It’s about time that businesses consider chatbots as an effective business tool, instead of just an afterthought or a ‘maybe’. If you think about it, modern technology has major effects on business operations today. Regardless of the size of your business, it’s undeniable that chatbots can provide both tangible and intangible benefits like assistance and convenience to both the supplier and consumer. 

This guide on how to build a simple chatbot will teach you the tips and tricks to make it possible to create a bot even if you don’t have a great deal of money in your account or an IT degree to back it up. With the help of the latest technology, anyone can create a bot that can assist the backend of a business while still producing the results customers demand. 

In this guide, we’ll be tackling what a chatbot is, the different types of chatbots, when you should use them, how to build a chatbot from scratch, and 6 bulletproof tips when creating your own.

What is a Chatbot? And most of all, why are they important?

Chatbots play a huge role when it comes to revolutionizing the way companies communicate with their customers. In today's busy business environment, it is crucial to interact with clients as quickly and clearly as possible. 

Before chatbots existed, the only option customers had to communicate with a company was to either call or email customer service. Now, there are several companies that offer a chat robot that provides a convenient and 24/7 customer service. Chatbots are conversational computer programs or applications that are designed to mimic written human speech. Users can communicate with this tool by using messaging platforms or the company’s website the same way they would converse with another person. 

These advanced bots are essential for any business, especially since the majority of e-commerce stores are competing with enterprise giants like Google, Facebook, Microsoft, and Amazon who set a pretty high standard for customer service; their impeccably fast response rate to a query is one of the reasons that helped them dominate the market for years. 

These massive inexorable forces unintentionally reshaped the expectations of both the user and the client. This special treatment, believe it or not,  is subconsciously embedded in each customer. Customers expect to get the same supreme, immediate, and relevant replies from a company’s customer service team whenever they ask a question and wait for an answer. With all that weight put on the shoulders of human resources, it’s nearly impossible to provide consistent customer service that lives to this standard. Luckily, this is where Chatbots can be of help. Just make sure that you use the right chatbot for the job. 

What are The Different Types of Chatbots? 

Understanding the factors that contribute to the efficiency of chatbots can help businesses make a conscious decision on which chatbot fits best for their platforms. There are two different chatbots: Rule-based Chatbots and AI Chatbots

1. Rule-based Chatbots

The easiest explanation for rule-based chatbots is that these bots can only provide a predefined answer to very specific questions. It’s important to remember that it can't answer questions that are outside of the defined rules. Rule-based chatbots are only effective and efficient in the scenarios you trained them for.

Rule-based bots are substantially faster to train than AI bots but it does come with limitations—one of them is that they can’t learn on their own. The answers they provide to the customers rely heavily on the answers given by the people that train them. Although, this benefits the company when it comes to consistency. Given that rule-based bots are boxed in the program they’re trained in, they will provide a consistent and uniform answer to the customers. If the conversation does enter an unknown field to the bot, it will pass the conversation to a human representative instead to handle the situation. 

2. AI Chatbots

On the contrary, AI bots are self-learning bots. These advanced bots operate through Natural Language Processing (NLP). NLP is exactly the same psychological approach that constructed voice recognition systems utilized by known virtual assistants like Siri, Google Now, and Microsoft’s Cortana.

With NLP as the root of AI bots, it is designed to understand the context and purpose of a question before mapping out an answer. The more these bots are trained, the more they learn. Even though it takes a long time to train them; once they’re properly educated and equipped, they can keep up with the user no matter how complex the situation is. 

AI chatbots are beneficial for deep learning. Unlike rule-based chatbots, AI chatbots can be programmed to understand the emotions or current mood of a customer, even without the assistance of human forces. It provides personalized services to each customer and if it’s trained long enough, it can also understand and communicate in different languages. 


In What Situations Should you Build a Chatbot?

The customer’s demands are pretty simple. They want the fastest and most effective solutions to their problems—even if they ask the question in the middle of the night. That sounds like it’s not too difficult to provide, right? The added challenge here is that aside from the fast delivery of your answers, customers have a need to feel like they are valued. Industry leaders are now faced with a stumbling block: they need to find a scalable way to help customers resolve their problems while making them feel like they’re important. 

Providing a great customer experience is tricky. Customers want nothing but the best and flawless experience whenever they communicate with a business representative. Automated services like chatbots fit the shoe just right. They aren’t perfect, that’s a given. But chatbots are programmed to understand the customer’s intent and work really fast in this element to assist them. Successful implementation of a chatbot can give a business the following benefits:

  • Save operation time 
  • Increased brand loyalty
  • Higher conversion rates
  • Establish brand voice and personality
  • Stand out from the sea of competition
  • Increased engagement and interaction 
  • Greater database to better understand users and potential clients

Investing in this new norm for customer service will help a company stay ahead of the pack and establish a great reputation without trying too hard. Knowing that customers bolt when they are unhappy with the services and are more than willing to voice their displeasure online just accentuates the need for a modern shift. 

