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In the tech world, the year 2016 was parturition of a technology called ‘Chatbot.’
Chatbots became the talk of the town when Facebook’s announced the launch of its Messenger platform in April 2016. The growth of Chatbot with a preferred platform for user interaction is escalating day-by-day.
Talking about chatbot technology, if I ask you to agree with me that today the topic of Chatbot technology and how it has been simulating conversations with computers, is not something new to the ears, you won’t have any reason to deviate. The Bot, also popularly known as the Offspring of Chatbot, has been grabbing the limelight for allowing the automation tasks that the user performs.
With this present-day neurosis, Chatbots have been taking the most prominent place in communication and therefore gave rise to many questions that deserve apt answers.
I have provided the journey of chatbot from novice to a superstar that would resolve the mystery of why Chatbots are your favorites.
Let us get supercharged by diving deep into the details.
I will keep it simple — a real visitor chats with an application based on some rules and algorithms established in advance. They find an important place in marketing and sales assistance.
Talking in simple terms, a bot is an automated tool that makes the task easier. The bots that have the capacity to understand the natural language is capable of keywords recognization and execution based on them.
Bots present those customers with a list of buttons so that they could click and move the interaction to the next page. For example, H&M, a renowned fashion retailer, aids the users through a bot on Kik to find products they may like.
For making the customer service more efficient, bots prove themselves as best in dealing with simple queries. Retailers need to pay attention to the capabilities in minimizing customer frustration and customer effort because bots don’t understand complex language.
If I have to speak in a techie lingo, then I would say that it is an automated program running over the internet. Some of the bots are capable of running automatically whereas others execute commands after receiving the input.
Some common examples include web crawlers, chat room bots, and malicious bots.
“Handle the customer service issues with care as your customers would decide the longevity of your business.”
With this golden rule, companies have been opting for AI solutions and are expected to have a triple boost up by the end of 2019. With the market getting acquainted with the basics of AIimplementing AI solutions will become a fad. You can say that inbound marketing and user experience together will touch new heights beyond our imagination.
The Chatbots are capable of providing 24/7 automated self-service solutions and thus handle the customer related issues intelligently. They are capable of handling the frequently asked questions, the underlying transactions, and many more.
You could locate chatbots on websites, social media channels, and now capturing the mobile apps which aim to use software for simulation of human conversation. Thus the timely addressing of issues by the chatbots results in increased efficiency by reducing the volume of calls from the customers to the staff.
Chatbots are responsible for increasing the engagement rate with the customer on various levels. Right from brand coverage to natural interaction including intimation of current deals, latest content, etc., chatbot gives you a lot of opportunity for creating a buzz and gather more audiences.
Chatbots have tremendous capacity in providing a personalized experience to the users. The messages shot out by the chatbots need to resonate with the customers right from the very beginning.
In 2019 and beyond, chatbots will accord itself with search functionalities and with the capability of acting as a separate search engine. With chatbot’s ability to understand voice-based messages with text, they can provide you with all the important details related to research at a single place without leaving the app.
Lead generation is one of the most crucial factors for marketers. You might be wondering chatbot’s role here. Well, to your surprise, chatbots come to your rescue to avoid unnecessary data collection.
For instance, a chatbot would reach and solve the issues irrespective of the number of people reaching out. Once the targeted audiences are engaged, the chatbot starts shooting relevant questions/responses that keep the customer engaged.
These killer contents by the chatbots will help to identify precisely the potential leads as they would interact depending on the qualifying questions. Thus it doesn’t need to reach a bundle of users and target only them who are more likely to get converted.
Isn’t the benefits of using a chatbot alluring?
Did You Know…? pic.twitter.com/fCeQ3N5ezO
— eSparkBiz (@eSparkBiz) April 25, 2019
They are not new in the market. They have marked their presence in text messaging and are used by many companies for the same purpose. The creation of rule-based chatbots by the developers solely rely on a set of predefined terms and rules which when given as input outputs a suitable result complimenting the action taken.
Example: Rule-based chatbots check the prepaid balance with the help of text messages by your mobile service provider. A command in the form of a specific text is sent. The chatbot on the server on receiving the text extracts it and starts executing.
This genre incorporates sophisticated chatbots built for messaging apps like Facebook messenger, Slack, etc. The chatbots interact with humans using the normal language. The chatbots decode the message easily and reply accordingly. The input message is decoded into structured data and further broken down into commands.
I could smell some curious minds! I know what you want to ask. Chatbots are fueling your brains to think about them. This technology drives crazy to everyone who comes into contact with it.
So with this context, I will assume that everyone has interacted with the Facebook chatbot at least once. Due to its popularity, companies are widely adopting. But there is always one confusion that comes into the light. ?
The difference between Chatbot development platform and Chatbot publishing platform ❗❓
But before going into deep let us first see…
A chatbot platform is a simple concept that makes you familiar with the chatbot. With a simple approach with drag and drop option, you can handle the chatbot without any technical experience.
