chatbot | 5 min read
Guide to Understanding AI Chatbots
Published by Khyati Badiyani on 13-Jun-2021
chatbot | 5 min read
Published by Khyati Badiyani on 13-Jun-2021
How many times have you interacted with a bot and wondered if you’re really speaking to a bot? Aren’t you just a tad bit curious as to how the bot is able to identify everything you ask and give a speedy and accurate response? How is it that despite the automated answers, the bot is still able to personalise every conversation?
Artificial Intelligence isn’t just science-fiction anymore. AI-powered chatbots are doing all of this and more, paving the way for a more sophisticated and human-like conversation with the visitors. AI Chatbots are the chatbots of the next generation. Consumers seek personalised and natural conversations, and the technology of AI chatbot is powerful enough to facilitate that.
Although we use the word chatbot to describe all automated text-based conversations, there are more types to them that encourage these conversations. Chatbots are bifurcated into AI Chatbot and Rule-Based Chatbot, and both are vastly different in terms of their complexities and use-cases. A rule-based chatbot doesn’t deviate from the pre-built conversational flow as the questions and answers are already determined. Rule-based chatbots are built in a flow-chart manner with a decision tree that allows the visitor to choose options.
AI Chatbots take it up a notch higher, facilitating more advanced conversations on a bot. Imagine that you’re teaching someone to speak a new language. You start by training, helping them identify words and phrases, and with time, the person is capable of speaking the said language independently, without your aid. An AI chatbot does just that. It utilises Natural Language Processing (NLP) to understand the intents and respond to those intents. Your AI Chatbot aids a live chat representative’s work, helping them answer complex customer questions and order products and services, among other things, through text-based inputs. To understand how AI Chatbots are different from Rule-Based Chatbots and elements of NLP, check out this article: Difference between Chatbot and Conversational AI.
As I mentioned before, NLP is one of the prime differentiation between rule-based and AI Chatbots. There are many NLP engines in the market, such as Dialogflow by Google, Watson Assistant by IBM, Lex by Amazon, and Luis by Microsoft. The ultimate goal of these engines is to decipher and understand the human language in order to generate an accurate response. The technique on which these engines rely to understand the human language is called Machine Learning. NLP technology works on deriving context from words. It does that by:
Intent Recognition: NLP recognises what the user is trying to get a response for. What exactly is the problem statement that the user wants to solve? For example, a user wants to place an order at a Burger Place. If they ask an AI Chatbot to do so, the bot has recognised the intent that the user wants to place an order.
Utterance: It means identifying the different ways in which a user asks their intent. In our example, there are various ways that the user will ask to place an order, such as:
I want to order
Order Burger Online
Place an order for Burger
I want to place an online order for burgers
How can I order a burger?
Utterances help the engines understand the phrases, but it also means the response to all these utterances will be the same.
Entity: Entity helps identify the intent in detail. In our example, it allows the user to specify the type of burgers and the location of the burger store.
Context: This helps in sharing different parameters through user interaction.
Using this technology and based on their use, NLP engines will pick up on patterns and learn without being programmed to do so over time. As a result, they become more accurate with their responses based on their previous conversations and make AI Chatbots a very advanced mode of communication that can replicate human-like conversations.
To understand AI Chatbots, you need to understand NLP first and how it helps in providing fluid interactions. Natural Language Processing is the branch of NLP that gives the machine the ability to read and comprehend human messages and derive meaning from them. It is because of NLP engines that AI Chatbots can learn from previous conversations and improve over time based on their conversational encounters. Because the bot understands the natural language of humans, it is qualified to provide answers based on what the visitor asks the bot. This technology has transformed customer communication and opened new avenues for customer engagement. An AI chatbot has various use-cases across different industries. Let’s check them out.
Source: Chatbot Magazine
Let’s take an example of an Airline company. Your prospective customers would have numerous queries ranging from ticket booking to flight timings. How can you answer these questions to the thousands of customers visiting your website every day? In this case, you can build an AI Chatbot and train it to answer questions like
What are the flight timings?
How can I book a ticket?
How can I cancel my ticket?
Are there any flights from Destination A to Destination B?
Are there any discounts on the flight tickets?
Once you set up answers to these questions, you can deploy this bot on your home page so that visitors can interact with the bot. This way bot answers all the questions relating to your service offering 24/7, to hundreds at once, and in multiple languages. As a result, you are no longer constrained in terms of resources and time, enabling you to provide seamless customer support at all times.
AI chatbot acts as a virtual assistant on your site, helping your visitor navigate, understand your product or services, and finally generate leads. For example, a real estate agent can deploy an AI Chatbot that showcases the properties the visitor wants to see based on their budget and location and schedule site visits. Furthermore, real estate agents also need to check whether the leads are qualified for the property they’re going to view. To do so, chatbots asks a series of questions relating to the visitors’ credit score, income, credit history, etc. Ultimately, it lists out leads qualified for the agent’s property and dramatically reduces the agent’s time and effort in doing so.
