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Since 2016, when Facebook opened Messenger for bots, brands have widely adopted them. Everybody is now talking about them and their value to businesses. But what exactly are chatbots, and why have they become so important?
In this chatbot guide, you'll find answers to these questions and learn what makes chatbots significant.
☞ What is a chatbot?
☞ How do chatbots work?
☞ The brief history of chatbots
☞ The difference between chatbots and bots
☞ How to create a chatbot?
☞ Why do businesses need chatbots? ☞ Chatbot use cases
What is a chatbot?
A chatbot is software that simulates human-like conversations with users via text messages on chat.
Its key task is to help users by providing answers to their questions. Bots can chat with multiple users simultaneously and provide information within seconds.
Because of that, they are now used on a wide scale to help businesses and consumers communicate with each other on websites and mobile messaging apps.
How do chatbots work?
Chatbots are powered by pre-programmed responses, artificial intelligence, or both. Based on the applied mechanism, they process a user's question to deliver a matching answer. There are two main types of chatbots, and those types also tell us how they communicate — rule-based and AI.
Rule-based (also command-based, keyword, or transactional) chatbots communicate using predefined answers.
They can be playfully compared to movie actors because, just like them, they always stick to the script. Rule-based bots provide answers based on a set of if/then rules that can vary in complexity. These rules are defined and implemented by a chatbot designer.
At this point, it's worth adding that rule-based chatbots don't understand the context of the conversation. They provide matching answers only when users use a keyword or a command they were programmed to answer.
When a rule-based bot is asked a question like, “How can I reset my password?” it first looks for familiar keywords in the sentence. In this example, ‘reset,' and ‘password' are the keywords. Then, it matches these keywords with responses available in its database to provide the answer.
However, if anything outside the AI agent's scope is presented, like a different spelling or dialect, it might fail to match that question with an answer. Because of this, rule-based bots often ask a user to rephrase their question. Bots can also transfer a customers to a human agent when needed.
It's worth underlining that rule-based conversational interfaces can't learn from past experiences. They respond based on what they know at that moment. The only way to improve a rule-based bot is to equip it with more predefined answers and improve its rule-based mechanisms.
On the other hand, the limitations of rule-based AI agents make them a very useful tool for businesses. Rule-based bots are the cheapest to build and easiest to train. Companies introduce them into their business strategies because they help to automate customer communication. The behavior of rule-based chatbots can also be designed from A to Z. This allows companies to deliver a predictable brand experience.
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An AI chatbot is a piece of software that can freely communicate with users. AI communication application are much better conversationalists than their rule-based counterparts because they leverage machine learning, natural language processing (NLP), and sentiment analysis.
- Machine Learning (ML) allows bots to identify patterns in user input, make decisions, and learn from past conversations.
- Natural Language Processing (NLP) helps bots understand how humans communicate and enable them to replicate that behavior. NLP lets them understand the context of the conversation even if a person makes a spelling mistake or uses jargon.
- The sentiment analysis helps a chatbot understand users' emotions.
AI communication bots need to be well-trained and equipped with predefined responses to get started. However, as they learn from past conversations, they don't need to be updated manually later.
AI bots can understand multiple languages and read the customer's mood. This enables them to personalize their communication with the user.
AI bots get smarter with every conversation, meaning they simply mirror users' behavior. This has already turned out to be a major challenge for conversation designers and was well exemplified in the Microsoft experiment called “Conversational Understanding.”
The experiment involved launching Tay, an AI bot, on Twitter. Tay was supposed to chat with millennials and prove a computer program can get smarter with "casual and playful conversations."
The experiment showed that Microsoft's assumptions were right; however, the experiment's results were far from expected. After chatting with Twitter users for just a couple of hours, Tay started to send racist, and offensive tweets, including messages like “Hitler was right” or “9/11 was an inside job.”
In response to that situation, in less than 24 hours, Microsoft took Tay down from Twitter. It issued an apology for the incident, which stated, “We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Although we had prepared for many types of system abuses, we had made a critical oversight for this specific attack.”
Microsoft's experiment showed that there is still room for improvement in AI. Tay wasn't trained enough, which resulted in it “blindly” mimicking the language and behavior of Twitter users. These were intentionally teaching Tay inflammatory messages.
After the experiment, Roman Yampolskiy, the head of the CyberSecurity lab at the University of Louisville, said that Microsoft's experiment proved that chatbots are like children. They need to be taught what is appropriate and what is not.
The brief history of chatbots
To the surprise of many, conversational interfaces aren't a modern invention. They were born out of curiosity and creative thinking more than half a century ago.
