Discover how low-code and no-code platforms are simplifying chatbot development, enabling faster, cost-effective, and scalable solutions without the need for extensive coding skills.
Software has undergone a sea change in the way it is developed, and, as we write this, there are new changes happening everyday. As Technology philosopher Ben “The Hosk” Hosking puts it, “Writing code is simple. Creating software is hard.” And this is the exact process that has undergone a massive transformation in the past few decades.
Computer programming, in the earlier days, was a discipline that was relegated to a few scientists and people with advanced degrees in mathematics and statistics. This is because computer science, as a discipline, had not evolved yet, and so programming was considered something of a fad for research geeks.
Fast forward a couple of decades, and two Dartmouth College professors gave the world the BASIC programming language, to make programming easier for students. Microsoft took this a step further and installed BASIC on many of its earliest computers.
Then came C, the programming language that was developed by Dennis Ritchie at the AT&T labs in 1972. C became one of the most popular programming languages ever, becoming the foundation for other languages such as C#, Java, JavaScript, Perl and Python.
Today, in the age of the internet, the language you write code in is becoming less and less significant. The only criteria to good software development is the ability to iterate fast and make changes on the go. This is where techniques like Agile software development come into the picture.
Waterfall development methodology of software development was seen as too slow and not receptive to change, which led to the emergence of Agile methodology. Things like Automation, ALM and DevOps also disrupted the way software is made.
The shortage of skilled software developers, along with factors outside the control of companies such as the pandemic, means technologies such as low-code no-code got a huge boost in their adoption rates. Let us now see the major differences between low-code and no-code technologies.
Imagine creating your own mobile or desktop application without writing a lot of code, by simply dragging and dropping elements. Low-code no-code technology lets you do exactly that. They are a form of visual software development environments that allow even non-technical business users to build software using intuitive, graphical interfaces.
Low-code no-code are two separate technological advances, albeit with subtle differences. While low-code technologies still require you to deal with a small amount of code, and are aimed towards developers who want to speed up the development process. Low -code platforms make the already good coders code faster.
Coming to no-code platforms, the major difference here is that these platforms require no knowledge of coding and can be used by business users to create professional looking apps in a matter of hours. No-code technologies do not provide any means of manually editing the code, giving developers limited control over the way the application behaves.
According to Gartner, 60% of all the custom applications are now built in other departments, not IT. Close to 30% are built by employeeswith limited technical expertise. Gartner also predicts that by 2024, 65% of all development activity will be on Low-code platforms.
Here are few other statistics that show why LCNC methodology is here to stay:
Low-code no-code development technology finds a special application among the following group of computer users:
The global forecast for low-code technologies is around $65 billion around 2027.This number is expected to reach $187 billion by 2030. This technology is bound to disrupt various industries, cutting across different business sectors and geographies. Implementing LCNC technologies allows businesses to automate business processes quickly and develop custom applications. Some of the industries that are going to see a sea change thanks to LCNC technologies are:
Healthcare is one industry that is using low-code and no-code technologies to develop custom applications that help hospitals improve operational efficiency. Hospitals, for instance, can use LCNC technology to develop applications that track patient data, help in handling insurance claims, streamline doctors appointment bookings, etc. We are just scratching the surface of what this technology can do to the healthcare industry.
Manufacturing is seen as a traditional industry, and, just like healthcare, it may seem that there is not a lot that LCNC technology can do here. But on deeper inspection, one can see that manufacturing industries can build a lot of custom applications to handle some of the more important functions such as inventory management, supply chain management, automating production processes, etc. Again, the possibilities of LCNC technologies here are limitless, and there are bound to be exciting times ahead.
Now the retail industry was always tech-savvy, having invested in software to track inventory and manage stocks, even before LCNC technologies came into the picture. With the advent of low -code technology, this industry will now see a host of custom applications developed by business users, in a host of interesting use cases. This could range from enabling customers to making purchases online, tracking orders, receiving personalized recommendations based on personal shopping history, etc.
Schools can use low-code technologies to create custom applications that increase student engagement, automate many of the administrative tasks, and streamline operations. These low-code applications can be either developed by teachers or students themselves, for whom it will be an interesting lesson in implementing theoretical education. Both the parties can build custom applications that can help students access course materials, track progress, and also communicate clearly with their classmates.
Unlike traditional software development, low-code chatbots are chatbots that are created using drag-and-drop interfaces or visual builders, which require less coding expertise.
It is now possible for developers to assemble chatbot components and configure their behavior without writing a lot of code, making the process of building chatbots faster and more easier.
No-code chatbots, on the other hand, are chatbots that can be created without writing any code at all. This is typically done using a conversational platform that provides pre-built components, templates, and workflows that can be combined and customized using a graphical interface. The goal of no-code chatbots is to empower non-technical users to create chatbots without the need for coding skills.
