Since the advent of online businesses and e-commerce, everyone has been in search of the Holy Grail which is providing the best customer experience by giving a perfectly designed user interface that is able to maximize user engagement. Longer exposures meant higher chances of sales and leads. The most recent innovation in this regard was the evolution of UX from static towards more responsive pages but soon, even that could be rendered obsolete, as a new dimension is added to this whole saga of providing the ultimate experience i.e. deep learning.
Have you ever witnessed the phenomena of you getting onto YouTube and then spending hours on end of viewing a ginormous amount of videos without ever getting bored? Why does this happen to everyone? Why do you keep finding videos that are immensely entertaining to you? Why do they always match your likes and interest? The answer lies in the fact that YouTube saves your data every time you search for and watch a video in addition to collecting data on what you watched more and then uses it to constantly provide suggestions to you on the side panel which are magically close to what interests you immensely. As you keep spending more and more time on that site, it accumulates more and more data of your preferences and becomes even more engaging than ever before.
This is just a small and primitive personification of machine learning, where instead of telling the machine what to do; it learns to execute this function on its own and customizes the user experience for every single visitor, paving the way for the immense success of the site.
Deep learning will be a more advanced form of this phenomenon whereby the machine will start working like a human brain does and “learn” to save data. Then use it wisely to predict your user preferences and what you might deem more relevant than others which will amalgamate together in an information architecture and provide wind to a new type of UX — one which is designed to maximize user attention and achieve immense engagements.
Just look around you, you buy everything based on your UX, that’s the reason you prefer to buy an iPod over a conventional mp3 player every single time, the need is the same for both but the difference in UX makes way for immense success for one of the two and provides failure to the other.
Deep learning-driven UX would provide the user with the opportunity to not think about what to buy every time it is confused, rather provide very smart guesses which would make the user light up immediately and get ready for a purchase.
UX in this form would need to be very flexible, to accommodate not just suggestions on buying and selling, but also to predict by learning itself of what the motion of the hand of the user on screen suggests. For e.g. some people are left-handed, while most apps are not designed to cater to that, deep learning could allow UX to regenerate itself by dismantling its surface and come up with a mirror image of itself to make way for a very smart surprise to the user who will now actually feel more at ease using the app more than ever before. It could even mean that it starts arranging icons and features on the surface in a manner that is adept for its current user, taking the word “customization” to a whole new level but this time, customization wouldn’t be done by the user, it would be predetermined for him/her and be the same as it would have been if he/she has it done him/herself.
The user, who up till now, had to do everything itself, will now have things done in a far more efficient manner, even better than what it could have itself, but still mind-bogglingly close to its preference as the design is now customized after being “informed by data”, so whenever the user gets an app for a certain use, it is sure to get hooked on to one immediately as it minimizes effort but still gives immense satisfaction.
Deep learning is still in its early stages, but the disruption, even now, has the potential to beget immense impact on how business is conducted online but only if it is coupled with greater UX by developing a juxtaposed relationship between these two frontiers of customer engagement.