You can talk with chatbots in many ways. The most common (and trendy) are NLP and button replies.
With NLP (Natural Language Processing) the chatbot engine tries to process the text typed by the user, extracting and processing the words inside the same text to decode the supposed “user intent”. The intent, in the case of Tiledesk native bot, is the answer for what the user was searching for. NLP is a powerful technology, because provides the user with the freedom to write as he was chatting with a real human (while there is a machine on the other end). But sometimes this freedom is not the best solution.
Buttons are an alternative way to help end users to reply to a message. Your click on the button simply sends back the button-text to the chatbot on the other side of the conversation. So, if the button contains the text “Talk to agent”, after clicking on said button the exact same text will be sent and shown in the conversation as if you typed it manually.
To better understand when and how buttons are a better solution than using NLP, we can start with a practical example.
Let’s start from our Charlie chatbot in the first tutorial. Just consider, but we’ll return on this later, that the way described in this article to render buttons works on all Tiledesk chatbots, e.g. Native chatbots, External and Dialogflow chatbots.
Now suppose that the user wants to ask something about the type of accounts supported by Quantabank. He can, for example, ask something like this: “what type of accounts do you provide?”. Let’s create the answer to this question.
Fill the questions-answer form with the following text:
what types of accounts do you provide?
__Quantabank__ actually provides the following account types: - Basic Checking Accounts. - Savings Accounts. - Interest-Bearing Checking Accounts. - Money Market Accounts. - Brokerage Accounts. What of this accounts are you interested in? (i.e _Savings account_)
Now, to fulfill the user knowledge needs, we should provide to the user additional replies for each of the proposed options. For the purposes of this example, we can just provide an answer for the “Savings account” question. To accomplish this easy task we must create a new answer. Presse the + New answer button in the chatbot Charlie toolbar (as above). Fill the fields with the following data:
A __Savings account__ is an interest-bearing deposit account held at a Quantabank. Through these accounts you pay a modest interest rate while their safety and reliability make them a great option for parking cash you want available for short-term needs. [more info](https://quantabank/accounts/savingsaccount)
Click on CREATE ANSWER. Now we can test all the questions-answers workflow in the SIMULATE VISITOR view (press SIMULATE VISITOR in the Request panel). Click on the widget and press “New conversation”. After the chatbot greetings us with “Hello”, we can type our first question:
The chatbot will reply with a list of options in the message body with the final suggestion in the message tail to type the name of one of this options to get more information. We can type “Savings account”, getting the expected result:
Very good as a result – the user got his response and he is probably satisfied with the reply – but he had to type manually “savings account”. This is an error prone task, because typing often introduce errors and, moreover, typing is tedious for the user. Why writing something when there is a better alternative? Render the accounts as a sort of options menu: the list of available account types. It’s better in this case to give up on the power of NLP and switch to buttons instead! How to get buttons for the list of accounts?
With Tiledesk it’s very easy to render buttons. Simply place an asterisks (*) on a new line, followed by a space and the text that you want to render in the button.
In our case, to render the list of accounts as buttons, we can replace the “-” in the list of the accounts with asterisks (*).
So the new answer text will become:
Question (as above)
what type of accounts do you provide?
Answer (edit by replacing “-” with “*”):
__Quantabank__ actually provides the following account types. Please choose the one you are interested in. * Basic Checking Accounts. * Savings Accounts. * Interest-Bearing Checking Accounts. * Money Market Accounts. * Brokerage Accounts.
Every element in the list of the reply message marked with an asterisk (*) will be rendered as a button.
Press UPDATE ANSWER. Now test it in the usual way, moving to Request panel and pressing SIMULATE VISITOR button. Start a new conversation pressing the “New conversation” button (or continue the old one). After the greetings message you can ask your question “what kind of accounts do you provide?”, and this will be the resulting reply:
Our list was rendered as a group of buttons, aligned on the right of the widget area. Probably our user will be more happy, having only to click and option on a menu instead of typing an entire text.
What will now happen if we click on some of those buttons? Let’s try!
Just wait one second and we’ll get the same response as the NLP case:
We’ve seen the power of buttons, and how easy it is to introduce them in our conversations.
Enjoy Tiledesk buttons!
Please feel free to send feedback about this tutorial to email@example.com. Thanks!