Using Conversational AI and Chatbots to Transform the Neobanking Experience
Neobanking, a.k.a. challenger banks emerged from the mounting demand for easy and round-the-clock access to efficient banking processes. With no physical offices & branches, the level of precision, efficiency, and ability to handle load is a deal-breaker. These make Conversational AI and chatbots natural choices for creating the perfect Neo-Banking experience. They successfully enable Neo-Banks to run low-cost, easy-to-use, and digitally accessible services. Think of Neo-Banking and conversational AI like two peas in a pod.
As per current trends and reports, 41% of adult users use voice search at least one time a day. This means that voice-based interactions are steadily on the rise.
A significant contributor to the upward trend in voice searches among customers is the advent of smart home devices. By ensuring that the responsive voice coming from the smart home devices remains human-like and provides an empathetic experience, users are more inclined to interact with services such as chatbots.
In a detailed report published by the American Psychological Association (APA), data shows that even though technology has increased the number of communication channels between users, voice is still preferred. This is because chatbots lend a more intimate experience to interactions and help create stronger bonds.
How do voice bots and chatbots work?
Conversational Artificial Intelligence (AI) is a software solution used by business houses and other chatbot users to engage with customers through email-based bots, chatbots or voice bots. Voice bots interact with customers by analysing their vocal data and formulating the most accurate response by encoding and decoding spoken content. With the help of Machine Learning, the system can train itself to continuously improve its accuracy and provide better service.
On the surface, the use of chatbots might seem very linear. However, various components go into this technology to ensure accuracy and speed.
Various Components of Voice Bots
Automatic speech recognition (ASR) – the backbone of voice bots
Automatic speech recognition or ASR forms the backbone of voice bots, as it’s directly responsible for converting spoken verbatim data into text. When a customer speaks to an AI bot, a chatbot or a voice assistant, the ASR component uses an audio feed to transcribe the voice message into a wav file. The wav file is then filtered by removing background noises and other disturbances, to provide a smoother experience to the customers, after which it is then broken down into phonemes. Phonemes basically define how words sound, and by linking that data together, the ASR can deduce what the customer has said.
Natural language understanding (NLU) for voice and text-to-speech bots
Natural language understanding (NLU) is a sub-branch of the larger topic of natural language processing (NLP). NLP deals with the entire spectrum of functionality for voice bots, where it interprets input data, deciphers the meaning and formulates responses for the users. NLU plays a vital role in this process, by helping the algorithm identify intent and tone within a short time. In other words, NLU helps the AI-powered virtual chatbot assistant distinguish conversational elements and take the business interaction forward.
Voice bot conversation module for correct responses
A well-defined conversation module allows users to effortlessly interact with the voice bot service without having to follow a directive course (like in the case of IVR systems). The entire style and experience of interaction revolves around the user’s service requirement and intent, and subsequently retrieving relevant information to help the user.
Text-to-speech AI voice bots system
Text-to-speech or TTS is the business component that ‘reads aloud’ the text data that is visible on a computer screen or digital interface. In other words, the system uses various deep learning techniques over time to read a response and mimic a human voice when reading it aloud to the customer. When the NLP-NLU component analyses the input data from a user, it formulates a relevant service response that is fed into the TTS component. The output from the TTS is what the customer hears and experiences.
How do voice bots comprehend complex languages and accents?
Global adoption of voice-based technologies has established ‘voice’ as a preferred business service medium amongst customers, and the numbers and data speak for themselves. However, a common roadblock that most conversational AI solution providers endure is the process of training their interactive voice bot for the optimum user engagement and experience.
Today, there are over 7,000 different languages and dialects. Yet, this isn’t the real problem. If we were to take one single language, we’d soon learn that this one language is spoken differently across the world.
The English language is the biggest example of this problem. English is spoken across all 195 countries in the world and has over time grown to be the official or business language for 67 of them. That means there are over 100 different accents for the English language alone! For each accent, there are a definitive set of phonemes that makes it difficult for the AI-powered voice bots, chatbotsand text-to-speech bots to comprehend.
With the help of AI voice bots speech recognition optimization
Speech recognition optimization is a branch of computer science that deals with the business of computational linguistics. It helps the AI understand specific languages and accents by benchmarking them against an existing database of knowledge. With the help of a voice biometrics solution, the AI can, in very little time, identify the customer’s accent to begin processing conversational input.
By implementing pre-trained multilingual AI voice bot speech encoders
With the help of modern machine learning models, text-to-speech bots can be trained with billions of customer conversations to help create a strong foundation for the NLP component. For example, Gnani.ai is continuously strengthening its foundation by training its model in 20+ international languages to broaden its service of customer experience. As opposed to building new models for multiple languages, we can now deploy any language on-demand in a few days or within very less time.
