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What is the Best Way to Create Chatbot Programs?

The benefits of chatbots are becoming increasingly clear to many companies. Not only do they improve customer service, but they also serve to streamline internal processes. According to research conducted by Gartner, over half of all medium to large enterprises will deploy chatbot products over the next two years. Therefore, many senior management teams are thinking about how they can create chatbot programs to keep step with the competition. Often, if C-levels and IT departments are new to chatbots, this can seem like a daunting process.

However, this needn’t be the case. Put simply, there are three main approaches to creating a chatbot: coding from scratch; using natural-language understanding services; or using a DIY chatbot creation platform. Each method has its own pros and cons, depending on the team’s level of computer literacy and coding acumen. In order to develop a chatbot that fits the business’s unique needs, management and IT need to carefully analyse the options. After all, deploying the wrong technologies will almost always have counterproductive effects. In order to help professionals make the right decision for their enterprise, we’ve put together this guide to building chatbots – read on to find out more.

What type of chatbot do you need?

However, before discussing programming methods, you need to identify the type of chatbot that best suits the organisation’s needs. Overall, chatbots can be divided into two groups: rule-based chatbots and AI-powered chatbots. As is probably immediately obvious, one is far more technologically advanced than the other. However, that doesn’t necessarily mean that they’re better in all instances. Depending on the nature of your business or the purpose of your bot, there may be many good reasons to implement a rule-based chatbot.

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Essentially, rule-based chatbots will only respond to a specific set of commands. The major benefit of this is approach is that they’re far cheaper to develop. Although this may sound limiting, it is possible to build an extensive list of commands. So, for example, let’s say a business monitors their email FAQs over a long period. From here, they can collect the data and implement responses within the program. This means that mundane queries are directed away from staff, leaving them more time to get on with complex tasks. Meanwhile, if a customer has a query the bot can’t handle, the program can be set to generate notifications to let human employees know they need to take over.

In contrast, AI-powered chatbots have a higher level of complexity and intelligence. Through machine learning and natural language understanding, AI-powered chatbots ‘learn’ through processing vast amounts of data. Therefore, when a customer asks a question, the bot catalogues the response to inform future interactions. As a result, the bot can handle a multitude of operations with minimal input from the developer. It’s exciting technology, but there are downsides; development is costly and sometimes, chatbots will still get it wrong.

How to create chatbot programs

As introduced above, there are three principal approaches to building chatbots. The route you choose to go down will be informed by the type of chatbot you need; your budget; and the level of expertise at your disposal. Each method have its advantages and pitfalls, which are described in more detail below.

1-  Create a chatbot from scratch

It goes without saying, but building a chatbot from scratch is the most complex approach. However, if a company has a large and capable IT department at their disposal, creating a chatbot from scratch has several significant advantages. The first of these is that the company has total control over the technology, enabling them to manipulate the code to suit their unique needs. However, the team must have an in-depth knowledge of programming languages including Python and JavaScript. They’ll need time and resources too, including the facilities to develop their own open source NLU library and robust cloud hosting services. In essence, doing it from scratch is great – but companies need to be prepared to properly resource the project.

2. Use NLU services and code yourself

The second approach you can use to create chatbot programs is to use a natural language understanding service or chatbot API. This approach strips away some of the deeper stuff around deep learning, leaving programmers with a clean, user-friendly interface within which to code the chatbot interface. Therefore, the team will still need a good understanding of coding languages, but the super technical aspects are set to one side. Examples of NLU services include IBM’s Watson and Google’s DialogFlow. However, although the team will have the tech giants at their side, this process is still time consuming.

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3. DIY chatbot creation platforms

The third way to create chatbot programs is to invest in a do-it-yourself chatbot creation platform. This approach requires the least expertise, and in some instances, the user doesn’t even need to know how to code to get their chatbot working. Plus, platform providers can provide training and support to help you get the bot up and running. Furthermore, these platforms also have hosting included, which means they often represent very good value. However, the downsides are probably fairly obvious – you won’t be able to access the backend of your bot and you’ll have to share a significant amount of company data with a third party.

Chatbots for every business

Companies shouldn’t think twice about initiating a chatbot project – the benefits are extremely far-reaching. However, it is important to think carefully about the kind of chatbot you want to create. When considering all the variables, consider to what extent you’ll need to customise the bot; what resources are available; and the support the organisation will need to operate the program. Perhaps the principal concern is that a chatbot enables you to focus on your core business; therefore, for many companies, this will mean going down the DIY platform route. From here, the next question is finding the right partner to make sure you get maximum value from your chatbot initiative. Make sure you choose a company that provides comprehensive support, lots of features, integration options, and a robust NLU library – like Hu:toma AI.

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