With enterprises realizing the true potential of conversational commerce, adding a chatbot for your business is no longer an option. A conversation engine enables chatbots to ensure a better experience to users with more contextual and personalized conversations. Let’s explore in detail, the role of the conversation engine in chatbots and design aspects involved in building a conversation engine.
Conversational AI-first will supersede cloud-first and mobile-first as the most important, high-level imperative for the next 10 years.”
In the era of experiences-as-a-economy, user experiences matter the most and enterprises are channeling all their efforts (financial and technological) to this process to ensure a superior experience for their users. Here, Chatbots play a dominant role for enterprises to ensure superior interactive experiences in the information retrieval journey of users. Conversational engines are what allow chatbots to understand what the user is looking for. Integrating AI algorithms to conversation engines makes chatbots well informed about users and serve them with more personalization.
Conversation engine involves designing a chatbot to be context-aware in its conversations. It’s this context awareness feature that differentiates chatbots from an IVR (Interactive Voice Response) system. If you are talking to an IVR system you can’t just converse freely, you must follow a set of instructions because it's not dynamic. To answer specific questions of users, the chatbot must be dynamic, context-aware and intelligent. This is where the conversation engine plays an important role. This feature is what enables the chatbot to simulate human-like conversation.
Though chatbots are capable enough to share human work, we can’t completely replace a human agent with a chatbot, as there are limits to what a chatbot can do in a conversation. Also, they can’t maintain full-fledged conversations without human assistance. As of now, chatbots can’t replace humans but they can certainly amplify productivity.
Train an intelligent agent by placing it directly into real interactions. Given a conversation, the agent analyzes how the conversation flow has happened between the human agent/chatbot and the end user. It studies how the user has replied: If the user was satisfied the reply would be “thank you” or something like “that has helped,” thus it records these as positive signals. If the user is not satisfied, the user would probably ask more questions or send negative signals. It grasps these signals and learns from the interaction points.
The conversation engine enables the chatbot to identify from its user preferences, problem areas and in what different situations the user would be happy, sad, or angry. It tries to identify behavior patterns and learn from the interactions. It analyzes not from one person, it learns from different people, diverse interactions, and situations.
Based on this analysis, policies are framed. These policies will be used to decide what to do in a given situation based on history (e.g.: How customers would be satisfied). The goal is customer satisfaction, for that it collects as many customer experiences as possible and further reframes policies to ensure better customer satisfaction. This process is called reinforcement learning. It can take deep reinforcement learning to develop more accurate policies. After running these tasks repetitively, and going through intensive testing, the chatbot will be ready to deploy.
"Reinforcement learning (RL) is defined as the process of learning by interacting with an environment. An RL agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions on the basis of its past experiences (exploitation) and also by new choices (exploration), which is essentially trial and error learning."
Here, Natural language Processing (NLP) is what empowers the conversation engine to decode the user’s message by mining out the analytics of the user’s intent and sentiment. The Machine Learning and AI algorithms help the chatbot to study the past user interactions and behavioral pattern. Based on this, the user’s conversations are tailor-made and able to connect with the user’s emotional quotient.