June 21, 2021
Everything You Need To Know About Conversational AI
With chatbots foreseen to save businesses and consumers an immense $5 billion hours by 2023, superior conversation evidently packs an exceptional value. However, while basic interactive voice response systems and legacy text chatbots carried the burden of customer interaction for the past 20+ years, they certainly won’t be the drivers of the time-saving trend we’re about to experience. So, would you like to know what the secret sauce that will drive intelligent customer conversation for the years to come is? Enter artificial intelligence.
Artificial intelligence, especially one of its most common applications, conversational AI, is the synthetic brainpower that expands the scope of present-day chatbots from delivering stiff, preset replies to intelligent, adaptive action and makes machines capable of understanding, processing and responding to human languages. Organizations of the future will want to explore the path from simple customer support to agile frontend and backend operations by utilizing conversational AI in their operations.
So, to be among those that will get the most optimal results by implementing this groundbreaking technology, we’ve compiled this blog post to help you learn what your business can expect from current AI bot solutions. First, we’ll define conversational AI and see how this technology works. Later, we’ll go through conversational AI’s substantial benefits and most common use cases to help you get a clearer picture of how it can transform your business for the better by optimizing your customer service efforts, improving user experience, and automating particular business tasks and operations. Without further ado, here’s everything you need to know about conversational AI.
What Is Conversational AI?
Basically, conversational AI is a group of technologies behind speech-enabled apps and automated messaging that provide human-like interactions between humans and computers. Conversational AI models combine natural language processing and machine learning with traditional software like voice assistants, chatbots, or IVR systems to help customers through either a typed or spoken interface. This solution can help software communicate like a human customer service representative by recognizing text and speech, understanding intent, deciphering different human languages, and responding in a way that mimics human conversation.
Applied conversational AI demands both art and science to develop successful applications that incorporate personalization, context, and relevance within the human to computer interaction. Moreover, conversational design, a discipline devoted to designing flows that seem natural, is an essential component of developing conversational AI applications.
Conversational AI solutions like the Y Meadows model can be offered over text and voice modalities. That way, conversational AI could be delivered through different devices and channels that support the modalities mentioned above—from smart speakers and phone calls for voice modalities to SMS and webchat for text ones. Y Meadows model can offer a result that’s indistinguishable from an experience delivered by a human and complete the same tasks, with the same if not better accuracy and with way less effort than it could be if a human agent delivered it.
Components Of Conversational AI
Conversational AI fuses NLP with machine learning. The natural language processing process surges into a continuous feedback loop with machine learning processes to improve conversational AI algorithms. These two make conversational AI’s primary components that allow it to process, understand, and generate a response in a natural way.
Machine learning is a subfield of artificial intelligence, made up of algorithms, features, and data sets that steadily improve themselves with experience. So, as the input grows, the AI platform machine gets better and better at recognizing patterns and uses them to make accurate predictions.
NLP is the current method of analyzing language with the help of machine learning used in conversational AI. Prior to machine learning, language processing techniques evolved from linguistics to computational linguistics and statistical NLP. In the years to come, deep learning will further advance the NLP capabilities of conversational AI.
Moreover, NLP consists of four basic steps: input generation, input analysis, output generation, and reinforcement learning. Unstructured data is first transformed into a format that a computer can read and understand, which then gets analyzed to create an appropriate response. Machine learning algorithms improve their response quality over time as they learn. These four basic steps can be broken down further like this:
- Input generation. Users provide input through an app or a website, and the format of the input can either be text or voice.
- Input analysis. If the data is text-based, the conversational AI solution will use natural language understanding to decrypt the meaning of the input and derive its intention. On the other hand, if the input is speech-based, it’ll leverage a combination of natural language understanding and automatic speech recognition to analyze the input.
- Dialogue management. During this phase, natural language generation, a key component of NLP, creates a response.
- Reinforcement learning. Lastly, ML algorithms refine responses overtime to ensure better accuracy and customer satisfaction.
How Does Conversational AI Work?
So, how do these essential components and processes of conversational AI transfer to a real-time situation where we have a customer with some inquiry or request on the one side and software like a chatbot or a virtual assistant on the other? How does a chatbot get from an asked question to an adequate response?
