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Chatbots are ruling the marketing automation game. 

Studies show that more than 71% of businesses are planning to adopt chatbots in their businesses. 

And although they are increasingly gaining popularity because of their capabilities of streamlining core business processes, it is still their easy buildup that takes the cake. 

However, to truly leverage the chatbot technology, knowing the basics isn’t enough. 

Especially for developers. 

Because there isn’t just a single way of building chatbots. You either build them by a rule-based approach or an AI approach. 

In the former approach, the developer writes the rules for the system. Whereas with an AI approach, a massive amount of streaming data is used and the chatbot learns with each interaction. 

For an AI chatbot, there are several steps involved in between that the developers need to pay heed to. 

You also need to understand the technicalities of NLP (Natural Language Processing) engines, implementing design, adding integrations, and testing the chatbot to ensure accuracy. 

Let’s take a look at this process in detail. 

Steps to Building an AI Chatbot

Conversational Design

Before proceeding with the design, outline the goals and motives of the chatbot. Ask yourself: 

• Why are you required to deploy a chatbot? 
• What is the end goal of the chatbot?
• What NLP engine are you going to require?
• What will the flow look like? 

Answers to these questions will help you outline the conversational flow of your chatbot. They will help you understand how the chatbot will represent your brand, what tone you should adopt, what fonts you should use, and what personality to give to the chatbot. 

What makes conversational design challenging with AI chatbots is that you’re not simply making a decision tree on a bot-builder. In the case of NLP engines, you’ll have to start by defining the intents, entities, and responses. It will require you to brainstorm and get creative. 

Some of the top NLP engines in the market are:

• Dialogflow
• Luis
• IBM Watson Assistant 
• Amazon Lex
• Wit.ai

The components of an NLP engine include: 

Intents

‘Intent’ in an AI chatbot is the core problem of the user. For example, for a movie theater, some of the most common questions that a visitor might have are related to timing, location, and pricing. These form the different intents for a movie theater chatbot. 

Entities

Entities comprise the objects of the conversation. It’s what breaks the intent to extract specific pieces of information from users. For example, the entities in our example would be the name of the movies and the intent would be the timing. 

Responses

This is the response to the different intents and the output that is aimed to satisfy the user intent. For example, what will be the answer to the question “What is the movie timing for X?”, or “What is the ticket price for the movie Y?”. 

Based on your customer queries, you’ll have to identify multiple intents and add responses to each one of them. This is also where you would want to add flavor to your bot conversations. Although the replies will be automated for every user, you can ensure that the copy isn’t bland. 

You can address every user by their name and also take the help of a content writer to draft a compelling chatbot script that engages the user. 

You can also make use of the marketing data to understand the ideal visitor and decide if your bot should be formal or informal, quirky or professional, etc. These factors will ultimately give your bot a personality that resonates with the overall brand image. 

Chatbot Development 

If the NLP engines are the brain behind the chatbot, the chatbot platforms are the body. This is the process where you’ll actually be integrating the NLP inputs on a no-code bot-builder. 

You can also build a chatbot framework using programming languages. But building it on a platform will save you the hassle of coding and hasten the process. 

To build an AI chatbot on a chatbot platform, you’ll need to ensure that the chatbot pricing plan provides integration with NLP engines like Dialogflow or IBM Watson. Some of the top examples of no-code bot-builders are:

• WotNot
• Landbot
• Ada
• Ubisend 

With a visual drag-and-drop interface, You can start by developing a rule-based flow for simple questions. You can then add an NLP integration by calling an API to the respective NLP engine.

Chatbot Testing

One of the most distinguished qualities of an AI chatbot is that it will constantly learn. The engine is such that it may or may not get the responses right in the first go. It will self-learn with each interaction. This makes testing a very essential part of AI chatbot development where you can keep training the bot to improve its accuracy and fix errors. 

To improve the chatbot programming skills to better understand the customer intent, you’ll need more training data to input values that chatbots need to process. Check all the manner in which users are asking the questions that the chatbot isn’t processing. You can use this data to ensure that your chatbot provides answers in every scenario. 

You can also test the chatbot via RPA, security testing, and UFT testing or leverage tools like: 

• Botium
• Zypnos
• TestMyBot

After testing the bot, remember to implement the changes in the bot. You may come across various new training phases that ultimately have the same intent. You’ll have to include them in the bot to enhance the efficiency of AI conversations. 

Conclusion

AI chatbots are all about nailing the intricacies of human conversations and replicating them in a bot. The development of an AI chatbot becomes much easier if you have a holistic outlook of how people interact and how you can teach your AI to do so. 

Once you get that right, you have a range of options using which you can build an AI chatbot. Just take into account your business objectives and possess the basic technical know-how of constructing a chatbot and you’re good to go.

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