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Five Steps To Building an AI Chatbot That Drives Value and Converts

Chatbots may seem like a brand new thing, but the fact is – they’ve been around for quite a while before going mainstream. The first chatbot called Eliza was built in (taking a deep breath)…1966. Admittedly, since then, chatbots have become advanced to the point when one can even confuse them with humans: they can imitate human speech, ask questions, self-learn, and derive insights from data. Chatbots are everywhere; by 2020, Gartner predicts, they will account for 85% of all conversations with customers. Gartner also believes that soon we will have more conversations with chatbots than with our spouses, which seems like a quite realistic prediction, come to think of it.

So, technically speaking, what is a chatbot?

Techopedia defines it as “an artificial intelligence (AI) program that simulates interactive human conversation by using key pre-calculated user phrases and auditory or text-based signals.”

Does that mean a chatbot is, essentially, an app?

With apps like Siri manifesting distinct chatbot features, it’s easy to believe there is no difference. In a way, they are similar, because a chatbot is intelligent software. Yet, apps perform a plethora of functions, while chatbots handle simple rule-based tasks like answering customer questions. Admittedly, some of the advanced chatbots are way more complex, but their main functionality revolves around a limited set of functions. Chatbots aren’t likely to replace apps; they are an addition, not an alternative.

Chatbots also bear a certain resemblance to live chats – those dialogue boxes at the right side of your computer or mobile device screen appearing when you browse websites. There is a difference, though – when using a live chat, you are actually talking to a person. Out there, there’s an assistant waiting to help you and answer your questions. On the downside, most human assistants aren’t available 24/7.

A chatbot is, basically, a robot – not in a physical sense, but in a way that it uses algorithms (including machine learning and deep learning) to communicate, process, and deliver information. Hence, we may define a chatbot as an AI-based service which may integrate into a website, a social media page, or an application to deliver messages or to perform more complex functions.

In terms of functionality, there are two categories of chatbots: transactional chatbots and conversational chatbots.

Conversational vs Transactional Chatbots

A conversational chatbot uses an already available data (like, for example, a set of frequently asked questions and corresponding answers) and aims to improve customer service by offering relevant replies. In this case, AI chatbot development would involve training Machine Learning algorithm to perform a number of specific actions: extracting intents and related information like company names and phone numbers, matching answers to replies and building a feedback system for testing and evaluating it.

A transactional chatbot delivers a particular service and offers a customer something brand new. In this case, a chatbot has to obtain the necessary data. For example, a restaurant recommendation service should not only be aware of where customers live to suggest best options in their area, but it should also know how often they eat out, their food preferences, etc. When building AI chatbots like this one, the architecture of data retrieval and connecting it to different platforms is essential.

How do chatbots perceive and process human speech?

They use NLU (Natural Language Understanding) to comprehend human language with its mispronunciations, inaccuracies, colloquial expressions and slang, and NLG (Natural Language Generation) techniques to produce responses. Both methods go under an umbrella term NLP (Natural Language Processing). Chatbots are still not good at understanding subtleties like the slight variations in meaning and tone of written speech, but they’re getting better.

Steps to Making An Awesome AI Chatbot

Although compared to apps, chatbots only perform a limited set of functions, AI chatbot development is a complicated process, comprising the following steps:

Step 1. Understand your chatbot purpose

This may seem too obvious, but it’s critical to understand the goals you want to accomplish and the value you want a chatbot to add to your business. The worst thing you can do is start building a chatbot because everyone else seems to use them or out of fear to end up among laggards who fail to adopt new technology. The range of chatbot application is broad: like Siri, they can imitate human voice when talking to customers, they self-learn to enhance communication, integrate with CRM systems, conduct direct sales, etc.

Step 2. Determine your chatbot type

Knowing what you expect your chatbot to do will help you determine its type. Developing transactional and conversational chatbots will require different approaches. After you have determined your chatbot type, define the technologies you will use to build it, and estimated the resources you will need to accomplish it. For example, if you want your chatbot to integrate with a CRM system, the task gets more time-consuming and complicated.

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This step also involves determining the channel your chatbot will use for communication. Depending on a chatbot type, its target audience, and its preference, the channels may include apps, websites, or social media.

Step 3. Program your chatbot

As easy as it may sound, this is where we get to the most complex part. The steps for AI chatbot development include:

  • Collecting and preparing data;
  • Analyzing it for intents, and, if needed, for building answer systems;
  • Designing user interface (will depend on target channels);
  • Analyzing entities in questions and answers;
  • Building chatbot architecture;
  • Coding the chatbot.

Step 4. Test your chatbot

As difficult as it may be to build AI chatbots, testing them is a no less complicated process. On an elementary level, a test should evaluate the chatbots ability to follow a conversation. Before you start, assemble a library of conversation patterns consisting of both “happy” and “unhappy” scenarios and begin with the most frequent ones. Investigate and improve if anything goes wrong.

Testing a chatbot manually is only doable when a chatbot is very simplistic and basic. More complex chatbots require automated testing – to evaluate how it works. There are automated tools for testing chatbots: you can use an open-source TestMyBot service.

Step 5. Release your chatbot

As you plan your chatbot release, you have to ask yourself several questions. Do you want to generate word of mouth with your chatbot release and hog the media headlines, or would you rather lie doggo and wait for your chatbot product to be as well-polished and fine-tuned as possible? One way or the other, you will have to think of a chatbot release strategy and have your PR and marketing collateral ready if you want to make a loud announcement.

A release date is a challenging moment in itself. It is nearly impossible to release a perfectly flawless chatbot even before multiple rounds of tests. Expect some issues to be revealed on or shortly after the release date and take action.

As customer surveys indicate, people, generally, tend to like chatbots. They are futuristic and cool, fun, and entertaining; they alleviate the stress of human interactions and save time by providing quick answers to questions.

A rarely mentioned downside to chatbots (especially those built with free tools) is their limitations: they lack spontaneity and don’t get sarcasm, they are too simplistic and tend to follow the same routine. Instead of adding a competitive edge to your business, you risk boring your customers to frustration. To stand out in the crowd, a chatbot has to be more advanced and leverage different AI technologies.

Although the majority of DIY/online chatbot builders promise you can build a chatbot within a couple of hours without any coding experience, any programmer will tell you it’s impossible to create an efficient chatbot without coding. If you’re looking to introduce an AI chatbot that will add business value, don’t save on a DIY approach.

On the other hand, chatbots, especially CRM-integrated and built using ML and NLP, tend to get so costly, it almost contradicts their purpose, which is, ultimately, to reduce expenses. For most companies, AI development isn’t their core function, and they lack the in-house expertise to build a useful chatbot. Outsourcing chatbot development to a reliable company offshore is a realistic alternative. By the way, we’ve recently been recognized as a leading Eastern European AI development company and we’ve got our spot in the interactive AI landscape map by Deep Knowledge Analytics!

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