Well, conversational AI comprises and engages in contextual dialogue using the help of NLP and additional AI algorithms. In the beginning, the AI must understand what the customer is trying to say and the question’s intent. Natural language understanding deciphers the customer’s words so that the AI can understand the user’s intent even among shortcuts, grammatical errors, and idiosyncrasies, and remember context from one statement to the next, comprehending what’s being said throughout the dialogue.
Afterward, the conversational AI solution determines the adequate response based on its understanding of the customer’s intent using machine learning. As the AI model answers questions over time and as human representatives help guide its knowledge, it learns more variations of the same intent and which responses are most appropriate for each intent.
Lastly, using natural language generation, the chatbot or software, with the help of conversational AI, generates a response in a format that the customer quickly understands.
Benefits Of Conversational AI For Businesses
Everyone from small to medium companies, to large enterprises and organizations can find fantastic benefits from leveraging a conversational AI platform like the Y Meadows model. The manifold benefits which come along with such a model can broadly fit into one of two main categories:
- Customer-centric benefits
- Organizational-centric benefits
First of all, implementing conversational AI within your software can help deliver quick responses to clients of all kinds. Modern-day customers are busy and impatient, so providing them with fast response times is one of the most obvious ways to make their experience more positive and delightful.
After the initial response, conversational AI can also play a vital role in educating customers through helpful prompts and probing questions. It’s widespread for customer support representatives to give customers tutorials about the company’s product or service repeatedly. With conversational AI, these tutorials can automatically leverage a customer’s profile data to ensure customers receive personalized guidance.
Lastly, conversational AI has proven itself over the years as a valuable guide for clients who aren’t sure where to go. Within banks, for instance, conversational AI is often helpful in directing clients to the appropriate department when they call in.
Beginning with your customer's experience, conversational AI can help your business better convert website visitors into leads through intuitive, easy-to-engage-with live chat windows and software.
From there, conversational AI can help you improve your overall conversion rate. By asking tailored questions, conversational AI-powered chatbots can excite customers and support sales representatives' efforts down the funnel.
At last, conversational AI can play a vital role in the post-acquisition process in triaging customer service inquiries. Y Meadows AI-powered solutions can help your business experience highly automated customer experience management systems and streamlined workflows that will help your customer service department resolve queries more effectively and free up time so that human representatives can focus on more complex issues.
Examples And Use Cases Of Conversational AI
When people think of conversational AI, voice assistants and online chatbots frequently come to mind because of their omnichannel deployment and customer support services. These conversational AI software have extensive analytics built into their backend programs to ensure human-like conversational experiences whenever they interact with an actual person.
Conversational AI is an exceedingly lucrative technology for companies and organizations of all sizes, helping them become more profitable through automating specific business processes like customer service operations. And even though an AI-powered chatbot is the most popular form of conversational AI, there are many other examples and use cases for this technology in enterprise environments. The most notable examples include:
- Providing top-notch online customer support. As online solutions like chatbots are immoderately replacing human representatives along the customer journey, they are utilized to answer FAQs around subjects, like delivery options, or provide personalized advice, cross-sell products, or suggest sizes for customers, this way changing the way we think about customer engagement throughout websites and social media platforms. Use cases for conversational AI include messaging chatbots on eCommerce websites through virtual agents, messaging apps, and many other tasks usually done by voice or virtual assistants.
- Better accessibility. Organizations can become more accessible by reducing entry barriers, especially for users who utilize assistive technologies. Frequently used features of conversational AI for better accessibility include language translation and text-to-speech dictation.
- Optimizing HR processes. Several HR processes can be optimized by utilizing conversational AI, like the onboarding process, employee training and education, and updating various employee information.
- Computer software. Numerous office environment tasks can be simplified by utilizing conversational AI, like autocomplete when you search for something in the organization’s system or spell check when you file important documents or reports.
Overall, all conversational AI applications and use cases can replicate human conversational experiences and lead to higher customer satisfaction rates and automated business processes.
Final Thoughts On Conversational AI
The implementation of conversational AI within your existing software can help your enterprise reach more customers, provide superior customer support, turn leads into loyal customers, and boost profits by a margin previously unfathomable. If you want to learn more about how Y Meadows’ AI-driven solutions can help your business thrive, please don’t hesitate to contact our representatives and set your company for future success.
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