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Do You Really Know How a Chatbot Works?

how a chatbot works

how-a-chatbot-works

You may understand what a chatbot does, but do you know how they actually work? Do you know how they generate new leads online, how they engage with potential customers, how they answer questions, and how they help to support businesses as they set out to achieve their goals?

If not, then this blog post is for you.

Understanding Chatbots

Different chatbots work in different ways depending on how they’re programmed so there’s no one explanation of how they work. The specific coding or technology will vary depending on the purpose of the bot.

Social media chatbots, for example, may work in a different way to a shopping chatbot as they’ll each carry out different functions. That said, there are many shared processes in chatbots stemming from the same core idea. The layers built atop that core will be dictated by the bots intended purpose.

The German computer scientist and MIT professor Joseph Weizenbaum said, “Once its inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.”

He was referring to Eliza — the first ever chatbot to be developed by his MIT team in 1966. Today, we believe that understanding the various ways that website chatbots are capable of collecting, analysing and using data is key to deriving full value from the technology.

So let’s take a look at how chatbots work…

Exploring The Different Types of Chatbot

While there are many different chatbots out there, they can all be categorised into one of three types: basic type, artificial intelligence (AI) type, and machine learning type:

● Basic Chatbots

The earliest chatbots, such as Eliza, succeeded exclusively through keyword matching, without any incorporation of artificial intelligence or machine learning.

These are rule-based bots, and the way they work is simple yet highly effective. A database of pre-scripted responses which are thought to be most likely to be relevant are generated, along with a range of associated keywords.

The responses will vary depending on what the chatbot is being used for. The bot then identifies those keywords in user-generated text, and automatically delivers a response from its script.

For example, a restaurant chatbot could be programmed to deliver the scripted response ‘what date would you like to visit?’ based on the use of keywords such as ‘reservation’ or ‘booking’ which are likely to be used by customers planning a visit.

● AI Chatbots

AI chatbots take the notion of keyword matching one step further to look at pattern matching. Some bots may use Artificial Intelligence Markup Language (AIML) which analyses groups of text, rather than individual keywords, to deliver a suitable response.

Like basic type chatbots, this is also a rule-based bot, although the breadth of possible responses is extended from pre-programmed sentences to responses that can be auto-generated from previous discussion logs which the bot analyses and utilises.

For example, a website chatbot may be able to deliver an improved customer experience by examining previous relevant interactions to deliver responses and information which has already proven to match a similar customer’s expectations.

● Machine Learning Chatbots

Machine learning chatbots are not rule-based. Instead, they dare to go off-script through natural language processing, or NLP. With NLP, chatbots transform user-generated text (or even speech) into highly structured and organised data that it can derive context from.

When concept and meaning enter the equation, chatbots are able to deliver tailored, personalised responses to individual enquiries. Over time, it is expected that NLP chatbots could very closely mimic real human interactions.

Machine learning chatbots store and analyse each interaction to ‘learn’ and become smarter with each conversation. However, NLP is still in its early stages, with some notable mistakes being made, so AI chatbots are perhaps the most popular today.

Generating Responses

Of course, there’s more to chatbots that just keywords, patterns and language processing. Just how do these systems manage to generate responses which are often be indistinguishable from the real thing?

While different chatbots will have their own ways of working, many are programmed with language inversion rules, which understand that if a user types ‘I’, the bot should respond with ‘you’, and so on.

Another very common method for programming chatbots is to use a dependency tree which organises information in such a way that it’s able to identify relationships between different ideas.

A dependency tree helps chatbots to align one idea with another, helping it to generate automated responses that are authentic and beneficial.

No matter what sort of chatbot we’re talking about, they’re all based on the same core idea, and they’re all able to effectively manage conversions without the need for human input, regardless of whether they use keyword matching, pattern matching, or machine learning.

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