How to Build a Bot from Scratch?

Creating a chatbot from scratch sounds intimidating at first, but they’re not. Before we dive deeper, there are a few things you need to understand first. What is a bot development framework and how does it differ from a bot platform? 

Bot Development Frameworks provides tools and prebuilt functions that take out all the manual work required in building a chatbot. It helps developers and coders write the codes faster for better application. Bot Platforms on the other hand are commonly used by beginners who are non-technical users. These are platforms where designated professionals will assist the company’s marketers when they create and maintain their chatbots.

Code-based Frameworks for Bot Development 

In order for a chatbot to work, pre-developed web apps are required. Messaging platform connectors obtain conversations from different chat mediums, NLP for message processing, and a server to ensure impenetrable communication with the API. There are several code-based frameworks that can help with bot development: API. AI, Microsoft Bot Framework, IBM’s Watson Conversation, and Ada

1. API. AI

The API.AI software ensures that the entire process of creating a bot is smooth by helping developers provide specific information that is tailored to a bot’s needs. It works on the intent, speech recognition, and context management of the bot.

API.AI is a code framework with a simple web interface. It authorizes the users to create engaging conversations by using a variety of libraries and SDKs. This includes Android, Webkit HTML5, IOS, Node.js, and Python API.

2. Microsoft Bot Framework

Microsoft Bot Framework allows the development of chatbots across several platforms such as SMS and non-Microsoft platforms. On the contrary, this framework is particularly powerful when combined with other Microsoft tools and services. Examples of Microsoft tools and services are Azure Bot Service, BotBuilder, and Cognitive Services. 

Bot Platform to build your bot 

1. IBM’s Watson Conversation 

What makes Watson Conversation special is that it utilizes machine learning to help train and build conversation interactions between the bot and the customer. The Watson Conversation programs the bot to reply to the customer in a way that resembles a natural human conversation. 

2. Ada

Ada helps companies build simple to complex chatbots with no coding necessary. Any business can automate its customer services with an illustrative builder that makes it easy to create structured workflows for any department.


How to Build a Chatbot in Python

To build a chatbot in Python, all the necessary variables you want to use and embed in your chatbot should be ready and imported. 

Libraries & Data Needed 

1. — the code that enables the users to easily interact with the bot.
2. — the code for reading in the natural language and turns it into a training set. 
3. classes.pkl — a list of the different types and categories of responses. 
4. words.pkl — a list where different words that are part of our vocabulary is stored and that could be used for the bots pattern recognition. 
5. intents.json — the data file that has predefined responses and patterns.
chatbot_model.h5 — the trained model that contains all the information about the specific model.

Step-by-step Procedure 

1. Import all the files and variables

First and foremost, importing the library into your system is a crucial step when creating a chatbot in Python. Import all the necessary packages and files for your chatbot and separate the variables you will use in this Python project.

 2. Post-processing data

Working with text-driven data requires preprocessing before making a machine learning model. There are several operating requirements it has to go through to effectively preprocess the data. The most basic post-processing used in text data is called “Tokenizing.” This is the process of breaking the entire text into smaller parts or words. 

3. Build a testing data 

Creating the testing data will provide all the input and output patterns you need. The purpose of creating and testing data is that it narrows everything down to the simplest form possible. It will save a lot of time and prevent small errors from happening when the words go through the process of machine learning.

4 .Building the model

Once all the training data is ready, you can start the process of building the deep learning model from keras called ‘Sequential.’ The Sequential model has 3 layers in particular. The first layer has 128 neurons, the second one has 64, and the last one has the number of intents as the final number of neurons. 

5. Anticipate the response (Graphical User Interface) 

Before anything else, you need to provide the same input the way you first did when you were training the bot. This will create a few functions that will carry out the preprocessing text and then predict the class.

Having all of the necessary procedures for running the GUI is your priority at this point. The clean_up_sentence() is the first function you need to use — this polishes the sentences that are inputted. The clean_up_sentence() function is then utilized in the bow() function. The bow() function takes the sentences that are polished and generates a list of words that are used for predicting their classes. After you predict the class, chances are you will get a random response from the list of intents.

6. Run the chatbot 

The final stage is testing out the bot. Two main files are needed for this stage: and 

First, you have to train the model by using the python in the terminal. If it runs smoothly and there are no errors found, this means you have successfully created your bot. To run the file, the bot will automatically pop up in the GUI window within seconds. 