By utilising a multilingual AI voice bot value extractor
Value extraction and its degree of data service accuracy could make or break your customer experience (CX) goals. When a customer interacts with an AI voice bot, certain crucial values regarding name, address, reference numbers etc., are extracted irrespective of the language used. If a user is talking to an AI voice assistant in French, crucial values might be in English (age, phone number). Hence, the value extractor must be trained to analyse languages and decipher critical business data.
What lies ahead with AI, voice and text-to-speech bots?
The global market size of AI-powered virtual assistants will be worth a business of over USD 1.3 billion by the end of 2024, and trends point towards voice-based assistants owning over 50% of this service share. Hence, it’s clear that the power of ‘voice’ will take over business processes and customer engagement initiatives and voice bot experiences.
If you’re interested in learning more about how you can leverage AI and voice botservice for your business, talk to us any time.
Frequently Asked Questions
What is AI voice bot?
Voice bots are software powered by artificial intelligence (AI) that enable a caller to navigate an interactive voice response (IVR) system with their voice using natural language generally. Callers don’t have to listen to the menus and press corresponding numbers on their keypads.
What are the examples of voice bot?
Some common examples of voice bots are Amazon Alexa, Apple’s Siri and Google Assistant.
Methods of Self-Learning in AI – Which One Works Best for Analytics?
Perhaps what gives artificial intelligence that characteristic of ‘intelligence’ is its ability to learn using deep data and to be taught through large datasets. AI applications can either self-learn or are fed large datasets usingself-supervised learning models. Naturally, there are debates about which learning model or guide is better, with people comparing self-learning to the effectiveness of learning a new language by immersing in a new culture. But is the science behind it that simple?
The history of Conversational AI bots follows the same trends as other technological advancements. They developed gradually until the 90s, and then the new developments rolled out faster than ever seen in the annals of technological evolution. From the 2000s, there have been improvements in Conversational AI technology at a blink-and-miss pace.
A no-code/low-code chatbot is a ready-to-use computer program that provides support by automating tasks for you. You don’t have to create it yourself because there is a platform, a no-code bot builder, providing an interactive interface to your customers with visual elements that lets you drag and drop the features you want. Choosing a no-code voice bot builder platform gives you a lot of control over bot customization channels.
It is a smart solution for companies that don’t have or don’t want to use their resources to makechatbots from scratch. The low-code approach is becoming popular because it lets non-technical developers without expertise create software applications to meet their needs. Customers with little or no knowledge of coding can use these intuitive interfaces to make mobile apps, websites, and more.
Customers want nothing less than a 5-star experience these days, that too every time they interact with any brand. A focused customer-first business will try to incorporate the latest technologies to keep itself afloat. More than 95% of customer interactions will be taken over by AI by 2025. That’s the kind of impact that AI and its related technologies have had on businesses around the world.
In this article, we will look at some of the latest conversational AI statistics that will make you sit up and take notice of this technology.
In the second half of the 20th century, numerous science fiction movies had talking computers executing voice commands. The characters and computers would engage in natural conversations and the latter carried out complex tasks with minimal instruction.
In 2022, these scenarios are fast turning into reality. Virtual assistants are becoming indispensable for daily life, business processes, and more. Conversational AI is evolving at a fast pace making all these automation scenarios possible.
Conversational AI in customer service has completely changed the outlook of call centers when it comes to handling holiday hours. Even from the other side, it’s estimated that 25% of customers will switch to voice interactions by 2023. With tremendous potential on the cards, AI-powered voice bots are here to boost sales and present customer-oriented opportunities.
A Deloitte study affirms this and further states how voice commerce will be responsible for 30% of online sales by 2030! For instance, Cigniti experiences over a 40% conversion rate! Also, products like Apple, Siri, and Amazon Alexa are fundamentally based on the concept of conversational AI.
The first successful AI was developed in 1935 by British Mathematician Alan Turing to decipher the Nazi’s encrypted message. From the day of its invention to date, AI is being used to simplify time-consuming jobs and reach goals quickly.
The beginning of 2021 itself had lots of expectations, and every business was looking forward to making the most of this year to compensate for what they have missed in the year 2020 due to the pandemic. AI has played a significant role in maintaining normality in the new normal. Tech giants have released their own AI-related products and platforms to catalyst AI adoption.
The global market of Artificial Intelligence (AI) has provided around 341.8 billion USD revenue in 2021 and this growth is expected to reach over half a trillion USD by 2024. This growth is due to the fact that many companies around the world are adopting AI, especially conversational AI to improve their customer experience. Companies are using conversation AI as it can respond to instructions, have human-like conversations with customers, understand customers’ requirements by analyzing conversations and deliver the best possible solutions accordingly. Therefore a report says, by 2024, the number of AI-powered digital voice assistants is expected to reach 8.4 billion units.
This post will help you understand how companies are actually using AI and making the best out of it. But before diving into that discussion, let’s have a brief view of what conversational AI bots are.