Conversational AI: The Future of Customer Experience
Learn about the power of conversational AI and how it is revolutionizing customer experience for businesses and consumers alike.
What is Conversational A.I?
Conversational Artificial Intelligence (AI) is the technology that allows machines to create natural language interactions with humans. It is the artificial intelligence that exists within applications and devices, allowing them to communicate with their users.
This enables consumers to communicate their needs without opening another program on their device. These interactions are commonly used in chatbots, mobile apps, and virtual assistants.
Conversational AI is the latest development in the world of technology. It aims to make human-machine interaction more efficient, intuitive, and natural.
Components of Conversational A.I
In order to successfully create a conversational AI, there are three key components that need to be present: Natural Language Processing(NLP), Text Analytics and Machine Learning.
Let us explore each of these in detail and highlight how to successfully combine them to achieve effective conversational AI.
Natural Language Processing (NLP)
In order for users to be able to communicate with a machine using voice or text inputs, the natural language processing layer is extremely important. This component has three key functions:
Speech Recognition: Enables the system to understand when a user has carried out a voice input and then uses that input to determine what action should be taken (e.g., if you said 'turn off the lights, this would prompt the conversational AI to turn off the lights in your living room).
Natural Language Understanding: 'Understanding' in essence means that the conversational AI understands what you mean by your input (e.g., if you say "I want to go to Rome", this would prompt the system to look up flights and hotels for your trip.)
Intent Detection: The intent detection layer is where the system can determine what the user wants to do by using natural language processing to interpret the meaning of their input. For example, if a user says "I'm feeling hot", the intent detector will recognize that the user is complaining about their temperature and could prompt them with recommendations for how to cool down (e.g., 'turn on air-conditioning').
The combination of these functions enables the natural language processing layer to convert human language into actionable data that can be passed on to other components in order for them to take necessary actions.
In most cases, if a machine is going to have conversations with users, they will need some sort of text analytics to help them understand what a user's input means. This can be done by using keyword and phrase extraction algorithms that identify the essential data from within large chunks of text.
When building a conversational AI, it is usually vital to make sure that the text analytics layer converts human language into indexable strings for easy retrieval purposes. This essentially means that the system must identify keywords or phrases that can be easily indexed into databases.
For example, let's say a user asks, "can you recommend me somewhere to go for dinner tonight?". This input could be converted into text analytics by identifying 'dinner' as an important word because it would help the system find restaurants nearby and then 'tonight' as another important keyword because it allows the system to understand when the person wants to go out.
The machine learning layer is arguably the most important part of a conversational AI. This is where learning takes place and enables technologies such as neural networks and deep learning to process data and make predictions.
The learning is essentially done through three processes: training, inference, and testing.
Training is where the machine learning model will be trained with data in order to build a statistical model that can then be used to predict any given outcome. The inference is then about finding the most accurate way to use this statistical model by making predictions based on the outcomes it has learned.
Lastly, testing is where the model will be tested against new data in order to ensure that it can actually make accurate predictions.
Combining Natural Language Processing (NLP), Text Analytics & Machine Learning For Conversational AI
When combining NLP with text analytics and machine learning, it becomes possible to give natural language understanding to conversational AI systems. This can be done by using deep learning models in which the machine has been trained with relevant data sets to understand human language and become more accurate over time.
Reimagining Consumer Experience with Conversational AI
Marketers are increasingly pushed towards providing contextual, personalized, and seamless experiences to engage with their customers. This is where conversational AI comes in.
Something as simple as a voice or chatbot capable of answering customer questions and complaints is now an effective way for companies to retain existing customers and acquire new ones. As per a report by Forbes, business leaders claimed that chatbots have increased sales by 67% on average.
Read on to learn how conversational AI can help businesses transform their Customer Experience (CX):
Improve the first impression: The first interaction a customer has on a business website sets the tone for their experience with that business as a brand. Conversational AI bot answers questions and helps customers as soon as they land on a website. Getting instantly assisted would leave a good impression.