In both cases, the goal is to make the process of creating chatbots easier and more accessible. At the end of the day, companies want to build, deploy and put to good use conversational experiences, without the need to know how to code.
We will now look at why chatbots are important, and why your organization will benefit from implementing one.
Chatbots have become increasingly important in today's digital landscape for several reasons:
Having a chatbot on their website can save organizations time and money, when you compare them with traditional customer service and support methods.
Chatbots can provide 24/7 customer support, answering common questions and resolving issues quickly. This can significantly improve customer satisfaction and reduce response times.
Chatbots can deliver personalized experiences to users based on their preferences and past interactions, improving engagement and building customer loyalty.
Chatbots can handle multiple interactions simultaneously, increasing efficiency and reducing response times.
Chatbots can collect valuable data about customer interactions and preferences, providing insights that can inform business decisions and improve the customer experience.
Chatbots can automate repetitive tasks and streamline processes, freeing up time for employees to focus on higher value activities.
Chatbots can provide a convenient, accessible, and low-friction way for customers to interact with organizations, making it easier for them to get the information and support they need.
Now that we have seen a brief introduction into the world of building chatbots using low-code no code technologies, let us now take a step back in time and see how chatbots were built earlier. This will help us give much needed perspective.
There is a huge swath of information available online on the development of chatbots since the early 60s, with Joseph WeizenBahm’s ELIZA and then Parry chatbot created by American psychiatrist Kenneth Mark Colby. The history of chatbots will be a fascinating read, no doubt, but let us focus more on how chatbots have evolved since the 90s, since that will give us better context.
2001- now that is the year that chatbot development got a lot more interesting, and a timeframe that we will start our focus from. It was in 2001 that the chatbot SmarterChild was unleashed onto the world.
SmarterChild was available on AOL Instant Messenger and MSN Messaging Networks. If you are from the 90s and remember using these services, then SmarterChild sat right there inside every user’s buddy list. You could message him for data on a wide array of topics, ranging from weather forecasts, sports, news and even movie timings.
Created by Robert Hoffer, Timothy Kay and Peter Levitan, the bot was quite a rage in its heyday. It is said to have interacted with more than 30 million people and accounted for more than 5% of all AIM traffic. The company that built SmarterBot, called ActiveBuddy, was eventually acquired by Microsoft in 2007.
Slowly, but surely, chatbots were becoming more and more smarter, able to converse with human beings in a way that was more empathetic than ever before. Take the case of Mitsuku, developed by Steve Worswick in 2005. The chatbot was emotionally intelligent, and many users felt they were actually speaking to a human being.
It was around this time, that in 2006, that IBM introduced the Watson chatbot, named after its very first CEO, Thomas J. Watson. This question- answering system appeared on the hit US television show Jeopardy, and went on to win the highest prize of $1 million. Not bad, considering the fact that this happened close to 16 years ago.
29 June 2007 was a historic day, not just for chatbots but for the computing world in general, and geeks will surely remember this day. This was the day Steve Jobs introduced the iPhone to the world, and the world of computing has never been the same.
People, who were used to interacting with software built by large development teams, were now using something called “applications,” or apps in short. The App Store was launched, where indie developers could put together applications without needing backup from a large multinational. An idea truly did change the world.
Come 2010, Apple pushed the technological envelope one step further and introduced Siri. Siri was a virtual operating assistant that used voice queries and an interface that relied heavily on natural language understanding. Siri could be used as a search engine to make enquiries, assign tasks, set reminders, and even give advice. It was the first voice enabled bot of its kind.
In 2012, Google followed suit with Google Now, which was again a chatbot that answered a few basic questions. Google Now slowly evolved to Google Assistant, the smart AI powered assistant that we are all familiar with today. Around 2014, both Microsoft and Amazon entered the voice- activated chatbot technology. Microsoft called their version Cortana, and Amazon called theirs Alexa.
Social media companies, which were already undergoing paradigm shifts in the way users interacted with each other and the platform in the late 2010s, was also a new frontier for chatbot technology. Platforms like Facebook Messenger, Slack, Telegram and WhatsApp all began implementing chatbot technology into their services.
Today, chatbots are more advanced than ever, and more knowledgeable than some search engines (read: ChatGPT). But this was not always the case. Before LCNC technologies democratized creating chatbots, they still had to follow the traditional software development methodology.
There was a time when chatbots were built using Python, Java and C#, extensively mapping out possible user queries and bot responses. So what changed in the past 20 years was not just how smart the chatbots became in their ability to give us user responses, but also how easy it became to actually build and deploy these chatbots.
The chatbot market today is more mature, with everyone ranging from business users to students developing bots. Technologies such as low-code no-code have democratized the bot building process, and today, you can be up and running building a bot in a matter of minutes.
