What Is Conversational AI And How Does It Work?

Do you want to transform your customers' buying experience and enhance sales using conversational AI? The technology will not only deliver a personalized experience to your customers but also enhance overall customer satisfaction. Conversational artificial intelligence is growing more prevalent each day. Whether you’re speaking to a voice assistant or interacting with a chatbot, technology is working behind the scenes to analyze what is being said (usually called “intent”), and build a suitable response.

Here’s what we’ll cover in this article:

• What Is Conversational AI?
• Why Is Conversational AI Important?
• Types Of Conversational Intelligence
• How Does Conversational AI Work?
• Components Of Conversational Intelligence
• How To Implement Conversational Intelligence?
• How To Automate Conversational AI?
• Conversational AI Examples And Use Cases
• Aviso AI's Advanced Conversational Intelligence

What Is Conversational AI?

Conversational artificial intelligence (AI) is a set of technologies that work behind voice-enabled and automated messaging applications to provide human-like interactions between humans and computers. It allows computers to understand, analyze, and respond to human text and speech inputs. Conversational artificial intelligence can understand human intent, decode different languages, and respond in a way that's similar to humans. There's a lot of conversational intelligence involved that works behind the technology. That's because human language consists of complex scenarios due to the involvement of multiple meanings of the same word, slang, clarifications, interruptions, etc. Human text, tone, voice, and gestures also need to be analyzed. Due to this, conversational intelligence provides capabilities that go beyond predefined paths.

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Difference Between Conversational AI And Chatbots

Through conversational AI and chatbots are often used interchangeably, they are not exactly the same. Here are the main differences between the two:

• While conversational AI can process text and voice commands; chatbots can manage only text commands.
• Conversational intelligence can be deployed on voice assistants, websites, and smart speakers; chatbots can only be used as a chat interface.
• Conversational artificial intelligence considers natural language processing (NLP); chatbots work on pre-defined scripted conversation text flow.
• Conversational AI is focused on dialogue; chatbots are focused on navigation.
• Conversational intelligence can manage wide-scope and dynamic interactions; chatbots find it hard to manage out-of-scope tasks.

Why Is Conversational AI Important?

Organizations use conversational artificial intelligence in their sales, marketing, and customer support processes to offer a highly engaging customer journey. For instance, conversational AI for customer service helps organizations manage a lot of repetitive support tasks. As conversational AI doesn’t depend on predefined scripts, it allows businesses to automate customized support for each customer. It helps make each interaction unique, reduces the resolution time, and enhances customer satisfaction. Also, Conversational intelligence can be deployed in different customer service channels such as chat, voice, email, messaging, and social media. It allows organizations to scale their customer support to new channels to satisfy customer needs.

Also Read: How To Forecast Sales

4 Types Of Conversational Intelligence

Here are the different types of conversational AI that you can deploy in your organization:

• Chatbots: These were introduced to automate customer support. You can find chatbots in various places such as websites, display advertising, and Facebook Messenger. They mostly respond to a customer's basic support queries and help them find products they want to buy.
• Voice Assistants: These are similar to chatbots, but you need to speak out loud to interact with them. These mostly help you make phone calls, answer queries, set reminders, and alarms, play music, or provide weather updates. Examples are Amazon Alexa and Google Home.
• Mobile Assistants: These include Siri, Google Now, and others, and work similar to voice assistants. They help you perform tasks that need to be done quickly while you are doing something else such as driving or walking.
• Interactive Voice Recognition Systems (IVRS): These are used to automate customer service support and are used in contact centers. They help reduce service costs and enhance customer satisfaction.

Also Read: Importance of Sales Pipeline Management

How Does Conversational AI Work?

Conversational AI works by combining machine learning (ML), natural language processing (NLP), and automated speech recognition (ASR) to analyze and contextualize the spoken and written words. It identifies and comes up with the best possible way to manage and respond to human input. Conversational AI breaks down sentences to their root level. It pulls out human intent by determining patterns that it has been trained to find. It considers factors such as grammar, slang, synonyms, and more. Here, intent refers to what the human is trying to achieve.

Components Of Conversational Intelligence

Conversational AI includes four main components: input processing, input analysis, output generation, and reinforcement learning. These four components are explained below.

Input Processing: A human provides text or voice input through an application or website. For spoken words, speech recognition converts voice into text for the computer to read.

Input Analysis: The conversation engine uses NLP to decode the meaning and determine the intent of the text.

Output Generation: The application builds a response based on the intent using Dialog Management. Then, NLP analyzes and converts the response into a format that's understood by humans.