Tips to Build a Chatbot for a Seamless Automated Experience 

Building a chatbot is about customizing interactions to work for your industry and business, and creating a personal connection with your customers. For example, a FinTech company will want to enable customers to transfer money and an insurance company will need a chatbot to manage claims. Here are some best practices that apply to any vertical.

How to Build a Chatbot Tip #1: Strategize Internally 

First, assign a dedicated Bot Manager. This person shouldn’t be a tech guru, rather it should be someone who works closely with your customers and is committed to making a real organizational change. Next, determine which channels you want to use. Just your website? WeChat? Native mobile app? Figure out your strategy for handing off to live agents and which platform they’ll use—messaging channels, live chat, etc, and how your chatbot integrates with your systems of record, such as Zendesk, Salesforce, or Oracle. Identify your top inquiries and begin building conversation flows and that will serve up the right answers.

Be sure to let your customers know they’re interacting with a chatbot—they like to know who they’re talking to and will be glad they don’t have to wait in line anymore. Finally, deploy your chatbot and start measuring the results. You can use analytics around things like customer behavior, resolution time, and frequent service issues to inform your teams and improve on all levels. When it’s time to optimize, you’ll be able to make better decisions with the data and insights you’ve collected from every single automated customer conversation.

How to Build a Chatbot Tip #2: Personalize Interactions

You can really raise your CSAT when your chatbot supports intelligent dialogue and solves bigger problems, especially if you begin to gather personal information about the people that are reaching out. Collect contact details or account information in the early stages of interactions. This makes the conversation feel more personal and allows the chatbot to perform heftier tasks. With personal details, chatbots can help customers do things like book flights, get better project management software, or find a bag that matches their shoes.

You can also tailor your levels of support according to the customer’s account, such as membership tier or account balance. And you can empower agents with relevant information and past conversations the second they connect with the customer.

Don’t forget, you can also leverage historical data that you already have to provide customers with a personalized message immediately. This works great in all industries, but as a great example—if a shopper abandoned their cart the last time they visited your site, the chatbot can let them know that those items are on sale or are still available for purchase.

How to Build a Chatbot Tip #3: Grow Revenue

Make your chatbot a member of your sales team by giving them the smarts and the information to meet consumers at the beginning of their journey and guide them to purchase. Track which interactions lead to sales or upgrades, then find ways to up-sell and cross-sell to customers based on their responses or account info. Start with your top five sales interactions and focus on incorporating them into the right conversation flows. As much as possible, tailor your pitches to the customer’s profile and what they’ve told your chatbot.

What is a brand interaction platform?

And this leads us to an important question: what is a brand interaction platform? Perhaps a more pertinent question would be: what is the difference between a chatbot, conversational AI, and a brand interaction platform?

You’ve likely heard the terms chatbot and conversational AI used interchangeably. A chatbot uses AI to simulate one-on-one conversations with people and automate answers as if they are talking to human. Conversational AI can be applied to chatbots to power a deeper understanding of language and automate meaningful conversations.

Automation through chatbots and related technology help companies solve their most basic brand interactions, but they lack the breadth and intelligence to move beyond reactive support to deliver cross-functional interactions that anticipate individual needs and intentions across every touchpoint in the customer journey.

Most chatbots are siloed by department, meaning that they are serving a specific need or use case. A brand interaction platform, on the other hand, unites departments — from support, to sales and marketing, to product — to offer consistent CX across the entire customer lifecycle.

A brand interaction platform is a singular automation platform for all your brand interactions, building and connecting across teams and systems. It gives brands the power to:

  • Provide a consistent, connected brand experience across each customer and employee touchpoint
  • Apply greater personalization based on a more holistic view of the customer journey and profile
  • Access richer, more complete interaction and data insights in a single location

This unified approach to support and improvement across the organization introduces the modern enterprise to a new world of possibility. Brands can increase engagement, conversion, and revenue by accelerating the qualification rate of leads, improving customer retention and loyalty, and reducing cost per interaction.

Delivering these kinds of valuable brand interactions at scale has traditionally required tons of money and staff. And this leads to missed opportunities to drive incremental revenue and better experiences for customers and employees. A brand interaction platform covers the broad spectrum of CX, and it can be used to automate the same interactions needed to improve EX.

Brands can simplify and streamline internal inquiries, tasks, and processes to boost employee productivity by automating repetitive tasks. Resolving technical or logistical work-adjacent issues like accessing technology or managing benefits allows internal teams to spend more time on valuable work. Automating tasks that typically slow employees down reduces friction among teams and increases job satisfaction.

With a brand interaction platform, you can elevate the brand experience for all. Nearly every brand interaction can be improved through automation, and each customer and employee can enjoy a VIP experience that’s personalized, proactive and accessible — no matter who they are, what channel they prefer, or what language they speak.

