Are you excited to know what a chatbot is and its type? Here, we will cover them in detail. Also, With a growing number of web and mobile apps in the market, in the recent past, written language and speech are rapidly becoming the popular user interface of the current trend and future. We are already seeing voice assistants (like Alexa, Siri, and Google Assistant) or textual chatbots (like Duolingo, Hipmunk, and TechCrunch) influencing the technology and how people use it in real life. Some are even thinking that bots might even kill web and mobile apps because of cost efficiency, performance, speed, and increase in artificial intelligence.
In this series of articles, we would like to share our learnings of the challenges of building and testing a chatbot in a short amount of time. We also like to share some sensible practices, testing strategies, and different types of testing to do in different layers to build a quality and valuable chatbot for any business.
Before we go deeper into testing the chatbots, let’s look at and understand the essence of chatbots in the following.
- What is a chatbot or bot?
- Why do we need a chatbot?
- Types of chatbots available in the market based on evolution
- Classification of chatbots based on usage
- How does a chatbot work?
- Where are chatbots used?
- Companies providing chatbot solutions/services
Ready? Let’s go.
What is a Chatbot or Bot?
- In short, A Chatbot is a program that simulates a natural human conversation. Users communicate with a chatbot like how they would talk to a real person.
- Chatbots interpret and process users’ words or phrases and provides an immediate pre-set answer or use AI techniques to answer.
- Moreover, They occupy platforms like – FB Messenger, WhatsApp, Skype, Slack, Line, Kik, Wechat, or even your website.
Why do we need a chatbot?
- Intelligently address customer needs
- Reduce customer leaving rate
- Enhance overall customer experience
- Increase engagement with the brand
- Reduce repetitive customer calls
- Increase response time of queries
- Cost-efficient and time-efficient
Types of chatbots available in the market based on evolution
- Rule-based/Menu/Buttons/Scripted/Quick Reply/Action Chatbots
- Keyword Recognition/Intellectually independent Chatbots
- Contextual/ AI- powered chatbots
- Voice-Enabled Chatbots
Rule-based/Menu/Buttons/Scripted/Quick Reply Chatbots
- People collaborate with these bots by clicking on buttons and using pre-defined options.
- These chatbots need individuals to make a few selections based on the options displayed to provide relevant answers.
- As a result, these bots are the slowest to guide the customer to their goal.
Keyword Recognition/Intellectually independent Chatbots
- Keyword recognition-based chatbots can listen to what users type and respond accordingly.
- Generally, These chatbots apply customizable keywords and AI to determine how to deliver a devoted response to the user.
Contextual/ AI- powered chatbots
- Contextual chatbots are the leading kind of conversational bots because they take advantage of Machine Learning and Artificial Intelligence to remember conversations that happened in the past for specific users and try to learn and grow over time to improve themselves.
- These chatbots learn with their real involvement with the user. Some examples of contextual chatbots are Siri, Alexa, Google, etc.
Voice-Enabled Chatbots
- Voice-enabled chatbots build a personalized experience for the users.
- These chatbots accept user inputs through voice and answer their queries.
- Also, by using text-to-speech (TTS) and voice recognition APIs, businesses can create their voice-enabled chatbot.
Classification on chatbots based on usage
- Personal Chatbots
- Team Chatbots
- Domain/Brand-Specific Chatbots
Personal Chatbots
Personal chatbots provide direct communication between an individual user and the bot. E.g., Weather Bot.
Team Chatbots
Team chatbots are used when multiple users are involved in the communication process. E.g., Slack.
Domain/Brand-Specific Chatbots
Domain/Brand Chatbots are preferred when the company’s service needs to go deeper into detail to serve customers like notifications on discounts, relevant searches on location, price, etc. E.g., Airbnb, MedWhat, etc.
How does Chatbot works?
Chatbots work based on three classification methods:
- Pattern Matches
- Natural Language Understanding (NLU)
- Natural Language Processing (NLP)
Pattern Matches
- Chatbots make use of pattern matches to organize the text. And it produces an appropriate response from the clients.
- Artificial Intelligence Markup Language (AIML) is the standard model for these Patterns.
- Chatbots behave to anything relating to the correlated patterns.
Natural Language Processing (NLP)
- NLP or Natural Language Processing is a cover term used to describe the following
- Machine ability to process what is said to it
- Break down into multiple parts
- Understand its meaning
- Find the correct action
- Respond in a language the end-user will understand
Natural Language Understanding (NLU)
- NLU or Natural Language Understanding is a subgroup of NLP that approach with how to handle unstructured inputs from users like mispronunciations, swapped words, contractions, colloquialisms, etc. to convert and organize into structured inputs so that machine can able to understand and determine.
Natural Language Generation (NLG)
NLG or Natural Language Generation is used to turn structured data into text.
Chatbot Architecture
Let’s see an example to understand the chatbot architecture.
Imagine you are going to buy a watch in the showroom. What are the questions that will come to your mind? Watch brand? Size? Price? Type? So, based on your needs, you will process the search and buy in real-time. Chatbot follows the same process behind the scene. So, let’s walk through step by step guide to understand the chatbot architecture in detail.
Understanding the chatbot architecture
- You find a product on the Amazon Alexa platform, and let’s say it’s Watch. You will be using the presentation layer of the Amazon Alexa Chatbot, and all the inputs you provide will be transferred to the backend API layer to be processed at the next layer.
- Natural Language Processing converts text to structured data i.e. in coded commands, so that the next decision engine layer will understand and process.
- Now the decision engine loop will think to exit the conversation loop to meet certain criteria because it needs more information like what brand watch, Size, Price, etc.
- Then this array of responses will go to the backend API and will be shown as a question to the user in the presentation layer.
- Now, the user will again give the specific values for the watch he wants to buy.
- Again, the data will go back through NLP into the decision engine, and this time bot will analyze data given by the user to find a watch that is available in stock with the store and also gives back the available watches in the stores nearby.
- Once you choose the product you want, you will be directed to the payment page and places the order for you.
One of the biggest challenges of chatbot industry facings is in understanding the conversation with empathy. Like humans, conversational tones will not be able to understand by the chatbot because it is still a bot. But the industry and Python software development services providers and developers are working towards it, and sooner, the chatbot will be able to respond with feelings.
Where are chatbots used?
- E-commerce and online marketing
- Travel, hospitality, and tourism
- Healthcare
- Banking and finance
- Customer service
- Education & Learning
Let's look at how chatbot helping in each of the above industries in detail
E-commerce and online marketing:
- Substituting for emails
- Managing sales funnels
- Adding interactivity
- Building customer relationships on a more personal level
Travel, hospitality, and tourism
- Engage audiences
- Anticipate user needs
- Give recommendations on nearby locations
- Offer automated python development services
- 24/7 customer service
Healthcare
- Support self-care and self-monitoring
- Offer reliable medical information
- Get important information from new patients
- Perform automated appointment follow-ups
Banking and finance
- Account alerts and notifications
- Tips and suggestions on financial management
- Help with enterprise resource management
Customer service
- Automating frequently asked questions
- Differentiating between questions the chatbot can answer and questions that should be referred to a real person
Education & Learning
- Intelligent, instant teaching and training models
- Agile adaptation to the user's ability
Companies providing chatbot solutions/services
- IBM - IBM Watson
- Microsoft - Bot Framework
- Facebook - Wit.ai
- Google - Dialogflow
- Amazon - Lex
- AI
In the next part of the article, we will look into how the chatbots are built using Amazon Lex and technologies, the skills needed to develop a chatbot, and how to test the Chabot from a functional perspective through manual testing. In the meantime, please share this article with your friends.
