrichment data (account info, recent transactions, type of customer, etc.), and custom triggers (particular queries, intents, variables or entities detected within the chat session).

Chatbot Highlights

  • Unique Natural Language Processing engine with over ten years of lexical development
  • Works out of the box: no additional rephrasing or learning necessary
  • Webhook-based Decision Trees for a dynamic, context-sensitive conversational flow
  • Multiple entity-based detection method that interpret visitors’ intents
  • Scalable and expandable solution that integrates with CRMs, billing systems, and more...
  • Feature-rich transactional intelligence with seamless escalation to live conversation
  • Unique avatar options for a deeply personalized customer experience

Armed with this information, the Chatbot may provide or restrict access to certain content or information, automatically initiate specific actions or transactions, show or change display elements, request more information from the user, and virtually do anything else that modern web technologies permit. This allows for truly personalized experiences at scale, resulting in richer conversations, improved self-service, maximized conversions, and delighted users.

As smart and feature-rich as a chatbot must be to give an enterprise-grade performance and meet the rapidly growing demands of consumers, it must nonetheless also be intelligent enough to know the appropriate time to hand off the conversation to a human and do it without any negative impact on the user experience.

When the Inbenta Chatbot detects an escalation trigger event (which can be defined, e.g. when a particular piece of information is detected from the context, the user asks a specific question or has a specific intent, or the user explicitly wants to escalate), the Chatbot then selects the appropriate escalation path to seamlessly transfer the conversation.

Escalation paths can apply custom logic, the most common being for the Chatbot to check if a live agent is available in real-time, and if so initiate a live chat session within the Chatbot window, where it will pass the transcript over to the live agent. Alternatively, if no live agent is available, the Chatbot displays a contact form to submit a ticket to the support center. There can be multiple escalation paths defined and initiated by different triggers.