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Its - the chatbot hype is gone. What left and what makes sense? Get an overview of the opensource and Saas chatbot landscape and follow a complete tutorial on how to build your own chatbot with chatfuel and a backend architecture. Chatbots have been quite the trend of Several swiss companies have launched their chatbot services, with mixed experiences. Experts expected that the global chatbot market is growing from 50 Million dollars up to 1.
The main reason is of course the reduction of customer service costs. The rationale is simple, if the chatbot can handle customer requests for free, you will need less staff. Although its - and the chatbot trend might have cooled off a bit - I thought I'd still cover some of my experiences of building a chatbot for Facebook. Its purpose is suggesting a game you can play with kids. It asks you in which context you are in right now e. But before diving into the project, this article will provide you with a bit of background information on chatbots and explains why it is hard for machines to understand use first.
It will then provide an overview of different options if you want to build a chatbot: Go for an open source solution or use one of the many services available. We will finally cover an implementation of a simple chatbot with chatfuel and finish with an evaluation and lessons learned before providing an outlook. In you can expect most of them to follow through the valley of disillusionment. So what happened - you might ask? Well it turns out in the majority of cases chatbots simply couldn't live up the hype.
After all, building a chatbot which works as well as a personal assistant on the phone, turned out to be as hards as making a machine pass the Turing test. So why is it so hard to build a chatbot that understands us? After all we have seen huge improvements in language oriented machine learning tasks. For example Google was able to launch a new translation product whose product lead Barak Turovsky is described as: This big leap forward in translation in , was mainly based on the use of neural networks that allowed to consolidate translation efforts in one big system: I suspect that a lot of people saw this leap forward as a sign that the computer finally understands us.
Unfortunately this is still not the case yet. While neural networks have learned to rather translate sentence by sentence and not word by word and map whole sentences from one language to another their task was never, to actually understand what we mean by our words.
So right now we are at the point where we can use RNNs to mimick human language extremely well, but still not really understanding it. Until then, they are to a certain extent able to distinguish the named entities e.
In this task they are rather still limited to cookie cutter phrases like "What time is it in Beijing? While natural language understanding will advance rapidly for sure, for chatbots it turns out to be a good idea to bear two things in mind:.
That is why in this blog post I have chosen to build one of these "dumb" chatbots which is using a very narrow context and make heavy use of quick replies to get the user to navigate through a big solution space in our case a database of games.
By trying something new, as a developer you want to give the open source solutions a try first. In this case you are particularly lucky, since there are quite a few solutions available. We have been trying to list options for chatbot frameworks under http: Let me show you three:. There are options like P-Brain , that uses node and bootstrap and roll the chatbot logic and the visual representation into one handy framework that you can use in your browser.
Botpress offers you a very developer friendly and modular e. The good thing about these open source frameworks is that as a developer or business owner you are in control: You control the data and the logic and you are flexible on where to integrate the solution and might even save some money. A big benefit is also that all of those solutions can easily be connected to a number of Platforms such as Slack , Jabber , Twilio , Facebook Messenger or generic web apps of course.
The latest being actually quite a promising way of reducing complexity on websites thinking of those big menu bars, with millions of sub-menus or offering a smart and conversational search option. If you have been living under a rock, you might expect some cool startups in the chatbots as a service area. But the reality is that we are probably years to late for this game. So as usual the big 5 Facebook, Amazon, Microsoft, Google and IBM have bought the most promising candidates on the market and are now competing for your attention.
As a developer that might not be the worst thing, since you have a number of options to choose from. So here they are:. After reading all of these options and maybe after browsing through the fancy feature descriptions, you might be intimidated thinking: Chatbots are expensive, or that sounds really complicated. Luckily you don't have to be. It's fairly easy to get started. For this blog post I have picked none of the options above, but went with chatfuel , because it makes starting so much fun.
Chatfuel is a chatbot solution that work best for Facebook with their messenger service. Yes it's mainly for Facebook, but you can also integrate it on your website, if you feel like it, and after all you can potentially reach over 1.
