Movies can distort our expectations of AI, either by giving us too high expectations or by making us think that it is out to destroy humanity.
But reality is not at those levels. In fact, you already use artificial intelligence in your daily life, but it’s so ingrained in your technology that you probably don’t even notice. Netflix and Spotify both use AI to personalize your content recommendations. Siri, Alexa, and Google Assistant also use it.
Conversational AI, like what Quiq uses to power our chatbots, takes artificial intelligence to the next level. See what it is and how you can use it in your business.
What is conversational AI?
Conversational artificial intelligence (AI) is a collection of technologies that create a human-like experience. It combines natural language processing (NLP), machine learning and other technologies to enhance facilitated conversations. This can be used in many applications such as chatbots and voice (like Siri and Alexa). The most common use case for conversational AI in the business-to-customer world is the messaging experience with a chatbot.
Unlike rules-based chatbots, those powered by conversational AI generate responses and adapt to user behavior over time. Rules-based chatbots were also limited to what you put in, meaning that if someone phrased the question differently than you wrote it (or used slang/colloquial words, etc.), they wouldn’t understand the question. Conversational AI can also help chatbots understand more complex questions.
Putting technical terms into context.
Companies use a lot of technical terms when it comes to artificial intelligence, so here’s what they mean and how they can be used to improve your business.
Rules-based chatbots. Earlier iterations of the chatbot (and some current low-cost versions) work primarily through pre-defined rules. Your business (or service provider) writes specific guidelines that the chatbot must follow. For example, when a customer says “Hello,” the chatbot replies, “Hello, how may I help you?”
Another example is when a customer asks about a return. A chatbot is programmed to give a specific response, such as “Here’s a link to the return policy.”
However, the problem with rules-based chatbots is that they can be limited. It only knows how to handle situations based on the information programmed into it. So if someone says: “I don’t like this product, what can I do?” and you didn’t schedule that question, the chatbot won’t have an answer.
Machine learning. Machine learning is a way to combat the above problem. Instead of giving a chatbot specific parameters with pre-written questions and answers, machine learning helps chatbots make decisions based on the information provided.
Machine learning helps chatbots adapt over time based on customer conversations. Instead of giving the bot specific ways to answer specific questions, you show it the basic rules and it answers on its own. Plus, because it means your chatbot is always learning, it gets better the longer you use it.
Natural language processing. As humans and English speakers, we know that there are different ways to ask every question. For example, a customer who wants to know when an item is back in stock can ask: “When is X back in stock?” or they might say, “When are you going to pay X back?” or even “When do you stock X?” Those three questions all mean the same thing, and as humans, we naturally understand that. But a rules-based bot needs to be told that they mean the same things, or they might not understand it.
Natural language processing (NLP) uses AI technology to help chatbots understand that these questions are all asking the same thing. It can also determine what information is needed to answer your question, such as color, size, etc.
NLP also helps chatbots answer questions in a more human way. If you want your chatbot to sound more human (and you should), find one that uses NLP.
Web-based SDK. A web SDK (that’s a software development kit for non-developers) is a set of tools and resources that developers use to integrate software (in this case, chatbots) into websites and web-based applications.
What does this mean for your chatbot? Context. When a user says, “I need help with my order,” the chatbot can use NLP to identify “help” and “order.” It can then look back at previous conversations, pull customer order history and more if the data is available.
Contextual conversations are everything in customer service, so this is a big factor in building a successful chatbot with conversational AI. In fact, 70% of customers expect full context with everyone they talk to. With a web-based SDK, your chatbot can do this too.
Advantages of conversational AI:
Using conversational AI chatbots brings benefits to your business, but the clearest wins are in your contact center. Here are three ways chatbots can improve your customer service.
24/7 customer support.
Your customer service agents need to sleep, but your conversational AI chatbot doesn’t. A chatbot can answer questions and contain customer issues while your contact center is down. Any issues they can’t resolve, they can pass on to your agents the next day. Not only does it provide 24/7 service to your customers, but your agents will have less backlog when they get back to work.
Faster response times.
When your agents are inundated with customers, an AI chatbot can pick up the slack. Send your chatbot to immediately greet customers, let them know their wait time, or even start collecting information so your agents can get to the root of the problem faster. AI-powered chatbots can also answer questions and solve simple customer problems, bypassing human agents altogether.
For more information on how AI chatbots can improve your customer service, read this >
More customer service agents present.
Chatbots can handle low-level customer inquiries and give agents the time and space to handle more complex issues. Not only will this lead to better customer service, but agents will be happier and less stressed overall.
Also, chatbots can grow during your busy seasons. You’ll save on costs because you won’t have to hire more agents, and the agents you have won’t be overburdened.
How to get the most out of AI technology.
Unfortunately, you can’t just plug and play with conversational AI and expect to become an AI company. Like any other technology, it takes preparation and deliberate execution plus lots of iteration to get it right.
Use these tips to get the most out of AI technology:
Determine your AI goals.
How do you plan to use conversational AI? Will it be for marketing? Customer service? All of the above? Think about what your main goals are and use that information to choose the right AI partner.
Choose the right conversational AI platform.
Once you’ve decided how you want to use conversational AI, choose the right partner to help you get there. Think about aspects like ease of use, customization, scalability, and budget.
Design your chatbot interactions.
Even with artificial intelligence, you still have to get things done. What you do and how you do it will vary greatly depending on which platform you go with. Design your chatbot conversations with the following in mind:
- The voice of your brand
- Best customer service experience
- Logical conversation flows
- Brief messages
Create a partnership between agents and chatbots.
Don’t run a chatbot independently of your customer service agents. Get them involved in training and onboarding and start building a working relationship between the two. Agents and chatbots can work together on customer issues, both seamlessly and out of the chat. For example, a chatbot can collect information from a customer and relay it to an agent to solve a problem. Then, when the agent is done, they can bring the chatbot back to provide a customer survey.
Test and improve.
Sometimes you don’t know what you don’t know until it happens. Test your chatbot before it’s up and running, but don’t stop there. Keep improving your conversations even after you start.
What does the future hold for conversational AI?
There are a lot of exciting things happening in AI right now, and we’re only just on the cusp of what it can really do.
The big prediction? For now, conversational AI will continue to improve on what it already does. More human interactions, better problem solving and deeper analytics.
In fact, 75% of customers believe AI will become more natural and human over time. Gartner also predicts big things for conversational AI, saying that the deployment of conversational AI in contact centers will reduce agent labor costs by $80 billion by 2026.
Why jump in now when bigger things are coming? It is clear. You’ll learn to master conversational AI tools before your competitors and gain an early competitive advantage.
How Quiq is conversational AI.
To ensure you give your customers the best experience, Quiq powers our entire platform with conversational AI. Here are some outstanding ways Quiq is uniquely improving your customer service with conversational AI.
Design personalized chatbot conversations.
Create chatbot conversations so smooth and intuitive that it feels like you’re talking to a real person. Using the best conversational AI techniques, Quiq’s chatbot provides customers with fast and intelligent responses for a superior customer experience.
Help your agents respond to customers faster.
Make your agents more productive with Quiq Compose. Quiq Compose uses conversational AI to suggest answers to customer questions. How: It uses information from past similar conversations to generate the best response.
Empowerment of agent activity.
Tools like our Adaptive Response Timer (ADT) prioritize conversations based on how quickly or slowly customers respond. The conversational AI platform also uses AI to analyze customer sentiment to give extra attention to customers who need it.
This is just the beginning.
This is just a taste of what conversational AI can do. See how Quiq can apply the latest technology to your contact center to help you deliver exceptional customer service.