At the most basic level, a chatbot is a computer program that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if they were communicating with a real person. Chatbots can be as simple as rudimentary programs that answer a simple query with a single-line response, or as sophisticated as digital assistants that learn and evolve to deliver increasing levels of personalization as they gather and process information.
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You’ve probably interacted with a chatbot whether you know it or not. For example, you’re at your computer researching a product, and a window pops up on your screen asking if you need help. Or perhaps you’re on your way to a concert and you use your smartphone to request a ride via chat. Or you might have used voice commands to order a coffee from your neighborhood café and received a response telling you when your order will be ready and what it will cost. These are all examples of scenarios in which you could be encountering a chatbot.
How do chatbots work?
Driven by AI, automated rules, natural language processing (NLP), and machine learning (ML), chatbots process data to deliver responses to requests of all kinds. For more information please visit Pritish Kumar Halder ‘s page.
Two main types
Task-oriented (declarative) chatbots are single-purpose programs that focus on performing one function. Using rules, NLP, and very little ML, they generate automated but conversational responses to user inquiries. Interactions with these chatbots are highly specific and structured and are most applicable to support and service functions—think robust interactive FAQs.
Task-oriented chatbots can handle common questions, such as queries about hours of business or simple transactions that don’t involve a variety of variables. Though they do use NLP so end users can conversationally experience them, their capabilities are fairly basic. These are currently the most commonly used chatbots.
Data-driven and predictive (conversational) chatbots are often referred to as virtual assistants or digital assistants, and they are much more sophisticated, interactive, and personalized than task-oriented chatbots. These chatbots are contextually aware and leverage natural-language understanding (NLU), NLP, and ML to learn as they go.
They apply predictive intelligence and analytics to enable personalization based on user profiles and past user behavior. Digital assistants can learn a user’s preferences over time, provide recommendations, and even anticipate needs. In addition to monitoring data and intent, they can initiate conversations. Apple’s Siri and Amazon’s Alexa are examples of consumer-oriented, data-driven, predictive chatbots.
Advanced digital assistants are also able to connect several single-purpose chatbots under one umbrella, pull disparate information from each of them, and then combine this information to perform a task while still maintaining context—so the chatbot doesn’t become “confused.”
The value chatbots bring to businesses and customers
Chatbots boost operational efficiency and bring cost savings to businesses while offering convenience and added services to internal employees and external customers. They allow companies to easily resolve many types of customer queries and issues while reducing the need for human interaction.
With chatbots, a business can scale, personalize, and be proactive all at the same time—which is an important differentiator. For example, when relying solely on human power, a business can serve a limited number of people at one time. To be cost-effective, human-powered businesses are forced to focus on standardized models and are limited in their proactive and personalized outreach capabilities.
Engaging multiple customers
By contrast, chatbots allow businesses to engage with an unlimited number of customers in a personal way and can be scaled up or down according to demand and business needs. By using chatbots, a business can provide humanlike, personalized, proactive service to millions of people at the same time.
Consumer research is showing that messaging apps are increasingly becoming the preferred method for connecting with businesses for certain types of transactions. Delivered through messaging platforms, chatbots enable a level of service and convenience that in many cases exceeds what humans can provide.
For example, banking chatbots save an average of four minutes per inquiry compared to traditional call centers. The same capabilities that help businesses achieve greater efficiency and cost reductions also deliver benefits to customers in the form of an improved customer experience. It’s a win/win proposition.
Digitization is transforming society into a “mobile-first” population. As messaging applications grow in popularity, chatbots are increasingly playing an important role in this mobility-driven transformation. Intelligent conversational chatbots are often interfaces for mobile applications and are changing the way businesses and customers interact.
Chatbots allow businesses to connect with customers in a personal way without the expense of human representatives. For example, many of the questions or issues customers have are common and easily answered. That’s why companies create FAQs and troubleshooting guides. Chatbots provide a personal alternative to a written FAQ or guide and can even triage questions, including handing off a customer issue to a live person if the issue becomes too complex for the chatbot to resolve. Chatbots have become popular as a time and money saver for businesses and an added convenience for customers.
How chatbots have evolved
The origin of the chatbot arguably lies in Alan Turing’s 1950s vision of intelligent machines. Artificial intelligence, the foundation for chatbots, has progressed since that time to include super intelligent supercomputers such as IBM Watson.
The original chatbot was the phone tree, which led phone-in customers on an often cumbersome and frustrating path of selecting one option after another to wind their way through an automated customer service model. Enhancements in technology and the growing sophistication of AI, ML, and NLP evolved this model into pop-up, live, onscreen chats. And the evolutionary journey has continued.
With today’s digital assistants, businesses can scale AI to provide much more convenient and effective interactions between companies and customers—directly from customers’ digital devices.
TYPES OF CHATBOTS
One of the most interesting things about chatbot software is the variety of chatbots out there. There are three types of chatbots that most consumers see today:
Rule-based chatbots: These chatbots map out conversations through predetermined rules. Rule-based chatbots respond to specific pre-defined options or keywords which allow them to guide the conversation based on the site visitor’s inputs. Keep in mind, though, that a rule-based chatbot is limited to its pre-determined rules. So it can only act or respond to things that have been anticipated.
AI chatbots: Unlike rule-based chatbots, AI chatbots are trained to analyze and understand a site visitor’s intent, then deliver the answer they think is best based on existing data.
With a Conversational AI platform, you can give site visitors the freedom to guide the conversation in their own words. What’s more, these chatbots continue to learn and refine their responses as they collect more and more conversational data.
Live chat – These chatbots are primarily used by sales and sales development teams to connect with site visitors for real-time conversations. Customer support organizations also use live chat software to answer questions in real-time.
As you can see, the way these chatbots work varies quite a bit — and they help your business in different ways. For example, an AI chatbot can help your sales team focus on high-intent conversations, whereas a live chat solution helps you better serve your customers. Ultimately, what chatbot you choose to use will depend on the goals you have.
But how do you know where your customer is in the buying journey? And how do you send them the right content at the right time? You can find the answers in The Conversational Marketing Blueprint.