Reinforcement Learning: ML algorithms learn from the whole process and refine the response going forward to provide a better response in the future.

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How To Implement Conversational Intelligence?

To implement conversational artificial intelligence services in your business, the best way to start is by integrating your existing systems (such as CRM or sales technologies) with NLP and ML. This will allow your technologies to accurately process customer interactions and automatically respond back with the most relevant answers. This will enhance your business processes, improve engagement with customers, and increase customer satisfaction.

How To Automate Conversational AI?

To become an automated conversational AI company, you must automate all customer interactions (voice and text-based). For that, here is a list of action items you should do:

• Use a low-code conversation editor to manage all conversations with a flexible interface.
• Deploy AI-based language understanding to get language support via NLP.
• Follow omnichannel integration strategy and deploy your bots on different channels.
• Integrate live chat with your contact center solution.
• Set up automated regression testing of all customer interactions to meet business objectives.

Also Read: How To Accelerate Revenue Process

Conversational AI Examples And Use Cases

When talking about conversational AI examples, the most common things people have in their minds are AI-powered voice assistants and online chatbots. Below are a few use cases and examples of conversational AI across different industries:

Retail:

From surfing a retailer website to buying products through a mobile app, conversational AI helps customers quickly find their desired product. AI-powered voice assistants help people navigate through different categories on websites, find products, and place the order. This helps improve the sales conversion rate and increase revenue for retailers.

Banking:

Banks need automated solutions to provide quick assistance to their customers. Conversational AI in banking helps banks provide customized support, and automate repetitive tasks such as the collection and processing of data. It helps improve customer engagement and understands customers' preferences.

Healthcare:

AI assistants help automate healthcare tasks such as collecting patients' data, scheduling appointments and sending appointment reminders. Some conversational AI solutions also help patients select the right healthcare plan and solve medication queries. This reduces a lot of human involvement and cuts the response time.

Also Read: Components of Sales Enablement

The popularity of conversational artificial intelligence is growing for good reason. A growing number of organizations want to leverage AI to enhance their customers' buying experience. Conversational AI helps businesses scale their customer experiences by offering solutions to complex issues within seconds. So, what does this mean to you? As conversational intelligence becomes more common, your customers will expect it from you and your competitors will fill that gap. Businesses that ignore fast-growing technologies and fail to meet customer needs often get left behind. Leveraging AI to power customer interactions can enable sales by making the buying process more personalized, contextual, and simpler. To leverage conversational AI to enhance sales performance, it's critical to create a digital sales ecosystem, where AI drives conversations on its own and allows sales reps to focus on value addition.

Aviso AI's Advanced Conversational Intelligence

Aviso AI is a recognized player in the broader revenue intelligence space. Aviso tracks different signals across Go-to-market functions, including in Sales, Marketing, and Customer Success. Our Conversational Intelligence capabilities are a crucial pillar of our whole-body approach to revenue optimization. Just as high performance athletes use the best signals to drive performance, Aviso AI also uses different signals including human conversation signals via audio and video to drive revenue performance.

Aviso’s Conversational Intelligence (CI) capabilities provide sales and revenue leaders the ability to know what their customers are actually saying and its impact on winning or losing a deal. Aviso's CI analyzes the text, voice, tone, facial recognition, and gestures on a customer call with state-of-the-art deep learning and NLP algorithms. It analyzes speech patterns and sentence structures to show sentiments with visual indicators and provide subtext of customer intent.

• At Aviso, we focus on the subtext of the conversations we analyze i.e, what’s not being said vs reporting just the news; it’s about whether the customer is ready to buy; in next 1-2- 3 months vs 6-9-12 or not at all. With Aviso, you can focus your finite amount of time & energy on the deals that matter that will move the needle for you that of your team.
• Aviso also provides real time coaching during calls, tracks customer intent, and syncs call notes directly to your CRM. Aviso's Deal intelligence integrates with sales reps' activities, provides product and competitor insights, and enhances the coaching experience with visual indicators via computer vision.
• We take the guesswork out of the equation leveraging NLP and our proprietary models analyze the subtext of what a person is saying through Artificial Intelligence by analyzing speech patterns, vocal frequencies, tone and sentence structure.

Aviso takes all signals and combines them with our predictive Machine Learning and Deep Learning models to provide 98% accuracy in predicting when a deal will close at scale. Coupled with Aviso's Digital Identity graph and rep activity signals by stage, we provide companies with a true Revenue intelligence & Operations platform that has no parallel in the marketplace today. Learn more about Aviso’s platform or contact us today to see how we can help you and your team drive more sales.