1950 Alan Turing — the man that started it all
In 1950 Alan Turing, a computer pioneer, wrote a scientific paper titled “Computing Machinery and Intelligence.” In the paper, the scientist implied that a computer program can think and talk like a human. Turing proposed an experiment called the Imitation Game, which is known as the Turing Test, to prove the point. In the Turing experiment, the person designated as a judge was chatting over a computer with a human and a machine who could not be seen.
1966 ELIZA — the first chatbot
In 1966, an MIT professor, Joseph Weizenbaum, developed a computer program called Eliza. It's considered to be the first chat robot in history. Eliza was a simple keyword-based conversational interaface that mimicked a human psychiatrist. The program communicated by matching user questions with scripted responses entered into its database. When a patient would say, “My mother loves flowers,” Eliza would reply, “Tell me more about your mother.”
In 1971, Kenneth Colby, a Stanford Artificial Intelligence Laboratory psychiatrist, wondered whether computers could contribute to understanding brain function. He believed that the computer could help in treating patients with mental diseases.
These thoughts led Colby to develop Parry, a computer program that simulated a person with schizophrenia. Colby believed that Parry could help educate medical students before they started treating patients. Parry was considered the first chat robot to pass the Turing Test. Back then, its creation initiated a serious debate about the possibilities of artificial intelligence.
In 1988, a self-taught programmer called Rollo Carpenter created Jabberwacky. It was a program designed to simulate human conversation entertainingly. Jabberwacky learned from past experiences and developed over time. It reflected users' personalities and behaviors.
1992 Dr. Sbaitso
In 1992, Creative Labs, a technology company based in Singapore, developed Dr. Sbaitso. It was an AI speech synthesis program that imitated a psychologist. The program was distributed with sound cards sold by the company. They wanted to show the digitized voices their cards were able to produce.
Developed in 1995 by Richard Wallace, Alice was an NLP application that simulated a chat with a woman. Wallace Alice was inspired by Eliza and designed to have a natural conversation with users. Its code was released as open-source, which means it can be reused by other developers to power their conversational interfaces.
SmarterChild was an intelligent chat interface built on AOL Instant Messenger in 2001 by ActiveBuddy, the brand creating conversational interfaces. SmarterChild was designed to have a natural conversation with users. It's considered to be a precursor to Apple's Siri.
2010 Virtual assistants
Since 2010, when Apple launched Siri, virtual assistants have been on the rise. Siri was the first personal assistant available worldwide. Google followed in Apple's footsteps by releasing Google Now in 2012. Microsoft's Cortana and Amazon's Alexa were both released in 2014.
2016 Chatbot platforms
In 2016, Facebook opened its Messenger platform for chatbots. This helped fuel the development of automated communication platforms. In 2018, LiveChat released ChatBot, a framework that lets users build chatbots without coding. So far, there have been over 300,000 active bots on Messenger.
What's the difference between chatbots and bots?
Although the terms chatbot and bot are used interchangeably, there's a significant difference between them.
A chatbot is a computer program designed to communicate with users. It analyzes users' questions to provide matching answers. Businesses use chatbots to support customers and help them accomplish simple tasks without the help of a human agent.
A bot is an algorithm that interacts with web content. Bots help businesses and users perform helpful, mundane, or complex tasks faster online. Below are some different types of bots.:
- Search engine bots called crawlers are used by Google and Yahoo to index the web content and scale web cataloging. This helps users easily find information related to their search intent.
- Feed bots look for new information on the web to add to news sites.
- Copyright bots look for content that violates copyright laws. They help companies and authors check whether their proprietary content has been used without approval.
Unfortunately, businesses have learned to also use bots for malicious activities.
For instance, companies launch click bots that deliberately generate fake clicks. They hurt advertisers paying for those clicks and create quite a headache for marketers who get unreliable data. Bad bots can also break into user accounts, steal data, create fake accounts and news, and perform many other fraudulent activities.
How to create a chatbot?
Chat bots can be created from scratch or by using a chatbot platform. Both ways have their pros and cons.
Building a chatbot with a platform
Using a platform is the easiest way to create a conversational interface. Platforms have a low learning curve. They let you drag and drop predefined elements to design chatbots and launch them without coding.
To facilitate the building process, some platforms provide ready-to-use templates. You can use them as they are or customize them to your liking. Because of that, chatbot platforms are a good choice for brands that lack technical expertise but don't want to spend money on hiring external developers.
Platforms also come in handy if you want to test, at low cost, whether your business could benefit from using a chatbot. Some companies only use chatbot platforms from time to time, for instance, during the shopping season. They use a chatbot to help busy support teams or promote their new products.
Another advantage of platforms is integrating them with third-party services. With integrations, brands can add a smart agent to multiple communication channels and unify their customer experience.
On the other hand, platforms might limit your bot's capabilities. Unless you decide to build custom features or integrations, you can only operate within the platform's scope.