In simple cases you don't even need to be able to program to create an easy chatbot. All you need is to login for Facebook to authenticate chatfuel.
Then the dashboard shows up and you can create your first chatbot. Generally among things like; being able to analyze your chatbots performance, trigger promotions, broadcast messages to all users or setup additional attributes for your users, ; the most important part is the conversation flow modeling.
That's the part where you connect pre-written text blocks with user inputs and decide which text blocks should follow, based on the response. Usually you start with a welcome message, greeting the user and then sending him to a "block". A block is an area filled with different additional questions which the user has to answer.
When I say "has" to answer you already see where this is going. Usually users follow a predefined path through a conversation with having only a few options to "explore" a conversational space. This is possible though, because you can always define fuzzy trigger words or whole sentences such as "I am done here.
Generally it makes sense to draft the conversation to have a general idea how to structure it. It might seem superfluous at first but you might get lost quickly otherwise. So in our case there are basically two questions that we want to ask the user:. As you can see in the image above, the user has been asked where he wants to play games. Based on his response, which is captured by so-called "quick replies", he is sent to another part of the conversation.
Both responses are saved to user attributes that we can use to send to our server to trigger a query on our database of games now. So you might notice,that the combination of "Kids birthday" games with the type "Solo-Game", does not make much sense for example unless it's a very lonesome birthday: You can imagine, that in a more complex scenario, you might encounter more of such cases, where you have to take care of the conversation flow manually.
Encoding your "data" in the chatbot service is a bad idea I tried it in the beginning. Maintaining, updating and migrating your data will just be a horrible nightmare. In our case, I've chosen to use a small sinatra micro-framework, that offers two routes. When the user has answered both questions we are sending his response to the server via a simple GET request.
On the server we simply query the database for games matching the criteria and respond with a JSON object that chatfuel is able to understand. In this case, we are building a gallery object out of 10 random matching games, that the user can flip through.
Once the user has selected a game, we are showing a matching image and the game description to him. Using the JSON API we can very conveniently also supply more messages to the user, in this case the simple question is, if he wants another game or wants to end his conversation. Interesting fact to notice is, that we can point the user to a "block" in chatfuel directly, where the conversation is picked up.
In our case the block "configure" starts by asking the user again the same two questions , allowing to find other games. Of course after deploying your code to the server in my case I've simply used heroku you are ready to go. The good thing about chat fuel is, that all changes to the conversation flow are saved quickly and you can directly test your bot in Facebook messenger. From a testing perspective its rather tedious to write automated tests, that guarantee the functionality of your chatbot, since this might means to go down every conversational path and making sure that the responses make sense.
Once you are happy with the results, you can interact directly with your bot, by addressing him with his name that you have defined in chatfuel in messenger.
From here, you might as well create a Facebook page or community and integrate the communication with your chatbot prominently in the header of the page. Chatfuel and Facebook are offering quite a bouquet of analytics for your chatbot application. Beyond simple metrics, like the total number of users, the number of new users and general user activity, you can also compute user retention.
Generally you want users to come back and interact with your bot. If they are not coming back, something might be wrong with the user experience. To investigate such problems chatfuel offers a way to see user inputs that have not been recognized by the system or shows which buttons or blocks have been used the most. Similar to a website, where a high percentage of users might e.
Repetitively typing responses, or having to think of responses that are understood by the bot, might become a burden for the user too. In general, I think providing a smooth user experience is a very thin and tricky line to walk. I've only seen a few examples where this is done right. I personally think, a bot can shine in scenarios especially, where you want users to navigate through a vast and complex solution space, while not overwhelming them with the variety of options.
After all, chatting with a chatbot, should not feel like filling out a long form. Trying to predict the success of chatbots in different scenarios, I think they will be made use of in customer service heavily.
They might either be used to dispatch the customers as a way of 0-level support or even be able to answer some very easy and popular problems right at the start of a conversation maybe to reduce the workload of the staff.