Understanding Chatbots and Their Architecture: Do You Need One?

The concept of a chatbot that can mimic human-like responses when interacted with, existed quite early. As early as 1966 by the name ELIZA that matched its scripted human-like answers to user prompts.

Eliza, creation of Joseph Weizenbaum at the MIT Artificial Intelligence Laboratory

Now, fast forward to the 21st century, AI (artificial intelligence) has revolutionized chatbots; which has provided businesses with leeway to enhance its customer support experience and operations, entirely.

However, as common as the term ‘chatbot’ has become, organizations haven’t been able to fully make use of this efficient technology as of yet. This could be owing to their lack of knowledge on the subject and fears associated with its incorporation within their business and its long-term potential.

This is why we are getting into the nitty-gritty of chatbots for you to have a better understanding of what it is, how it is developed, and more.

What is a chatbot?

A chatbot is an artificial intelligence (AI) software that can engage in conversations as a regular human being through channels like messaging apps, websites, mobile apps, integrated or as independent software solutions.

These chatbots follow a question-answer system, while the recognition of the queries including the appropriate replies is determined by AI and machine learning.

Some of the sectors these chatbots are benefitting tremendously include eCommerce, online marketing, travel, tourism, healthcare, and on-demand services.

So how do chatbots work?

There are two steps that are performed by a chatbot when a user sends an input.

1. User request analysis
2. Returning a response

When a chatbot receives input from the user, the first task it performs is analyzing it against different metrics; which are the user intent and extraction of different data present within the user’s request. Once the intent has been deduced and identified, the chatbot then serves the user with an appropriate response.

Types of chatbots

There are two major types that exist are:

1. Processing inputs as commands
2. NLP (Natural Language Processing)

Processing inputs as commands

A rather traditional kind of chatbot that is programmed with a set of command rules. So the queries that are coming from the users are taken as commands by the chatbot, which then responds by retrieving relevant information.

Many companies have such bots integrated within their websites that clearly states about the commands that users can give to the bot to obtain information. However, the bot is incapable of understanding natural human conversation and would forward the query to an actual customer support agent.

So for businesses that receive a certain type of queries, again and again, can make use of the bot that is aware of the predicted questions and replies instantly with the right answer. Take an eCommerce platform for example; these sites have consumers inquiring about the same few things that include order placement details, booking of their orders, and more.

Using NLP

Natural language processing powers the chatbots to converse in a human-like manner. These bots are quite learned in the human ways that one cannot even detect that they are speaking to a robot. However, the quality of communication depends entirely on the construction of libraries as NLP chatbots work on keywords they fetch from pre-defined libraries. In addition, they are also constantly improving with the help of machine learning.

Now we know what chatbots are and their types, you should also be aware of their workflow. This is the part that clarifies the need for a chatbot for your business and how you can leverage it further by getting a custom chatbot made entirely from scratch by acustom software development company in Houston.

Architecture Models of Chatbot

Generative models

These models are difficult to build and develop. Basically, the generative model is used to develop smart bots that are advanced and intelligent. However, since this type of chatbot requires complex algorithms and an exhausting investment of time and effort; they are rarely made use of.

Retrieval-based models

The easier model that is not only easier to build and develop, but is also more reliable. Reliable because this model follows chatbots to have a predefined list of messages that they can choose from. This removes any uncertainty of inappropriate or wrong replies from the chatbot. Moreover, there are several algorithms and APIs available for developers to built chatbots easily.

All in all, it is a win-win model for both the businesses in terms of functionality it provides and the developers.

Now that we understand the two models of chatbot architecture, we must look at the process through which chatbot understands the user’s message or the intent within the message.

Response Generation Mechanics

1. Based on patterns
2. Intent identification and classification through ML

Based on patterns

At this point, you are aware of what ‘pre-defined’ set of rules or messages are. Pattern-based heuristics includes choosing a response that can either be based on if-else logic or machine learning classifiers. Here, simple technology is applied that involves using patterns. AI and machine learning languages [AIML] are used by developers for chatbot development.

Once a message is received from the user, it is checked against all the patterns until the right one is matched. Once matched, the chatbot chooses the template to generate a response.

Intent identification and classification through ML

The IT mantra is ‘automation’. So having a pattern-based solution that needs to be done manually is time-consuming and pointless. Especially if you are developing a chatbot for a business that deals with hundreds of incoming messages; you are looking at hours and hours of work and slow chatbot performance as it will have to distinguish a plethora of intents.

On the other hand, machine learning technology that trains the bots to pick up patterns of data and learn from it by itself is much more convenient and a progressive solution. Developers also have machine learning libraries and APIs to choose from, reducing their development cycle.

So far so good? Let’s check out some of the famous examples of chatbots.

Chatbot Examples: Best chatbots in 2020

These examples of successful chatbots will give you a proven insight into how beneficial chatbots turn out to be in boosting businesses and increasing their revenue. So shall we?

#1 World Health Organization (WHO)

The latest example of a chatbot is the one initiated by WHO for WhatsApp called the WHO Health Alert to curb the spread of misinformation on COVID-19. WHO provides authentic and trusted information to the masses.

#2 National Geographic

Thanks to National Geographic’s bot, you can talk to, well, Einstein. This chatbot was developed in efforts to promote their new show Genius. you can have a complete conversation and the bot will reply to you intelligent answers and information about the show.

This genius idea led to a 50% increase in user-engagement and boosted conversation time to 6-8 minutes; which is impeccable for a chatbot.

#3 Kia

Who doesn’t know about the Kia company? Even though the automotive industry is pretty complex to have a perfect chatbot, yet Kia managed it perfectly. Kia’s chatbot uses the Messenger and has generated 3x more interaction talking to 115,000 users per week. These impressive numbers led to a massive 21% conversion rate. So you can imagine what it can do for you when developed and implemented the right way.

So what are you waiting for?

Start hunting for a software development firm that excels in chatbot development and integrates yours with your business immediately. KoderLabs can help you start your journey, identify your business requirements, goals, and objectives to create a chatbot that serves your visitors in the best capacity, and with respect to the services you offer.

Feature Image Creds: IBM