Ever used a chatbot? I bet you have—probably ordered pizza or booked services, or paid bills. Or contacted customer support to get some help. Everything I’ve mentioned could be used in well-known messengers like Facebook Messenger and WhatsApp because of chatbots.
Chatbots are very convenient for users and very profitable for companies. Bots help with automating routine tasks like customer support and bill payment. Companies with enough money—Starbucks, Amazon, Facebook—have already integrated chatbots into their products to offer their customers a better experience.
In this guide, I’m not explaining what a chatbot is and how to use it—I suppose you already know that. Instead, I’m talking about chatbots types and what it takes to build one.
Let’s get started.
How Exactly Do Chatbots Work?
There are two types of chatbots—rule-based and AI bots.
Rule-based bots (also called decision-tree bots) use a series of pre-set rules. They are built on an ‘if/then‘ basis which makes them simpler to train.
There’s a pre-defined list of questions users ask—and an instruction for the bot on how to answer these questions. For example, if customers ask you the same question all the time, you can create a chatbot that answers FAQ.
That means such bots only solve problems and complete requests they’re familiar with.
Rule-based bots work well for small-sized businesses. They’re:
- Cheaper & faster to develop
- Quite secure
- Can be integrated with old software
- You can use media files with rule-based bots
But rule-based chatbots can’t read users’ intentions. That’s what AI chatbots are for. AI-based bots use machine learning (ML) to understand the message, then respond to it.
AI-based chatbots analyze users’ moods to provide better feedback. Just like with any ML-powered software, you need to spend some time on training to get great results.
- Analyze data and learn from it
- Understand human behavior
- Respond in a few languages
- Make decisions
Rule-based chatbots require a list of interactions devs set up to server (or back end) part of the UI. Such chatbots usually ask users to choose something from a list. For instance, when you’re buying clothes, a rule-based bot will ask you to pick the type or color.
AI bots need time for training and a developer who will train them. As they’re powered by AI and ML, such chatbots carefully analyze the information and then respond to users.
That’s why their development costs more compared to rule-based chatbots. Still, they’re great for large companies who receive lots of different requests—and have lots of resources.
No matter what type of chatbot you choose, their architecture is very alike:
How to Create a Custom Chatbot in 5 Steps
We already know how chatbots work and what types of chatbots are there. Now, let’s finally see how to build your own chatbot for business.
Step #1. Outline the Users’ Needs
You’re making a chatbot for offering a better user experience, not because it’s a trendy topic. So the first thing is to figure out how people can benefit from your bot.
For example, bots are becoming more and more popular in the banking industry. Users don’t need to launch the banking app or make a call each time they want to check their card balance. They can do it in their most-loved messenger app instead.
Same for chatbots in healthcare that guide and assist people.
Or food delivery bots that also collect comments and send couriers’ geolocation.
Step #2. Pick Platforms
Chatbots are quite flexible: you can create bots that run on many platforms— websites, iOS apps, Android apps, etc.
But not just for any platform—choose the most common ones for your customers.
For example, if your users live in Eastern Europe, they probably like Telegram more than Facebook Messenger. But if they live in the USA, Facebook will do a better job.
Step #3. Look for Developers
If you’re not a programmer or a Python-lover, you’ll need some help.
Actually, there are lots of bot-constructors you can find online. And If you need something very basic, they’ll do their job.
But if you’re making a chatbot for business and want it to work well, it’s better to look for a developer. A poorly designed chatbot will rather harm your company than bring something good to it.
You may cooperate with a freelance developer or choose programming outsourcing to cut down the expenses.
Try looking for the team on websites like:
Step #4. Discuss the Technologies
We can divide all the tools into two groups—online constructors (where anyone can make a chatbot) and frameworks (for people with a programming background).
They offer to create chatbots that work on websites and in popular messengers, answer FAQ (Microsoft’s QnA Maker), or AI-based chatbots (Botsify).
These platforms are relevant if you want to make something simple. For more complicated stuff, you or your dev team needs to use more complex technologies like frameworks.
The most popular ones are:
- Wit.ai. Allows making voice and text-based bots for different messaging platforms. The framework uses ML algorithms to improve after interacting with people. Currently, Wit.AI can be used by Ruby, Python, and Node.js developers.
- Microsoft Bot Framework. Allows creating chatbots that listen and talk to users. The frameworks support many platforms, including popular Slack, Skype, Facebook Messenger.
- BotKit. It’s a part of the Microsoft Bot Framework, used for smart business chatbots. BotKit’s functionality can be extended by middleware—libraries and plugins that’ll help you add features like language processing or statistics.
That’s it! But your work doesn’t end up with deploying the chatbot. Next step in the maintenance—you should monitor how your chatbot performs, analyze the results, and improve your bot from time to time.
As for the costs, it depends on what chatbot you’re going to use. For example, you can build the bot at no cost with services like Chatfuel and Botsify. But they have some strict limitations on the number of users.
As for custom development, it’s better to negotiate the price with the dev team. The costs always depend on functionality. A simple rule-based chatbot will be less expensive compared to an AI-enhanced one.