Conversational AI (artificial intelligence) today is probably the closest technology has come to mimicking human interactions.
That’s one of the reasons this tech has grown in popularity — and for customer experience in particular. In a world where businesses try to engage their customers on a personal level across digital touchpoints, virtual assistants and AI tools make effective (and cost-efficient) allies.
But, the workings of artificial intelligence are often complex. If you want to know more about this technology, start here; our beginner’s guide will cover these essential aspects of conversational AI:
Let’s dive in.
What is conversational AI?
Conversational AI is a set of technologies that allow an application to communicate with humans via voice or text. This is possible when the application understands what humans are saying (or typing) and formulates an appropriate response.
The most advanced function of this tech is using machine learning to learn over time. This helps the system improve both its understanding of human speech and its ability to construct the right replies.
A conversational solution is usually a user-facing chatbot, virtual assistant, or voice assistant. For example, people can ask a question to a pop-up widget (often looking like a robot with antennas) and artificial intelligence will make sure the conversation sounds and feels natural.
The technology behind conversational AI
Conversational AI technologies revolve around machine learning, natural language processing, and advanced speech recognition.
Machine learning (ML)
Machine learning is an AI method that allows systems to use data to continuously improve their ability to recognize patterns and make decisions. The more data is fed into a system, the more capable that system becomes.
Conversational applications use ML to better understand human interactions. What do humans mean? What responses do they expect? How can these responses be more accurate or personable? Machine learning helps the system answer these questions over time.
In customer-facing chatbots, learning translates into more questions answered successfully and fewer fallbacks to human agents.
Natural language processing (NLP)
NLP is the technology that allows the machine to analyze human language. NLP breaks down human sentences to identify actions or information.
One part of NLP is natural language understanding (NLU). This function deals with, as you may have guessed, understanding intent. Using NLU, the system can dissect and recognize the meaning behind a person’s words. That’s the first step in any successful conversation — it’s what humans naturally do (most of them at least).
Automatic speech recognition (ASR)
While NLU works well with text-based user inputs, what happens when a human speaks? Then, the system will need a way to transform verbal speech into a format it can understand. That’s what ASR does.
ASR will work together with NLU to make sense of what the user is saying in voice-based applications.
Dialogue (or dialog) management is the part of the application that will determine what the correct response is. For example, if a user asks whether an item exists in inventory, the dialogue management will initiate a dialogue about inventory.
This system's job can become complex because it can take into account context and the flow of the conversation. For example, if the user asks for price of a particular product and then asks merely for color, dialogue management will understand that the second question refers to the item mentioned previously. Or, if the AI asks for a location and the user replies with both a location and a date, the chatbot will keep the knowledge of the date and will not ask again.
Natural language generation (NLG)
So, the AI understands what you said and has determined the response via dialogue management. How can it put that response in a format the human user would understand?
What it needs is NLG — this AI function allows computers to formulate words in human language. It’s basically the technology that makes this whole interaction “conversational”.
One of the most intriguing applications of NLG is personalization. For example, the Orlando Magic basketball team used NLG to send automated emails to fans, personalized based on their past behavior. This kind of personalization can be applied to various employee- and customer-facing communications.
This technology isn’t necessary for a conversational bot to work, but it does help take things up a notch, providing a way to process and identify user emotions by analyzing the sentiment of the words they’re using.
And that’s the ultimate way to make conversational artificial intelligence truly (although not completely) mimic humans.
Emotions are a core part of our interactions with other people. Especially when it comes to customer experience, knowing that your customer is frustrated helps you apply empathy to your responses. You can use it in other ways, too — like keeping track of happy customers to see the impact of your brand.
How does conversational AI work?
Conversational AI uses ASR, NLP, and machine learning to understand and respond to users, while becoming better at it over time.
Here’s a breakdown of how a conversational application would work:
- The user asks a question, whether by voice or text.
- The application uses NLU to understand meaning and intention; aided by ASR if it’s a voice-based interaction.
- The application uses NLG to figure out the appropriate response and put it into words.
- The application uses ML to learn and finetune responses over time.
Despite its many effective uses in business, this kind of AI requires substantial training and can only do very specific tasks. It’s not an advanced form of artificial intelligence that thinks and plans (that’s the stuff of science fiction for now).
What's the difference between chatbots and conversational AI?
‘AI-powered chatbots’ is a term often used interchangeably with conversational AI. But, not all chatbots use artificial intelligence.
A chatbot can work on a very basic level, too — giving pre-determined greetings, asking specific questions, or providing standardized answers. This type of chatbot is more like a rule-based answering machine, and may often have trouble understanding users or providing the right answers if it hasn’t been specifically trained to.
AI-powered chatbots, though, count as conversational AI because they use the related technologies to interact with users.
