Everything you wanted to know about chatbots: Part1

The world of technology has already gone far beyond our imagination, turning the scenes of sci-fi movies into part of our everyday lives. A chatbot is another technology that streamlines conversation and facilitates customer support services with a single click. The idea of talking to a bot no longer feels odd or scary.

Chatbot technology, already widely used in different industries, has proved to be a productive marketing tactic working more efficiently than traditional ones. Incorporating chatbots into digital marketing may significantly transform the sales funnel. This is just the first part of a comprehensive survey aimed to reveal the role of chatbots in digital marketing, key market trends, and the ways to benefit from the technology. 

What are Chatbots?

A chatbot is a software to automate conversations with a user and generate relevant responses. The simplest type of chatbot is programmed with different responses or choices. Powered by artificial intelligence, new-gen chatbots are more humanized and intelligent. Users feel more comfortable and confident when contacting a bot. At the same time, the chatbot unloads the customer support team, directing only those queries that need human interaction and collecting valuable information for marketing efforts. 

Chatbot history; Timeline of Chatbot Development

The Evolution of Conversational Software

Though we are talking about chatbots as a relatively new technology, the history of the first chatbot goes back to the 1960s and the first developed chatbot, ELIZA by MIT professor Joseph Weizenbaum. A conversational proxy imitated psychiatrists and generated answers through natural language processing methods. The intention of a bot, i.e., copying human conversation, had a totally different response; users started trusting their most profound thoughts to the bot. 

• In 1988, Rollo Carpenter created a Jabberwacky chatbot that simulated a natural human conversation that used “contextual pattern matching.” 
• In 1992, Creative Labs developed Dr. Sbaitso for MS-Dos, and this was the first attempt at using AI in a chatbot.
• In 1995, Richard Wallace created A.L.I.C.E, the universal language processing chatbot on XML schema artificial intelligence markup language (AIML).
• Decades later, in 2001, ActiveBuddy Inc. developed a colloquial agent, SmartChild, operating on MSN and AOL Messenger. The chatbot provided instant weather forecasts, sports results, and news. 
• In 2006, IBM developed a conversational agent for the American TV show Jeopardy. The bot had limited answers to one-liner questions, and it could not engage in conversation with a human.

The Future of Chatbots

AI-powered chatbots

As a critical resource for improving the customer experience, chatbots are evolving at a rapid speed and are changing the global market. The major trend in the coming years is the implementation of AI in customer service and chatbots.  

In the future:

• Chatbots will be more intelligent and more human-like: based on NLP (natural language processing), chatbots will use predictive analytics to understand and proceed with customer queries, thus cutting down on misunderstanding requests and executing inaccurate commands.
• Artificial intelligence and machine learning will be at the core of chatbots: the AI chatbot is predicted to save on customer support significantly.
• Voice bots will become mainstream: with the popularity of voice assistants, customers will feel comfortable interacting with voice bots. 
• The improved customer experience (CX) driven by chatbots: conversational commerce is preferable as a means to increase customer engagement։
• Chatbots will automate payments: more customers already prefer purchasing by using a chatbot։
• Bots will be used for internal purposes within enterprises: by supporting constant communication, chatbots will deliver business value by automating internal workflows։
• Chatbots as an integral part of daily activities:  just like notes or reminders, we may soon use chatbots for planning and scheduling personal tasks. 

According to Gartner, by 2027, chatbots will become the primary customer service channel. More and more companies are evaluating chatbots as a means of automating business processes and reaching out to customers. Thus, from 2020 to 2027, the global chatbot market will grow at a CAGR of 28.7%, and the market will be valued at $19.570 million by the end of the period. The pandemic was a colossal trigger, affecting market growth positively, and the impact still continues to change the market trends. 

Key statistics

• The value of chatbot eCommerce transactions will be valued at $112 billion by 2023 (source: Juniper Research).
• By 2024, over 90% of customer queries will be handled by bots (source: CNBC).
• Chatbots can save over 50% on customer service costs (source: Inverspcro.com).
• 87,2% of customers have a positive experience with chatbots (source: Drift).
• Chatbots generate over 40% response rates, with predictions to increase this number (source: Matthew Barby)

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Common types of Chatbots

Modern Types of chatbots

To ensure communication and interaction between customers and the brand, the chatbot should respond to the requirements of both the business and the customers. Depending on the business type and chatbot purpose, there are six categories of chatbots to consider. 

Menu/Button-based chatbot

The most basic and, so far, the most frequently used type of chatbot is a menu or button-based chatbot. This type makes up over 80% of support queries that are automatically and quickly processed while generating high user interaction and satisfaction. Built on decision tree hierarchies, the chatbot offers options in the form of buttons, leading to the ultimate answer. Still, this type of chatbot may fall short in advanced scenarios, and the user will need human support to solve the issue. 

Keyword recognition-based chatbot

Based on Natural Language Processing (NLP), this chatbot listens to customers and generates answers based on detected keywords. They are similar to menu-based chatbots, but instead of button options, the chatbot detects the options from the user’s answers. These are good options for short interactions but may fall short when answering similar questions in a raw. The keyword redundancies in related questions may confuse the bot and generate wrong answers. 

Intelligent AI chatbot

The newest generation of chatbots powered by AI and machine learning are more advanced than all the previous chatbots put together. Every subsequent conversation will be based on the previous experience and get smarter. The more a customer interacts with a chatbot, the more information AI will store and process for more precise answers. Unlike the previous types described, this type of chatbot is designed to handle free-flowing conversations where customers will have the same experience as with a real person. 

Hybrid model

Though we recognize the AI chatbot as the best of its kind, there are times when smart chatbots can be useless. If the business model doesn’t have big data to support AI and machine learning, the hybrid model comes to the rescue. It's a combination of rule-based bots and AI chatbots.

Voice assistants

The previous models can deliver better productivity and higher engagement by adding one more feature, TTS (text-to-speech), and allowing streamlined and more realistic conversations. TTS with the integration of voice recognition APIs enables other customer support and customer experience tools and technologies for voice support, such as post-call satisfaction surveys, outbound calls with smart automation, voice assistants, etc.

Chatbot Use Cases

What is a chatbot used for?

Aside from the chatbot software type, we should also choose the chatbot purpose to better deliver information to the customer and create interaction. 

Support Chatbot

Support chatbots solve customers' specific problems and help you walk through the business by answering FAQs. They are the best at increasing customer satisfaction rates and providing immediate support without delays. 

Skills Chatbot

Based on NLP features, chatbots help users perform an action, such as following the order. A famous example of a skilled chatbot is Alexa. 

Assistant Bot

Assistants are more entertaining and communicable than support chatbots. A great example of an assistant bot is Siri, which helps users give precise, informative, and funny answers.

Transactional Chatbot

Interacting with external systems, transactional bots can help users navigate and lead them to final decision-making.

Information Gathering and Informational Bot

These two bots collect data to send push notifications or suggest the latest news generated by AI and text classification technologies. 

Do you really need a chatbot? Wrapping Up

There is still so much to say about chatbots; their benefits and effectiveness, the ways to integrate them into your business, and how to automate part of the workload. Keep up with the blog to learn what chatbot marketing is and how it can help your marketing efforts work with higher efficiency.