When we hear chatbots getting popular day by day and seeing every company choosing chatbots, it seems ridiculous to believe that any day smart chatbots will replace websites and apps.
But, there is quite one reason to believe so, be it simple bots usage, no apps needed, no memory space needed, an equivalent platform works for tons of bots then on.
85% of customer interactions will be managed without a Person by 2022 by using Smart chatbots.
Few days ago, Facebook allowed third-party developers, and Facebook Messenger has become one of the foremost popular platforms for bots. And, seeing the recognition of Messenger, it seems one among the foremost popular ways to attach to users.
‘Messaging is one of the few things that people do more than social networking’ – Mark Zuckerberg, Facebook CEO
Before we start looking in the ways to develop a smart bot, I would just like to say that the current underlying chatbot technology is incorporating Machine Learning and Artificial Intelligence in the bot.
Let’s begin the smart bot development.
Let’s look at an example scenario and build a smart bot for it: purchasing a house.
Before using a chatbot to buy a house, let’s look at some ways to buy a house.
One way is to go to a broker or agent in person, meet him, go with him to see some houses and then choose one house.
Second option is to go to an online website and then search for the houses, apply some filters and then look for the desired house and make the decision. A good approach but still too much dependency on the available filters which are too mainstream. Suppose I want to have a house near a school as I have kids, I cannot exactly look for it. No such filters available.
The third option is using a chatbot and tell more about yourself to the chatbot directly, save yourself with the hassle of checking all the filters and then selecting all of them. All things can be done in a single or a couple of sentences. But the chatbot must be smart enough to do so.
Now the problem arises: How to make the chatbot smart enough?
To know more about the chatbot, their types, and how they work, please have a look at this article.
There are two ways to make a chatbot interact with the user.
- Sequential Bot
- Conversational Bot
A sequential bot is a bot where the bot asks a series of questions and the user answers them. It works good for some use cases but it fails when there is a change of mind by the user. The bot cannot understands things and the user has to start over.
Let’s see how the House Booking Bot will work in sequential manner.
Here the bot seems to work super good as it asking what needs to be done and once user provides all the information, it works great and searches the results based on the parameters and replies back with all the answers.
Now, let’s look at another scenario. If the user writes more parameters at the start and the bot has to go all the way down to again ask the same questions again in the same sequence to collect all the desired parameters.
As we can clearly see, the smart chatbot is following a sequence and is asking the questions in the same manner again irrespective of the number of parameters provided by the user.
A conversational bot behaves more in a human similar way. This works even at those times too when a user changes his mind in between a conversation as we as humans, we do this very often.
The main advantage of this approach is to make humans more comfortable saying things. But the question is how to implement such a thing. The answer is RAN.
But how RAN works ?
In RAN (Random Access Navigation), the major concept is allowing the user to feel independent and converse freely as a human does in real life. Also, the user can modify/change his views anytime. This is important as this is how things work in real life.
In the above we can see user just provides only 2 entities and not the third parameter. Now if we think as a human, a human will ask only one question for the missing parameter and not all the above questions again. Here for example, the question to be asked is What is the budget ? And the smart chatbots does the same.
Also, we can see that the user changes it’s location from Copenhagen to New Delhi and the smart chatbot updates the parameter of location from Copenhagen to New Delhi in it’s final search.
As we can see above the user has provided all the 3 entities: Copenhagen -> Location, 3 rooms-> Number of rooms and 500000 Euro -> Budget. And as simple as that the smart chatbot recognizes all the parameters and then shows the outputs. Hassle-free thing.
Similarly, if the user provides one parameter or more as above, the smart chatbot will only ask relevant missing questions.