The future of communication: Conversational AI
Fancy a chat? One where you can buy or even change a service quickly and easily; one where you don’t need to wait in a queue for hours and one where the brand’s representative really understands what you want? After so many unfulfilled promises of dealing with companies in this way, the possibility of fully automated conversational chat and customer service from ‘digital humans’ is with us.
Creating compelling digital chat experiences for customers is one of the major goals of digital transformation. Communication service providers (CSPs) are continuing to build digital ‘chat’ as part of their transformation processes. Quality conversational AI is a channel through which superior customer experience can be provided, at lower cost, as it revolutionises the way customers interact with service providers, with almost no human intervention.
Conversational AI is a part of a machine's ‘brain’ that allows it to learn, understand, analyse and reply to human language. Intelligent virtual assistants filled with new talents have digitised human-machine interactions enabling them to do anything from playing music to deciphering language and conveying messages. And this potential, after years of poor-quality interaction with ‘traditional’ bots, hated by customers, is now with us.
The shift from chatbots to conversational AI platforms
The shift from traditional bots to advanced conversational platforms is a much needed natural evolution for organisations dealing with consumers. While traditional bots automate tasks to guide customers, AI chatbots use machine learning (ML) to understand the very intent and context of customers, providing human touch with balanced automation.
These ‘intelligent’ chatbots will be indistinguishable from humans because they use statistical models of sentiment and predictive analysis. According to IDC data, around 63 percent of CSPs are now investing in conversational AI to improve and optimise client experience.
The role of natural language understanding (NLU) in developing conversational AI
Conversational AI uses NLU, natural language processing (NLP) and ML to engage in customer conversations. NLU provides the building blocks to interpret human language and messages. It recognises patterns and establishes what the user is trying to say, even when a similar message is expressed in different ways.
NLU is the understanding of the text given and classifying it into actual intents. ML determines the ideal reaction, and this reaction is transformed into comprehensible human language using natural language generation (NLG), another component of NLP.
Unlike conventional bots that depend upon sensitive keyword data, conversational AI chatbots use NLP and AI algorithms to process the dialogue in a conversation. It further uses this information to form better and more natural responses to customers’ concerns and queries. In simple words, conversational AI is bringing conversations to life.
Designing a conversational AI chatbot
Building a conversational AI chatbot has been challenging, especially the design. While designing a conversation, it's important to comprehend the capabilities of the AI brain and the human brain.
Key attributes of a conversational AI chatbot are:
- Building a personality
Businesses first and foremost must decide on a chatbot’s ‘personality’ to complement their brands and values, in the same way they look at language and tone for all their other channels. They need to decide which services and products the ‘personality’ will deal with.
Conversational AI bots must simulate a real-life conversation by providing customers with a sense of freedom while communicating. They get better at this over time using AI Markup Language, the background language which helps machines learn. They build their intelligence and personality further by processing customer data using ML algorithms.
- Understanding the core objective
One of the crucial aspects of chatbot design is the end goal for the bot. The actual purpose it is intended for must determine the design of the chatbot because this will ensure that it provides the right parameters for analysis during its learning process. This will mean it gets better and better at doing what it is actually supposed to do.
- Creating intent
Due to limitations, chatbots misunderstand the user's intent, provide incorrect answers, and fail to achieve their intended use. Creating intent requires building intelligence using customer data and common behavioural patterns. Once the customer intent is clear, conversational AI chatbots can ‘pop up’ to guide customers using the knowledge they have gained to date. Allowing users to create their own intent is also an interesting area to explore such as Alexa where users can set up rules for a chatbot to perform certain actions by providing a name and a list of utterances the user makes to trigger that intent. Here an increase in the amount of training data reduces intent detection errors.
- Contextual and meaningful conversations
Chatbots can find it difficult to differentiate a reaction to a previous answer from a brand-new query, so contextualisation must be a part of the chatbot’s ‘brief.’ Anticipating responses using past experience is an obvious choice for the brief, whilst another approach is to investigate all the chatbot’s confusions and note the previous messages to anticipate the misunderstanding and to see if there is any link between them.
- Performance metrics
Measuring the KPIs of chatbots is vital in order to establish whether the user experience is working, particularly to:
- determine if the chatbot is meeting the core purpose of its design
- optimise the chatbot based on its goals, or changing goals
- outline the best ways to drive audiences to the desired outcome
The Future of Communication: Conversational AI
Conversational AI and its applications are far deeper and more impactful than just chatbots. This technology can be further extended to establish intelligent and emotional connections between people and machines or between machines themselves.
- Person to machine (P2M) communication
Person-to-machine communication has revolutionised many aspects of business, with embedded sensors, processors and connectivity software now in products from cars to kitchen appliances. In a bid to improve the P2M experience, people-friendly technology will pave the way towards a better user experience. Instead of people struggling to learn the evolving technologies as is sometimes the case today, technologies will evolve to understand people better and make their daily lives more comfortable.
With profound awareness of human psychology and what people want, an incredible experience can be achieved between humans and machines.
- ‘Digital humans’: the next step towards the future
Digital humans present a further advance to chatbots, with ‘physical’, ‘human’ representations on screens, bringing truly meaningful connections to the digital world. Besides the emotional connection, digital humans add values like scalability and speed and can always be available.
A study from Harvard Business Review has shown that emotionally connected customers are four times more loyal to brands and more likely to spend twice as much. This emotional connection between the CSPs and their customers is being taken to the next level with unfathomed potential.
Chatbots are absolutely the future of customer service and as they learn to work with us and make themselves available when we need them. They are the future of customer engagement and customer experience in the digital world, where opportunities for deeper customer intimacy and trust is the need of the hour in a digital world. With various tricks up their sleeves, bots can make customer interactions simple, accurate, reliable, timely, high-quality, and emotionally attractive. They are probably among the most significant strategies to help CSPs expand revenue opportunities, increase customer retention and create richer and more enjoyable digital experience.
So as we look to this new reality, will we be disappointed when we get through to the non-digital human?