How do chatbots work? Often with a little help from AI
Not so long ago, chatbots were a curious, fringe customer support strategy, but with advances in artificial intelligence, machine learning, and natural language processing, they've become sophisticated and even mainstream. One report suggests that the chatbot market will grow at a CAGR of nearly 35 percent from 2021 to 2026 to reach $102B — up from $17B in 2020 for a staggering 500 percent increase over six years, with the Asia Pacific region set to see the highest growth. Meanwhile, consumer retail spend via chatbots is anticipated to skyrocket to $142B by 2024, up from $2.8B in 2019 — that's a 4,328 percent increase in five years. (Yes, you read that right.)
Retail isn't the only sector embracing AI chatbots: Entire industries are seeing the light, including healthcare, like in France, where they have been shown to reduce vaccine hesitancy, and with Woebot, a digital mental health startup that recently received its Series B funding and is taking a stab at cognitive behavioral therapy support.
For many companies, ignoring the rise of AI is risky business. Understanding chatbots — just how they work and why they're so powerful — is a great way to get your feet wet. If you're overwhelmed by AI in general, think of chatbots as a low-risk gateway to new possibilities.
Chatbots: AI's secret weapon for increasing engagement and revenue
AI chatbots provide immediate responses so users feel "listened to," they can dramatically reduce wait times, and they capitalize upon consumers' preference for online chatting over calling 1-800 numbers and waiting. They also deliver higher conversion rates, often among customers who have abandoned their shopping carts — a major pain point in online retail. A perky chatbot, for example, can say, "Hey, you've got items sitting in your shopping cart! Buy them before they’re gone!"
On top of that, AI chatbots help ecommerce companies make product recommendations based on a user’s browsing activity, previous purchases, and/or demographic data, and they allow businesses to provide 24/7 customer support inexpensively. After all, chatbots don’t need breaks, vacations, or holidays, and they can also take over mundane tasks ("You asked where your package is. I found it!"), leaving the more interesting customer service requests for live agents, who are then more likely to be engaged themselves.
Moreover, a good chatbot, supplemented by real people, can help any business deal with surges in demand (think holiday shopping) or sudden losses in the availability of customer service reps, like when call center staff were ill during the pandemic.
Ultimately, chatbots can be a win-win for businesses and consumers because they dramatically reduce customer service downtime and can be key to your business continuity strategy.
AI chatbots: How they work
A chatbot performs routine automated tasks based on specific triggers and algorithms, simulating human conversation. A bot is designed to interact with a human via a chat interface or voice messaging in a web or mobile application, the same way a user would communicate with another person. Like virtual assistants, chatbots are a form of conversational AI.
The simplest type of chatbot is a question-answer bot — a rules-based bot that follows a tree-like flow to arrive at answers. These chatbots use a knowledge base and pattern matching to give predefined answers to specific sets of questions — and they're not, strictly speaking, AI.
But when artificial intelligence programming is added to the chat software, the bot becomes more sophisticated and human-like. AI-powered chatbots use a database of information and pattern matching together with deep learning, machine learning, and natural language processing (NLP).
A typical chat bot program looks at previous conversations and documentation from customer support reps in a knowledge base to find similar text groupings corresponding to the original inquiry. It then presents the most appropriate answer according to specific AI chatbot algorithms.
Apple's Siri, Amazon's Alexa, and Google Assistant are examples of generative algorithm-based chatbots trained using a multi-step method. These bots generate advanced responses based on previous conversations and algorithms that allow them to use unique patterns to reply to queries.
AI chatbot algorithms: machine learning, deep learning, and natural language processing
Popular chatbot algorithms include the following:
- Pattern matching
- Naïve Bayes
- Sequence to Sequence (seq2seq) model
- Recurrent neural networks (RNN)
- Long Short Term Memory (LSTM)
- Natural Language Processing (NLP)
Since this post is focused on AI chatbot algorithms, we'll focus on the features of machine learning, deep learning, and NLP as techniques most widely used for building AI-based chatbots.