A chatbot publishing platform and A chatbot development
Chatbot publishing platform acts as a medium for the users to access and use the chatbots. For Example, Facebook, Snapchat, etc., provides a platform to express the thoughts and you can publish them which in turn will be visible to all.
A chatbot development platform is a tool or even an application through which you can create your chatbot. Functionalities to the bot are added with the help of the chatbot development platform by creating a flow, integrating API, capabilities of machine learning, etc.
The next question is ready; it seems❗
Isn’t it❓
The chatbot development platforms enable you to create a chatbot without coding in minutes. You can yourself design, build, test, and deploy chatbots with the platforms I have listed below. Some of the mentioned chatbot platforms are even free. A complex chatbot can’t be built without a professional mobile app development company.
If you have the desire to built a complex, high-level chatbot for your business, then get in touch with a leading chatbot development company providing world-class chatbot development services.
The following chatbot development platforms I have listed below will serve your need:
Your business needs a custom chatbot development if you consider the growing demand for chatbot technology.
Let us give our fingers some work. I will guide you to build a chatbot using Python. Let us give a try together.
NLTK has a module nltk.chat. This simplifies building these engines by providing a generic framework.
I will be using two imports from nltk.chat.util:
Chat is a class which consists of all the logic used by the chatbot.
A Reflection is a dictionary including a set of input values and its relevant output values. You can create your own dictionary as well.
The format is given below:
reflections ={ "i am" : "you are", "i was" : "you were", "i" : "you", "i'm" : "you are", "i'd" : "you would", "i've" : "you have", "i'll" : "you will", "my" : "your", "you are" : "I am", "you were" : "I was", "you've" : "I have", "you'll" : "I will", "your" : "my", "yours" : "mine", "you" : "me", "me" : "you"}
Try creating yourself. For example:
my_trial_reflections={ "hi" : "hello" "Stop" : "Stopped" }
and use this as:
chat = Chat(pairs, my_trial_reflections)
Keeping this concept in our kitty, let us move ahead for creating a simple but not intelligent chatbot from Python’s NLTK library without using any complex algorithm.
from nltk.chat.util import Chat, reflections pairs = [ [ r"my name is (.*)", ["Hello %1, How are you today ?",] ], [ r"what is your name ?", ["My name is Chatterbox and I'm a chatbot ?",] ], [ r"how are you ?", ["I'm goodnHow about You ?",] ], [ r"sorry (.*)", ["Its alright","Its OK, never mind",] ], [ r"i'm (.*) doing good", ["Nice to hear that","Alright :)",] ], [ r"hi|hey|hello", ["Hello", "Hey there",] ], [ r"(.*) age?", ["You are kidding me!nI'm a computer program buddynSeriously you are asking me this?",] ], [ r"what (.*) want ?", ["Make me an offer I can't refuse",] ], [ r"(.*) created ?", ["Hari created me using Python's NLTK library ","top secret ;)",] ], [ r"(.*) (location|city) ?", ['Ahmedabad, Gujarat',] ], [ r"how is weather in (.*)?", ["Weather in %1 is awesome like always","Too hot man here in %1","Too cold man here in %1","Never even heard about %1"] ], [ r"i work in (.*)?", ["%1 is an Amazing company, I have heard about it. But they are in huge loss these days.",] ], [ r"(.*)raining in (.*)", ["No rain since last week here in %2","Damn its raining too much here in %2"] ], [ r"how (.*) health(.*)", ["I'm a computer program, so I'm always healthy ",] ], [ r"(.*) (sports|game) ?", ["I'm a very big fan of Football",] ], [ r"who (.*) sportsperson ?", ["Messy","Ronaldo","Roony"] ], [ r"who (.*) (moviestar|actor)?", ["Brad Pitt"] ], [ r"quit", ["BBye take care. See you soon :) ","It was nice talking to you. See you soon :)"] ], ] def chatterbox(): print("Hi, I'm Chatterbox and I chat alot ;)nPlease type lowercase English language to start a conversation. Type quit to leave ") #default message at the start chat = Chat(pairs, reflections) chat.converse() if __name__ == "__main__": chatterbox()
Let us understand the code.
The function chatterbox() is invoked, a default message will be displayed:
Hi, I'm Chatterbox and I chat alot ;) Please type lowercase English language to start a conversation. Type quit to leave
If you see the above source code, I have created an instance of chat class containing pairs and reflections as discussed above. Pairs consist of a set of questions and answers tuples listed in the source code.
Now trigger the conversation
chat.converse()
A simple conversation has started now:
Hi, I'm Chatterbox and I chat alot ;) Please type lowercase English language to start a conversation. Type quit to leave >hi Hello >how are you? I’m good How about you? >i'm doing good Nice to hear that >What is your age? You are kidding me! I'm a computer program buddy Seriously you are asking me this? >Sorry Chatterbox Its OK, never mind >quit BBye take care. See you soon :)
The working of the ntlk.chat chatbots are completely on the regular expression of the keywords present in your queries. Therefore you can add as many questions as you want in a well-mannered format so that you don’t confuse your bot while determining the regular expression.