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Hand coding your chatbot from scratch
Building your chatbot from the ground up is a time-consuming job, but it gives you total control over your chatbot. You can customize your AI agent to serve the particular needs of your customers, power it to solve complex problems, and integrate it with any platform you wish.
Before you code your bot, consider whether it's worth doing. To breathe life into your bot in-house, you need to engage a team of developers or hire external bot-building services. That comes at a price. Also, consider that the testing phase may take a lot of time.
The same can be said for updating your custom-made chatbot or correcting its mistakes. It's a long game to play. If you're unsure whether using an AI agent would benefit your business, test an already available platform first. This will let you find out what functionalities are useful for you. You'll be able to determine whether you need to build it from scratch or not.
Why do businesses need chatbots?
Technological progress has radically changed the way people communicate. Face-to-face interactions have been largely replaced by online messaging. This has forced businesses to adapt to a new type of communication. To achieve success, brands need to provide a seamless buyer's journey. They must respond to customer questions around the clock and across multiple channels.
But living up to the rising expectations of “always-connected” customers is not the easiest and cheapest task. The more your business grows, the more it costs to deliver 24/7 customer service. This is where chatbots come in handy. They allow brands to scale up their support services at a low cost.
More and more often, companies are deciding to introduce bot applications into their marketing strategies because they allow for delivering personalized and consistent brand experiences. Long term, that translates into better brand perception and more sales.
Chatbot use cases
Brands use conversational agents to diversify their customer-engagement strategy. With them, businesses engage website visitors proactively and, eventually, sell more products.
It's for good reason that more and more companies are hiring conversation designers who know how to write engaging chatbot scenarios. Businesses have already realized that a well-written chatbot can work as a successful lead generation tool. It can collect newsletter subscribers, sales contacts, beta testers, or even job candidates by helping companies reach a larger audience with their message.
One of the brands that took their online service to the next level using a bot is Sephora. The company uses it to educate customers about their cosmetics.
Their AI assistant offers makeup tutorials and skincare tips and helps customers purchase products online. The company even enables its customers to try new makeup using AR technology implemented in their chatbot. By doing this, Sephora has delivered its personalized customer experience in-store and online.
Customers want their problems handled immediately and via the channels they prefer. Chatbots make that possible by redefining the customer service people have known for years.
They support customers 24/7 and enable them to solve simple problems, book appointments, or submit complaints. Take, for instance, Mastercard. The brand offers a Messenger bot to help customers easily check their account transactions anytime.
Restaurants like Next Door Burger Bar use conversational agents to help customers order their meals online. Customer service bots allow companies to scale their services at low cost but, more than that, meet changing customer expectations.
Brands automate their customer communication to boost the productivity of their support teams. Smart agents can function as the first line of customer support by taking over the vast majority of repetitive cases from live agents. They can group customers based on their issue type and, when needed, route them to agents.
Before purchasing a product, every customer must go through the sales funnel. Chatbots can take customers by hand and walk them through all the stages of that process: awareness, interest, decision, and action. A Gartner report shows that businesses that utilize conversational interfaces in their sales strategy can achieve up to 30 percent higher conversion rates.
By integrating into social media platforms, conversational interfaces let brands connect with many users and increase their brand awareness. Take, for example, National Geographic. The company has used a Messenger bot to carry out a daily quiz with users.
By doing this, the brand attracted users' attention to their new ebook, Almanac. The brand's bot also encouraged users to purchase the title by offering a 10% discount, which boosted its sales.
Harper Collins, the world-leading book publisher, uses the Epic Reads chatbot to help their community members find another book to read.
Their AI agent conducts a short survey with every user to find out what might interest them and recommends titles matching their preferences. By supporting prospects, the company helps book lovers make decisions and builds positive relationships with them.
Another global giant, Starbucks, uses an AI agent to help customers compose their favorite coffee drink. It enables customers to order a drink on the go and pick it up at a chosen cafe. It translates into a better brand experience because customers don't have to stand in a long line.
Chatbots can be of great use for sales teams as well. They help businesses eliminate unqualified leads and connect sales reps with qualified ones. This helps sales specialists spend less time acquiring leads and more on building relationships with prospects.
On top of that, AI assistants are a great repository of knowledge about customers. The more the bot chats with your prospects, the more data it gains about their needs and preferences. This helps companies better tailor their offers and messages.
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Chatbots are here to stay. They are already in our computers, phones, and smart home devices and have become an integral part of our life.
Although the chatbot technology is not perfect yet, it helps businesses and users quickly handle many repetitive and dull tasks. That is probably the essence of conversational interfaces. Their aim is not, and never should be, to replace a human. Through human-like conversation, they are here to help us in a way that is the most natural for us.
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