Increase in Business Transactions: The newer, more intuitive digital channels such as chatbots and voice assistants like Alexa and Google Home make it easier for customers to order products, book flights or do any kind of business transactions and provide real-time support. Consumers expect brands to keep up with their preferences and adopt newer methods for communication.
Recommendations: Personalized recommendations are key for companies to retain customers and boost their sales. Chatbots powered by AI can give fast, accurate recommendations according to what users have previously browsed or ordered on the company's website. It can also help them find similar items based on past choices.
Isolation of Conversations: It is important for companies to identify what kind of conversations are suitable for each channel. AI Chatbots or virtual assistants are designed to handle one-on-one or group interactions with limited human involvement while email or phone calls are better suited for complex queries or to communicate with more people at the same time. Once you know where a conversation is most suitable, it becomes easier for marketers to direct customers through each step of the customer journey.
Turn Around Issues Faster: Since companies are already using multiple communication methods for customer service, it is challenging for them to address all complaints and queries in a timely fashion. With AI chatbots and virtual assistants, companies can respond more quickly and efficiently by sorting through several queries at once.
Create a More Human Customer Experience: The tone of an online conversation with a chatbot is more human-like, which makes a consumer feel that a business cares for its customers' concerns and takes them seriously. The use of natural language facilitates this kind of engagement. Chatbots are designed to follow up with customer service support requests and track complaints in real-time, but they also maintain the tone of human interaction. Conversational AI is not about simply using words and sentences; it's all about the attitude and style behind the text. The brand tone of voice and writing style must be replicated in chatbots and virtual assistants for a successful conversational experience. Brands that succeed in the future will be those that combine the best of what humans do – their warmth, empathy, and morality – with the best of what machines do – their scale and objectivity.
Simplify business processes: AI assistants allow customers to reach quick resolutions without having to wait for a representative, which can improve their overall satisfaction with a company's service. Companies thus are able to reduce costs, focus on more complex internal and external issues, attract and retain high-value customers and improve their brand perception.
Gain access to demographic information you never had before: A great example of this is the data TGI Fridays collected from its conversational AI bot. Based on how many people used the bot to make a reservation, they discovered that women were using it far more often than men. With this insight, they could determine how they would market their new drinks and desserts in order to attract a more gender-balanced audience. Here are a few core factors that can contribute to an enhanced conversational experience. These factors must be considered along with the right use cases to shape up your business strategy.
Personalization: Today's generation is smart enough to tell when they are being talked to in an automated fashion. Considering this, enterprises are focusing on leveraging intelligent personalization techniques to provide a rich and personalized conversational experience for their customers. During the onboarding of new customers, chatbots automatically initiate friendly conversations through personalized greetings. They can also understand the preferences of each new customer and ask them relevant questions such as where they are from or what is their primary goal for using a given service, which makes it easier for marketers to engage with them. This can be achieved by leveraging the information collected via customer feedback, data analytics, and behavioural analysis.
Interactivity: Customers often prefer multiple-turn dialog interactions as it makes them feel like they are interacting with a real person, so enterprises must aim for such a level of interaction as much as possible. Conversational AI goes beyond simple personalization and moves into truly intelligent interactions where the brand is both accessible and helpful at just the right time. Smart conversations are rewiring customer-brand relationships like never before.
In 2018, Google performed a demo of its Google Assistant AI in which the bot made a phone call to fix an appointment for a haircut. The receptionist on the other end never realized she wasn’t speaking to a human.
During the call, Google Assistant “did an uncannily good job of asking the right questions, pausing in the right places, and even throwing in the odd ‘mmhmm’ for realism,” James Vincent at The Verge writes.
The future of conversational AI is looking bright, and it’s only going to become more commonplace as people get more comfortable with the technology.
As customer service evolves and conversational AI becomes better at providing accurate information and solutions, businesses will be able to reduce the overall costs while still providing high-quality support for their customers.