AI Chatbot Buyers Guide:
How to Pick the Right Chatbot for Your Organization
When we have conversations with computers, we want it to mimic human interactions as much as possible. It's no use trying to conform to how computers are scripted to speak – that only leads frustration, and often times, a lot of it.
Instead, we need the computers to speak human and communicate the way we do.
Conversational AI is the technology that makes that possible. It allows artificial intelligence (AI) technologies like chatbots to interact with people in a humanlike way. By bridging the gap between human and computer language, it makes communication between the two easy and natural.
But conversational AI isn't just one thing. It's a set of technologies that allow computers to recognize human language and decipher different languages, comprehend what is being said, determine the right response, and respond in a way that mimics human conversation. Here's how.
How does a chatbot get from an asked question to an appropriate response? Let's look at the technologies that makes conversational AI possible.
Conversational AI comprehends and engages in contextual dialogue using natural language processing (NLP) and additional AI algorithms.
First, the AI must understand what the customer is trying to say, or the intent of the customer's question. Natural language understanding (NLU) works to decipher meaning in the user's words, regardless of how it's stated. With sophisticated NLU, the AI will be able to understand the user's intent even among grammatical mistakes, shortcuts, and idiosyncrasies, and remember context from one statement to the next, comprehending what is being said throughout the conversation. This capability is much different from recognizing a keyword or phrase and answering with a canned response that was scripted for that keyword. (We'll talk more about a scripted chatbot vs conversational AI in a bit.)
Next, the AI must determine the right response based on its understanding of the user's intent using machine learning. As the AI answers user questions over time, and as human agents help to guide its knowledge, it learns more variations of the same intent and which responses are the most appropriate for each intent.
Finally, using natural language generation, the AI generates a response in a format that is easily understood by the user.
Conversational AI that uses natural language processing techniques can respond to a customer's inquiry (reactive engagement) and anticipate something the customer hasn't even asked yet (proactive engagement). A robust customer engagement strategy uses both AI approaches to put natural language processing in action.
Using reactive engagement, businesses give customers a clear and easy path to find answers and information without having to reach out to a human customer experience representative. Whether it be chatbots ready to help or dynamic search bars that simplifies digging through FAQs, answers to common questions are always available to customers when they need it most.
Using proactive engagement, businesses can reach out to customers in an effort to keep them moving along the customer journey. Companies couldn't possibly staff enough agents to reach out to every site visitor. With a proactive approach to conversational AI, no matter what channel customers are using, time of day they're searching, or native language they speak, businesses can engage consumers with personalized, contextual information at scale, creating opportunities to establish new relationships, convert more sales, and prevent customer churn. This allows businesses to intervene at critical moments, like when a visitor toggles back and forth between two product options, or hesitates at checkout, and enables companies to quickly support their customers outside of normal business hours.
When looking at natural language processing examples, consider what's happening on the back end, or how the conversational AI is learning to have these reactive and proactive conversations. Opaque AI (sometimes called "Black Box AI") is associated with deep learning. Potential outcomes are not given to the computer ahead of time. Instead, the admin user inputs some unstructured data and the computer reaches new conclusions on its own using deep learning algorithms.
The other approach is transparent AI (also known as "White Box AI"). This uses structured data and pre-set algorithms, so the outputs will match a pre-defined set of results. In other words, all possible outcomes are known ahead of time because it relies on human programmers to map inputs to the right output. Many businesses prefer this transparent AI because it helps them stay in control of what the bot is able to say to protect their brand image.
Conversational AI can be manifested in many ways, but the use of chatbots has exploded because people like to engage with AI in a humanlike way (think Google Home, Alexa, and other virtual assistants for your home). That's why conversational AI and chatbots go hand in hand. We like to interact with AI rather than fill out forms or search for answers on our own.
But not all chatbots are created equal. There's a big difference between a traditional scripted chatbot vs conversational AI.
Traditional scripted chatbots may claim to have conversational capabilities, but humans will have to write scripts and dialogues behind the scenes. The chatbot must be told what to say in response to exact keywords and train the bot for every foreseeable scenario. When (and only when) the chatbot recognizes words or phrases in a question, they respond with pre-written answers for that question. If the user's input doesn't match the keyword phrases the chatbot is programmed to recognize, the chatbot won't be able to deliver one of its canned responses. That can get really frustrating really fast. And it puts a lot of work on the owner of the chatbot. Without conversational AI, you will always need to write new "conversations" to ensure you're meeting user experience expectations.
A real AI chatbot conversation requires conversational AI. Think of conversational capabilities as the glue that holds individual utterances together. In conversation, humans remember what they're talking about from one response to the next. Likewise, a conversational AI chatbot can retain context throughout an entire conversation, because conversational capability is built into the software – no one wrote a script to make it look like the bot can have conversation. When you have a truly conversational chatbot, it has the innate capability to engage in dialogue about any topic – you just give it the data to build the conversation.
Creating humanlike conversations that change dynamically, instead of forcing customers down a rigid path, can be pretty complex. Luckily there are solutions that make conversational writing or conversational design much easier.
A modern tool will convert data source files into a simple, intuitive display that non-technical business users can work with. The repository of knowledge the chatbot pulls from is easy to keep up to date as products, services, or policies change. And it shows you in real-time how the conversation will look to the end customer so there are no surprises.
Here are a few things you can do with a modern conversational design tool:
You can also decide how to best present information to customers using various formats, including rich text, quick buttons, videos, and image carousels. This flexibility allows you to choose the best way to deliver the information you are trying to convey based on the use case.
Many companies are incorporating conversational AI into their customer engagement strategies because of the business benefits. Today, conversational marketing and conversational commerce aren't just buzzwords, they're proven business strategies.
Conversational AI can be used across the customer journey in engagement channels like live chat, messaging apps (like SMS/text, WhatsApp, WeChat, and other popular apps), social media interfaces (like Facebook Messenger and Twitter), and email. Using conversational AI chatbots in an omni-channel approach allows you to engage with customers on the channels they prefer for immediate responses and proactive engagement.
The benefits can be undeniable:
Discover smarter, more personalized engagement.