# Contextual Understanding
In customer engagement, real-time contextual understanding is essential to deliver meaningful conversations. To have a good understanding of context, a chatbot needs to analyze inputs like time, day, date, conversation history, tone, sentence structure, intent, identity, etc. These inputs are then fed to empower chatbots to comprehend the context in the conversation.
For instance, in the sentence, “Fantastic! Presently the flight is delayed by one more hour”, the customer isn’t happy about the flight being late but it can trick the interpretation of the machine encouraging to commit an error if it is unable to understand the context.
Apart from self-learning, it is wise to provide or feed chatbots with different contexts based on situations, linguistic preferences, persistence, emotional context, etc so that they can utilize the context when required.
# Perpetual Learning
The ability to learn is a pivotal factor in building an intelligent chatbot.
A well-honed chatbot is one that learns from the conversations to enhance its performance metrics. There are 2 steps involved in this learning process. One is handling the end-user appropriately when there is no relevant answer found in the knowledge base and the second is recording learning from the failed conversations. Also, managing standard responses stands a key in handling user frustration.
User modelling, machine learning, and natural language understanding modules can help achieve better conversations and avoid expectations mismatch.
Leveraging neural networks, deep learning, Machine Learning (ML) algorithms and human supervisors ensure the AI chatbot becomes a good learner.
Learning is key to ensure that the chatbot identifies patterns in data it receives and answers to user queries in the most appropriate way. Thus, learning abilities are a must-have if a chatbot is to be made intelligent.
# Seamless Agent Handover
Handling a user when there is no answer dictates the satisfaction scores. Amplifying these scores can be better achieved when the conversation is transferred to a human agent rather than annoying the consumer/employee with the same repetitive questions/responses.
According to a report, 88% of consumers said they expect a natural transition between a virtual agent to a human agent while making a purchase decision or contacting customer care.
It goes without mentioning that humans will remain an integral part of contact centres. However, chatbots automate the triage requests or help in troubleshooting, helping the human agents become more productive.
So the real challenge is to train the bot when to transfer it to an agent. Using the advancements in AI, train the chatbot with user sentiment analysis and preference during an interaction and transition the conversation to a human agent.
# Voice Technology
Voice bots are an integral part of almost every function that focuses on providing a positive customer experience. One can skip the interaction with a complicated UI and ask the voice assistants to do the job. This adds both convenience and error-free interactions with the chatbot reducing the chance of failure. Enable friction-free conversations while freeing up employees to focus on more meaningful work.
With significant advancement in the fields of natural language processing (NLP) and machine learning (ML), voice assistants have become much more intelligent and useful in guiding customers to meet some of their needs.
Utilizing the best of technologies like voice recognition, speech synthesis, and natural language processing (NLP), chatbot parses a spoken phrase and translate it into written text.
According toComscore, American media measurement and analytics company, more than half of the total searches will be voice-enabled searches by 2020.
With the power of AI and conversational UI, voice assistants can deliver more personalized experiences and can automate interactions by adding intelligence and insight to the conversation.
According toGartner, by 2023, 25% of employee interactions with applications will happen via voice, up from almost 3% in 2019.
