Facebook is among the leading networks for consumers to directly interact with their choice of brand. Over a billion people in a month use this platform to directly get in touch with businesses. A survey reveals that 56% of consumers would prefer any and all form of grievances to be addressed and solved via chat rather than telephonically. Furthermore, consumers expect the response time to be less than 5 minutes per grievance.


If you are looking for another paid platform, Beep Boop may be your next stop. It is a hosting platform that is designed for developers looking to make apps for Facebook Messenger and Slack specifically. First, set up your code using Github, the popular version control repository and Internet hosting service, then input it into the Beep Boop platform to link it with your Facebook Messenger or Slack application. The bots will then be able to interact with your customers with real-time chat and messaging.

This is where most applications of NLP struggle, and not just chatbots. Any system or application that relies upon a machine’s ability to parse human speech is likely to struggle with the complexities inherent in elements of speech such as metaphors and similes. Despite these considerable limitations, chatbots are becoming increasingly sophisticated, responsive, and more “natural.”
Interface designers have come to appreciate that humans' readiness to interpret computer output as genuinely conversational—even when it is actually based on rather simple pattern-matching—can be exploited for useful purposes. Most people prefer to engage with programs that are human-like, and this gives chatbot-style techniques a potentially useful role in interactive systems that need to elicit information from users, as long as that information is relatively straightforward and falls into predictable categories. Thus, for example, online help systems can usefully employ chatbot techniques to identify the area of help that users require, potentially providing a "friendlier" interface than a more formal search or menu system. This sort of usage holds the prospect of moving chatbot technology from Weizenbaum's "shelf ... reserved for curios" to that marked "genuinely useful computational methods".

Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval.
[In] artificial intelligence ... machines are made to behave in wondrous ways, often sufficient to dazzle even the most experienced observer. But once a particular program is unmasked, once its inner workings are explained ... its magic crumbles away; it stands revealed as a mere collection of procedures ... The observer says to himself "I could have written that". With that thought he moves the program in question from the shelf marked "intelligent", to that reserved for curios ... The object of this paper is to cause just such a re-evaluation of the program about to be "explained". Few programs ever needed it more.[9]
“Beware though, bots have the illusion of simplicity on the front end but there are many hurdles to overcome to create a great experience. So much work to be done. Analytics, flow optimization, keeping up with ever changing platforms that have no standard. For deeper integrations and real commerce like Assist powers, you have error checking, integrations to APIs, routing and escalation to live human support, understanding NLP, no back buttons, no home button, etc etc. We have to unlearn everything we learned the past 20 years to create an amazing experience in this new browser.” — Shane Mac, CEO of Assist
Marketer’s Take: While I didn’t like being directed to a website to finalize my purchase, I understand why Spring decided on this approach given how the Messenger platform was just released. Yet, this may be a sound strategy if you’re looking to augment upselling and cross-selling opportunities or looking for deeper analytics than what Facebook Messenger is providing.
There are lots of free chatbot building tools such as the one I run that offer the ability to setup a bot and connect it to facebook. You won’t require any coding knowledge, and you can sign up using your Facebook account. Once you’ve set up a free account you can start creating your chatbot. Make sure you keep these tips in mind when building your bot:
Malicious chatbots are frequently used to fill chat rooms with spam and advertisements, by mimicking human behavior and conversations or to entice people into revealing personal information, such as bank account numbers. They are commonly found on Yahoo! Messenger, Windows Live Messenger, AOL Instant Messenger and other instant messaging protocols. There has also been a published report of a chatbot used in a fake personal ad on a dating service's website.[54]
Chatbots talk in almost every major language. Their language (Natural Language Processing, NLP) skills vary from extremely poor to very clever intelligent, helpful and funny. The same counts for their graphic design, sometimes it feels like a cartoonish character drawn by a child, and on the other hand there are photo-realistic 3D animated characters available, which are hard to distinguish from humans. And they are all referred to as ‘chatbots’. If you have a look at our chatbot gallery, you will immediately notice the difference.
In 1950, Alan Turing's famous article "Computing Machinery and Intelligence" was published,[8] which proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, sufficiently well that the judge is unable to distinguish reliably—on the basis of the conversational content alone—between the program and a real human. The notoriety of Turing's proposed test stimulated great interest in Joseph Weizenbaum's program ELIZA, published in 1966, which seemed to be able to fool users into believing that they were conversing with a real human. However Weizenbaum himself did not claim that ELIZA was genuinely intelligent, and the introduction to his paper presented it more as a debunking exercise:

Despite all efforts during almost half a century, most chatbots are still easily uncovered, but over the next decades they will definitely get smarter and finally we will distinguish human beings by them giving us silly answers as opposed to the much smarter chatbots. All of this will really start accelerating as soon as one single chatbot is smarter than one single human being. They will then be able to learn from each other, instead of learning from human beings, their knowledge will explode and they will be able to design even better learning mechanisms. In the long run, we will learn language from chatbots instead of the other way around.


Streamchat is one of the most basic chatbot tools out there. It’s meant to be used for simple automations and autoresponders, like out-of-office replies or “We’ll get back to you as soon as we can!” messages, rather than for managing a broader workflow. It’s quick to implement and easy to start with if you’re just dipping your toes into the chatbot waters.
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