AI chatbots are capable of placing orders and facilitating payments on the bots. For example, Dominos has deployed an AI bot on their site that allows customers to place orders at their convenience without them having to download an app. The bot asks for their Pizza orders, asks them their preference of toppings, pizza sizes, and bread. It further helps cross-sell by asking if the consumers want a soda or dessert along with their Pizzas. This isn’t possible with a regular chatbot since the conversation is very dynamic and individualised to each customer. AI Chatbots can also promote offers, discounts, and online bookings while having a fun and casual conversation with the customer.
Source: Get Jenny
It’s a given that AI chatbots have a complex procedure of building and deployment than the typical rule-based chatbots. But it doesn’t mean you still have to learn to code. Like rule-based chatbots, you can also build AI chatbots on no-code bot platforms like WotNot. All you need is an API key that connects WotNot with the NLP engines. WotNot easily integrates with leading NLP engines in the market- Dialogflow and Watson Assistant, and it provides you with a frontend development platform to seamlessly develop and deploy your bots.
AI Chatbots are used in many industries and filling conversational gaps in customer service, sales and marketing. But every industry will have different areas where AI bots can help the best. Even the size of the company matters in identifying use-cases, and sometimes, a company’s operations may not even call for AI Chatbots when all their tasks can be fulfilled by rule-based chatbots instead. One of the best ways to recognise an AI bot use case is determining if a human judgement would be required in a conversation. If yes, an AI chatbot can be the best alternative. On the other hand, if you’re just deploying the bot for informational purposes, an FAQ or rule-based bot can fulfil all your objectives.
While deploying your bot, you need to have a detailed understanding of your audience and what type of conversations they look forward to having. How is it that your prospect perceives you as a brand? How much do they know about your product or service? What are the primary queries they would have of your product or service? Where are they based? What communication tone do they prefer? Getting an answer to all these questions can help you develop your bot script and make it engaging enough for your audience.
Machine learning is known to improve and adapt over time. But let’s face it, when human-to-human interaction can have miscommunication, human-to-machine interactions can have too, in fact, on a larger scale. No AI Chatbot is perfect, and like humans, they make mistakes too. You need to anticipate them and figure out how you’re going to tackle them. If the visitor cannot get the information he/she needs through the bot, have a seamless transfer for human handover instead of training your bot to do the impossible. In such situations, only speaking to a live agent can keep the consumer experience intact.
In Rule-Based chatbots, you’re sure that a conversation will move in only a limited number of directions with every visitor. It can only solve the issues that are mentioned in the bot. An AI Chatbot removes these limitations by offering a personalised conversation with each visitor since it takes the conversation forward from the contexts it identifies from the user input. AI Chatbots ensure that the conversation is always relevant to what the visitor wants to know and gets right to the point while addressing their queries.
Conversations on bots are not just regular interactions. These chats help businesses know and understand their customer better. AI chatbot can interpret customer sentiment and get valuable insights on the trends and queries that your consumers need help with. This can help you improve your products and customer support based on conversational data.
An engaged customer has a higher chance to become a returning customer. Your AI Chatbot can have a personality, be funny and have a sense of quirkiness to it that can make the conversation engaging. You can address your customers by their names and use emoticons to make the chat more humanly, leading to an exemplified conversational experience.
Chatbots have already significantly reduced the dependency on humans in answering repetitive and standard consumer queries. But AI Chatbots have widened the use-cases of chatbots. With AI Chatbots, even complex customer queries can be solved to an extent. This can further reduce the dependency on hiring full-time human support and lower your costs. AI chatbots can also assist the consumers in their entire buying process, from driving conversion to sales through upselling and cross-selling, thereby driving revenue.
24/7 support is a unique selling point of chatbots overall. But considering the broad use-cases of AI chatbots, your customers can get answers to a wide range of queries 24/7. Your customers don’t have to wait to talk to a live agent and can even get a response at 3 AM on a Saturday. More than that, AI Chatbots continue the website engagement even at odd hours, improving your brand image and customer satisfaction.
AI bots aren’t complex anymore. You don’t have to be a coding expert to build and deploy an AI Chatbot, although that’s what you might think, considering it’s AI. But building an AI bot on a no-code platform like WotNot is definitely possible. You just need to integrate an API Key on the bot builder from NLP engines like Dialogflow or IBM Watson, and your bot is ready to be deployed. To understand in detail how to build and deploy your AI chatbots using Dialogflow and Watson on WotNot, check out the following articles:
AI Chatbots are fulfilling customer expectations, and at the end of the day, that’s what customers care about, right? 40% of customers don’t care whether they’re helped by a human or an AI chatbot as long their queries are resolved. So the ball is in your court. What method are you going to adopt that scale your customer conversations and optimises your resources?