So, the two terms aren’t exactly the same, but there’s significant overlap.
Curious to learn more about chatbots?
Get our complete guide to learn what makes a successful chatbot, see use cases, and how to implement your own bot.
Use cases of virtual assistants
Conversational AI is usually a way to offer faster and smoother support over digital channels. A chatbot on Messenger, an in-app virtual assistant, or an AI-powered bot on a website can all be common use cases for conversational AI. Alexa, Siri, Cortana and Google Home are the more advanced examples of this type of technology.
Here are more general examples:
AI chatbots can offer instant support whether it’s after hours or in cases of emergency. Especially when it comes to non-complex issues, it’s not productive for customers to wait on the phone or even for an answer in live chat and email. With AI, they can get an answer much faster, while at the same time keeping some part of the conversational aspect of human interactions.
Provide personalized recommendations
AI-powered chatbots can collect data and understand what each user is likely to want. Setting up chatbots to suggest products or content based on those insights is a great way to engage users. For example, if a user is looking for ski goggles, the chatbot can help them decide and then try to recommend other ski equipment.
Most often a use case in banking, AI can help users with various transactions. From paying bills to tracking expenses and making projections to canceling orders, conversational AI is an easy and pleasant way for users to handle everyday tasks.
Help with training and onboarding
Training and onboarding (both for customers and new hires) can be long and complex processes. The AI can help relieve the burden from human instructors or customer-facing roles, by offering quick and helpful advice. Your bot can be constantly on-call for any customer or employee who needs help with a new product or process.
Of course, there are different use cases according to industry. Check out a more detailed overview of what AI chatbots can do per industry.
7 benefits of conversational AI technology
The whole Internet has been getting more conversational with time — you have to have a more personable connection with people, machines, and businesses when navigating online.
Enter conversational AI.
Apart from the “cool” element, this technology has substantial benefits for business, too. Here are the main benefits of conversational AI:
- Achieve more personalized and easy interactions. Having the choice to take part in a conversation, instead of reading dry FAQs, can be more engaging — especially if the AI uses past interaction history or other data to personalize each interaction. It’s also far easier than navigating a site and clicking through multiple pages to get answers.
- Have greater accessibility. Because of how this tech understands both verbal and written speech, you can cater to a more diverse audience. For example, you can serve people with hearing and vision impairments, or older people who may struggle to navigate the web, right from the same widget.
- Attract customers. Especially in SaaS, insurance, or similar industries, lead generation is a big goal for sales and marketing teams. Virtual assistants can be there to welcome and engage visitors who browse your site, increasing the chances they’ll become your customers.
- Better user support. AI chatbots are “on” 24/7, and ready to help users and visitors whenever humans aren’t available. For example, a conversational banking app can help a customer lock their stolen credit card immediately, without any waiting time. Same goes for healthcare where, for instance, contacting a claims rep or booking a doctor’s appointment via AI can be far less frustrating than the usual process.
- Help your team. Though the most common use cases are customer-facing, we shouldn’t forget about the employee side. Whether you have an HR chatbot to help employees with filling forms, or a smart chatbot that suggests answers to customer support agents, conversational AI can definitely enhance the employee experience.
- Reduce costs and employee burnout. AI chatbots can handle multiple conversations at the same time, at no extra cost. This means they can take work off your team’s plate, so human agents can focus on more complex requests. If they’re used for training or onboarding, they can also reduce the number of hours and costs for these processes.
- Gather and analyze data. Conversational AI learns on its own by collecting data. Often, there is data you can also use to make business decisions. For example, what are common questions users have that you could add in your knowledge base or make videos of?
How to implement conversational AI for customer service
If you’ve decided to try your hand with this technology to improve customer experience, here are a few things to consider:
- Define your use case. The technology is certainly intriguing, but you need to be certain of exactly what you need it for. Support, lead generation, employee productivity? Or perhaps you want it as part of a strategy to bring the physical and digital worlds of customer service together? Define the purpose and set some goals or success criteria around that.
- Do market research. There are many providers of conversational AI out there. Find the one that meets your needs. Often, it’s not just an AI chatbot you need, but rather a full customer experience platform, where every digital engagement tool will be connected to each other (think omnichannel strategy).
- Train your AI. Whether by feeding it data, constructing sequences, or writing scripts, training the bot properly is essential. For effective chatbot design, you may need an expert’s help, depending on how complex your use case is.
- Finetune. Don’t set it and forget it. Analyze the data your conversational AI gathers — especially from instances where it was unable to help users. This will enable the AI to learn over time, but it will also give you important insight into what your users need.
Have you ever tried your hand with chatbots, machine learning or other AI applications for customer service? We'd love to hear about your personal experience with artificial intelligence. Let us know with a comment.