The global chatbot market is forecasted to be valued at $1.25 billion in 2025, and is projected to register a CAGR of 30.29% over the forecast period from 2022-2027.
Chatbots today are built using a combination of machine learning, Natural Language Processing and Artificial Intelligence technologies. These technologies make chatbots more responsive to natural language inputs, such as text-based messages or spoken language, and provide automated responses to users.
There are various methods to build chatbots today, but we will look at 2 of the most popular methods. One approach is to use a neural network-model, such as a sequence to sequence model or a transformer model. The training for these models comes from volumes of conversational data, such as chat logs or customer service transcripts. These chatbots then learn how to respond to user queries using patterns or structures in the data.
Here is a breakdown of how these Neural Network model chatbots are built:
The first step in building a chatbot based on neural networks is to collect and preprocess a large volume of conversational data. This data will include both questions and answers, and preprocessing should be done to remove noise, extract important features, and encode the data in a format that will act as input for the neural network.
The next step in building a chatbot using neural networks is to define the neural network architecture. This can be done using frameworks such as Tensor flow, Pytorch or Keras. The design of the architecture must be such that it should be able to handle natural language inputs and generate appropriate responses.
Training the neural network on the preprocessed dataset is the next step in building the chatbot. This involves optimizing the network’s weights and biases to minimize the differences between the predicted responses and the actual responses in the dataset.
After the chatbot has been trained, it has to be evaluated to ensure that the chatbot is generating appropriate responses. You can accomplish this using a variety of metrics, such as accuracy, perplexity and F1 score.
The final step after training and evaluation is actual deployment of the chatbot, once it is sure to be effective. This deployment generally involves integrating the model with a communication platform, such as Facebook Messenger or Slack, and setting up an interface for users to interact with the bot.
As you can see, the process of building a chatbot using neural networks is complex and challenging, involving knowledge of machine learning, natural language processing and software development.
An alternative to this method, which we will now look at, low- code no-code platforms and frameworks, such as Google Dialogflow. Here is a list of general steps that one must follow in order to build a chatbot using a LCNC technique.
The first step is to research and select a chatbot builder platform that provides all the features and functionality you need to build a chatbot. Some of these no-code platforms include Google Dialogflow, Tars, Kommunicate and Manychat.
You should have a clear idea of what you plan to accomplish with the chatbot. What are the specific tasks or functions that the chatbot will perform? Once you have clarity on these questions, you can move on to the next step of planning the conversational flow.
Plan out the conversational flow of your chatbot, including what the users will input and how the bot will respond. Some of the bot builder platforms we mentioned earlier have a visual drag-and-drop interface that you can use to design your chatbot.
You may need to use Natural Language Processing (NLP) or Machine Learning to train your chatbot, depending on the bot builder platform that you choose. This involves providing the chatbot a large dataset of conversations from which it can learn.
Once you have completed building your chatbot and training it, you can then go on and integrate it with a messaging platform of your choice. This could be from a variety of popular messaging platforms such as Facebook Messenger, WhatsApp, or Slack.
It is now time to put your chatbot through the paces, testing it to ensure that it is working as expected. Make sure to collect as much user feedback as possible, and iterate on the design to improve your chatbot further.
As you can see, both the methods of developing chatbots are similar to a small degree, but in low-code techniques, you don’t need to write a single line of code.
This is a trick question, and the answer is something that we have been pondering a lot about lately. But if we were to give a one word answer to the above statement- the answer would be a big No.
Chatbot platforms like Kommunicate, which lets you build a chatbot in under 5 minutes without writing a single line of code, will definitely help more business users adopt chatbot building. Chatbots will thus get smarter, able to understand user intents more clearly, and respond accurately. Infact, an IBM study says that in 2023, chatbots will be able to answer upto 79% of incoming enquiries accurately. Chatbots are also faster, and an Invesp study says that chatbots can answer most of the questions 80% faster than live agents.
Like we said, no-code chatbot platforms come with their own set of limitations. This is mainly in the form of features and functionalities that no-code platforms can provide. Some chatbots may require custom coding to create unique features or capabilities, which no-code platforms will find difficult to accommodate. Sometimes, you may need to integrate your chatbot to complex systems, which again, will require some form of coding. This is where no-code platforms again find a disadvantage, and why you cannot rule out the importance of coding while building a chatbot.
We see a bright future ahead for low-code no-code chatbot technologies, with more chatbot builders offering customers the option to build bots easier than ever before. Machine learning and automation will make designing apps with low-code / no-code technologies even easier. Developers will hasten their pace with the help of tools such as OpenAI’s Codex, where code snippets will be auto inserted, just like we see it happening with text in Gmail. This does not mean humans will become obsolete in coding, of course. On the contrary, human ingenuity will be able to solve more and more complex problems, and low-code no-code technology will be just one of the methodologies to achieve that end.
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