From booking hotels and cabs, to delivering your groceries, to letting users set up reminders for personal responsibility, meetings, tasks or reminders, chat-based assistants are now organising our lives like our very own personal assistants. No wonder, every day they are becoming more and more adept at understanding and processing handling all sorts of possible requests a human being could make.
The chat-based assistant apps are rapidly evolving to leverage Artificial Intelligence and Natural Language Processing (NLP) capabilities in order to ensure a better customer experience. Be it locating a restaurant serving roasted prawns or mobile recharge, these apps are decoding millions of human messages every day to gather user preferences and predict requisite solutions. The latest advancement towards this direction is assistants using machine learning to even predict weekend plans for their users.
Consider for instance, it is one of those Saturdays where you haven’t planned out anything for yourself. You turn to your virtual assistant like Helpchat which could recommend you a movie to watch, a restaurant to have lunch. It then goes further to book a cab for you, suggests you a congestion free route, and even fetches special deals on your credit card. And all this can happen in the time between you shower and dress for your weekend outing.
Sounds futuristic but it hardly is, given the fact that now as mobile apps inch closer to ensuring instant gratification, they are also getting better at two very crucial aspects- prediction and personalisation. Both factors can make key contributions towards creating a virtual assistant that predicts users’ needs intelligently and gets things done.
So, how are chat based assistants getting better and better at predicting a user’s personal preferences? For this to happen, two things are being done by chat based assistants such as Helpchat. Firstly, Helpchat has tied up with businesses ranging from tech support and recharge to travel, food delivery and more, and is further opening up its platform for API integrations (technical integrations) to businesses. Alongside this, state-of-the-art machine learning and artificial intelligence techniques are drawing data from signals collected during user interactions with the app.
Bots with NLP capabilities gather this data, which includes everything from daily chores to personal preferences. The app keeps a note of everything whether you are vegetarian, if you own a pet, what phone you use and so on. Once data gathering is complete, machine-learning models like neural networks crunch data to predict where, when, and how you need help. The service layer then taps the business partners needed to meet your requirements. Just as a human assistant recognises the employer’s needs better with practice, the app too predicts and meets your needs more efficiently as your interactions with it increase.