The term "machine learning" applies to how a computer can receive, analyze, and interpret data to identify certain patterns, and then make logical decisions without input from a human operator.
An intelligent, machine learning chatbot recognizes repetitive patterns during conversations with humans, combined with pre-determined chat scripts and a database of answers used for responses. The more the program operates, the more the chatbot "learns" from the database — and the more sophisticated it becomes. For example, a machine learning chatbot can help customers find products and services on a website, answer queries in real-time, or send updates and targeted notifications.
Deep learning chatbots are created using machine learning algorithms but require less human intervention and can imitate human-like conversations. By creating multiple layers of algorithms, known as artificial neural networks, deep learning chatbots make intelligent decisions using structured data based on human-to-human dialogue.
An essential element of the AI chatbot algorithm is the NLP layer, which allows computer programs to translate and mimic human conversation through predictive analytics and sentiment analysis along with text classifications.
Predictive analytics combines big data, modeling, artificial intelligence, and machine learning in order to make more precise predictions about future events. Sentiment analysis explores the context of a situation to make a subjective determination. In the context of chatbot technology, sentiment analysis can determine what a user "really means" when they type in a certain phrase or perhaps make a common spelling or grammatical mistake.
By breaking down a query into entities and intents, a chatbot identifies specific keywords and actions it needs to take to respond to a user's input. For example, queries like "I want to order a bag." and "Do you sell bags? I want to buy one." will be understood by a chatbot algorithm in the same way so that a user will see bag options offered on a website.
Text classifications allow NLP to understand human language (e.g., phrasing, intent, and colloquialisms) and respond consistently through chat, text, or voice message. Multinational Naïve Bayes is a classic example of text classification and NLP: It matches terms from the input sentences and assigns a score to each classification to determine the highest scored class associated with the input. For example, if a bot’s training set contains "How are you doing?" and "Good morning" sentences in the "Greetings" class, the query "Hello, good morning" will get two matches: "Good" and "morning" in the classification with a score=2.
These technologies all work behind the scenes in a chatbot so a messaging conversation feels natural, to the point where the user won’t feel like they're talking to a machine, even though they are.
What's the best programming language for an AI chatbot?
There's no single best programming language for chatbots, but there are technical circumstances that make one a better fit than another. It also depends on what tools your developers are most comfortable working with.
Python. Python is currently the most popular language for creating an AI chatbot, and it’s the best choice for natural language processing, as the initial Natural Language Toolkit was written in Python.
Java. Since AI programming is based on the use of algorithms, Java is also a good choice for chatbot development. Java features a standard Widget toolkit that makes it faster and easier to build and test bot applications.
Ruby. Due to a wide variety of reliable libraries, Ruby is considered a good choice for building a chatbot. This programming language has a dynamic type system and supports automatic memory management, making it an efficient tool for chatbots design.
Lisp, Clojure, and CSML can also be used to build chatbots. Lisp has been initially created as a language for AI projects and has evolved to become more efficient. It is a dynamic and highly adaptive language that helps to solve specific problems in chatbot building. Clojure is a Lisp dialect that allows users to create chatbots with clean code, processing multiple requests at once, and easy-to-test functionality. CSML is a domain-specific language originally designed for chatbot development. This Rust-based open-source language is easy-to-use and highly accessible on any channel, allowing to build scalable chatbots that can be integrated with other apps.
Chatbots are the new "contact us" links for business pages, to the point where many customers prefer to communicate with companies via chatbot; instant gratification, after all, is the shared currency and motivator of the digital world, and users expect faster and more personalized shopping and browsing experiences on the web.
If you're not already heading down the chatbot path, it's time to roll up your sleeves. We're true believers, too: If you're ready to get the conversation going, head on over to our home page and click on the icon in the lower right. Our friendly, knowledgeable chatbot will get right back to you.