We are currently at a stage where we are witnessing the increased usage of the chatbot. Chatbots have made their presence felt in social media platforms like Facebook, Telegram, Slack or even your own website.
You have to hold a lot of patience if you desire to build a herculean Client Retention Management(CRM). Chatbot aids in boosting your business cycle and bears the capacity to manage the CRM routine professionally.
Since chatbots are backed by AI, the language is decoded unmistakably and is capable of replying the person in such a manner as if the user is talking to a real person. It is further capable of collecting the data easily in relevance to the existing and usable for future customers.
We are already aware of the benefits of having chatbots for our business. Let us explore the Chatbot frameworks that would aid in chatbot design and development.
This framework will serve for your multipurpose needs. It only is used for bots but also for creating wearable devices, voice interface for any mobile application, and electronics hardware used in homes.
Wit.ai is free, and the list given below is the SDK available:
Check out the top 10 chatbots making headlines for 2019. The list of chatbots is given below:
Do you want to create your own bot? ?
In this section, I will guide you in creating a new bot.
Nodejs comes into the picture. Nodejs with express or restify is required. Now create a bot folder for holding the directories and this will be your working folder.
$ mkdir nodebot $ cd nodebot
The project is initialized. Install express or restify.
$ npm init $ npm install express --save $ npm install body-parser --save
Install botbuilder
npm install botbuilder --save
Setup ESLint (I have done it but not necessary you have to do it too.)
$ npm install eslint --save-dev
By adding the following, start your simple service
const express = require('express'); const bodyParser = require('body-parser'); const app = express(); app.use(bodyParser.urlencoded({ extended: false })); app.use(bodyParser.json()); // middleware error handling app.use((err, req, res, next) => { if (res.headersSent) { return next(err); } console.error(err.stack); return res.status(500).json({ message: err.message }); }); app.listen(process.env.PORT || 3000, () => { console.log('Example app listening on port 3000!'); });
Go to http://localhost:3000 in your browser; you will get a response back from your server.
Get back to the nodebot folder. You have to add a controllers folder and a new.js file in there. The heart of our bot is with this new.js folder. The requests from the users will be heard. It would call the newsapi to fulfill the user’s requests and then answer with relevant, befitting replies.
There is a need for owning a bot app id and password. Go to https://dev.botframework.com/bots/new and register for your bot. The required information will be received through these steps.
Put this code in the new.js file that you had created above.
const builder = require(‘botbuilder’); const env = process.env; function init(app) { // Create chat bot and binding const connector = new builder.ChatConnector({ appId: env.BOT_APP_ID, appPassword: env.BOT_APP_PASSWORD, }); app.post(‘/api/news’, connector.listen()); const bot = new builder.UniversalBot(connector, (session) => { session.send(‘Sorry, I did not understand ’%s’. Send ’help’ if you need assistance.’, session.message.text); }); // Print out help message bot.dialog(‘Help’, (session) => { session.endDialog(‘Hi! this is a help message’); }).triggerAction({ matches: ‘Help’, }); } module.exports.init = init;
The following pieces should be added to your app.js. This actually calls the init function in the news.js and pass in an app function to enable us to listen to endpoint/API/news
const newsbot = require(‘./controllers/news’); newsbot.init(app);
Note: If you could notice, I have used process.env.BOT_APP_ID. This variable has to be defined in the nodejs environment so that the application runs.
Dotenv is a nice way of doing this https://github.com/motdotla/dotenv; At a later stage when our services will be deployed, we will need to set up these environment variables as well.
If VSCode is used, you can define in the launch.json like below:
{ “version”: “0.2.0”, “configurations”: [ { “type”: “node”, “request”: “launch”, “name”: “Launch Program”, “program”: “${workspaceRoot}/app.js”, “env”: { “BOT_APP_ID”: “xxxxxxxxx”, “BOT_APP_PASSWORD”: “xxxxxxxxx”, “PORT”: “3001” } } ] }
Now pat your back. You have just created your own bot who could listen to help command and can respond to the command.
An important thing to be kept in mind- Push your code to your favorite git repo.
BotFramework Emulator is available to test your bot locally. All you have to do is install the latest version. Then input the URL, Microsoft App ID, and password which you have created in the above steps.
Our world can be said to be driven by communications. Due to this, chatbots have been on the rise. They have marked their presence over all the messenger platforms.
The users have started imagining their lives without a chatbot. But in spite of its popularity and wide acceptance, the real challenge is to build a bot that brings value to the user.
Gripping the advancements in technology for machine learning and artificial intelligence, the developers are able to meet up the expectations and realize their dreams of building intelligent chatbots in the future that would know no bounds.
After reading the entire blog, many of you would be tempted to develop an intelligent Chatbot for your business or website. If that’s the case, then feel free to Connect with eSparkBiz . We’re one of the leading Chatbot Development Company in India that provides customized bot solutions to various business verticals around the globe.