How to Build a Bot From Scratch for Non-Technical Users 

After reading about the code-based frameworks, you might have opened a new tab and typed in ‘How to build a chatbot without coding’ in Google’s search bar. We get it. It can be too much for non-technical users, but there are chatbot platform builders that can help you create a chatbot without the complexity of codes

No-code Chatbot Builders like Ada

Ada gives its users an easy and code-less outline to follow when building your bot for the first time. The benefit of using Ada is that you can arrange conversations with AI. It automatically learns from the customers’ moods and emotions, reads typos, and understands simple to complex jargon. Ada's advanced features even support 100+ languages, and personalized greetings and replies that will provide premium services to each client—your customers wouldn’t even feel like they’re talking to a robot. 

If you want to book a demo, click here

How to Build a Chatbot in Facebook

It’s no surprise that Facebook Messenger is one of the most used communication platforms in the whole world. With 1.3 billion active users monthly, there is no question that chatbots are definitely a useful tool for their platform.

With the right chatbot in action, every customer interaction will be automated. All the users have to do is to click the “Message” button on a Facebook page and then the Messenger tool or the chatbot is automatically launched. This quick and easy tool allows the user to type in a question and begin chatting with a bot in an instant. 

Depending on how complex the chatbot is and the type of conversations it's intended to have, the best way to install and develop it is by seeking external help. A chatbot that is equipped with pre-programmed responses is commonly developed faster than chatbots that utilize natural language processing as the focal point of their intent.

For a more in-depth and step-by-step explanation on how to build and install a Facebook Chatbot, Facebook has a quick start guide for developers.


6 tips for Building your First Chatbot

Building a simple chatbot from scratch opens up a variety of useful skills you can use for data science and general programming. Now that you’re almost ready to go, here are 6 useful tips to consider when you’ve finally made your first chatbot:

1. Make sure your chatbot doesn’t sound robotic

Just because chatbots are robots, doesn’t mean it should sound like one. Chatbots are smarter and designed to perform much more than the typical *I-am-a-robot* tone. 

Natural language processing will program your chatbot to start and end a natural conversation flow that sounds like they’re just conversing with an old friend. By making sure that your chatbot doesn’t sound robotic, it makes asking questions and understanding the solution easier for the customers.

2. Build. Train. Deploy. Measure. 

Build: It’s a no-brainer that building the chatbot is the first initial step. Once you have built your chatbot, the next important step in the development process. What is the bot for? What are the types of conversations it’s intended to face? What are the types of responses are they supposed to give? You can implement these general questions whenever you train the bot to have real human interactions.

Train: The process of training your chatbot is easy and without a doubt, repetitive. The process depends on how advanced you want your chatbot to be. Is it designed to answer simple and common questions, or is it designed for complex and spontaneous questions? The answer to these questions will be the basis of how much you need to train your bot. Just be sure that you really take time to feed the bots’ every need in order for it to be effective. 

Deploy: Now that it’s trained and ready to go, you can finally deploy it to action. You can use different platforms like Facebook, your company’s webpage, or other mediums that you can show it off. Once it’s deployed, customers can start asking questions about your company, the products you sell, and the services you provide. Basically, anything the bot has been trained to respond to.

Measure: This final step is to measure and track the bot once it has several interactions with people. You can even follow up on your customers and ask them how their experience with the chatbot went. It’s important to get their feedback since the chatbot is technically designed to serve them. 

If you get negative feedback, that’s normal. Don’t beat yourself up for it. You can always pinpoint every flaw and start improving it to increase the chatbot’s success rate in the future. 

3. If you’re building from scratch or using a free chatbot plan for a time-consuming process

When you create a chatbot, you have to consider the amount of time you need to invest in training and analyzing the flow of their conversations. Planning the content you put in also takes a lot of time. From a technical point of view, you’re training a robot to think and talk like a human being. Just with this idea alone, it already sounds like a lot of work. 

4. Give the chatbot a real voice

Customers absolutely hate it when the responses are ‘too robotic’. Imagine having to repeat the same question over and over again but the chatbot’s responses are too scripted and unhelpful. Make sure that when you launch your chatbot, it has a realistic voice and character that can easily adapt to the environment it’s in. 

5. Understand the most common requests from customers 

Having a thorough understanding of your customer’s common requests is key to achieving your business goals. Whether you’re trying to optimize their customer service experience or by creating more engaging content for your chatbot to say, knowing your customers better than anyone is still king.

6. Work with your CX team - or even put them in charge 

The CX team’s main purpose is to co-create new and exciting experiences with customers. They centralize and analyze the customers' feedback and record valuable data. They are the best team to take charge of chatbots because they identify complex metrics that allow them to track and monitor whether or not these metrics positively impact the business outcomes. It makes sense that they are the ones leading the content and development of your chatbot.