Predictive Analytics to Influence Mobile UI/UX

May 18, 2020 Geekopedia 0 Comment AI, Analysis, App, Applications, Artificial Intelligence, Developers, Mobile, Platforms, Predictive, Social, UI, UX

Artificial intelligence’s growing influence on mobile app development could not be more apparent in the use of predictive analytics to improve app UI/UX. The technology’s entrance into mobile is fairly new but it’s been gaining traction ever since it demonstrated its remarkable benefits to the said field.

Predictive analytics exerts a substantial influence on UI/UX development simply because it’s the specific area where most of its benefits can be derived. AI and UX also pretty much have the same goal: interpreting user behavior. Predictive analytics basically just serves as a bridge between the two.

That said, knowing the said benefits will give you a good enough overview as to why predictive analytics has this much clout on UI/UX design, and why developers should be using it to their advantage.

Why Is Predictive Analytics a Perfect Match for Mobile?

Predictive analytics is at the forefront of mobile app development precisely due to the rising role of data in the field. Patterns derived from data often improve user conversion rates significantly. How do they manage to do this? By capitalizing on the capability of most predictive analytics programs to derive data in a comprehensive and in-depth way, app owners and companies can enjoy unprecedented benefits in shaping their apps according to how users need and want them.

How in-depth and comprehensive? Well, think of being able to have all the data you need regarding user behavior and even actual sales with close to unlimited scope. Then, imagine being able to rely on an advanced algorithm to analyze it for you – learn which products will suit the user best or find out the products that are generating the most buzz.

Further benefits of predictive analytics to mobile app development include:

Customers can easily be segmented according to their demographics or age groups.
Regression analysis can be conducted, which basically means discovering possible correlations between specific transactions and other variables in your target audience.
It allows you to monitor all the customers’ transactions to heighten security and to keep track of suspicious activity.
Ultimately, it mitigates the risk of an app, too, which is nothing short of an invaluable advantage to have.

Conversion rates serve as one of the main end-goals of mobile apps for, in the end, it’s what determines their success. Is it any wonder, then, that predictive analytics has come to a point that it can already exert this much influence on app development? Also, is it really at all surprising that the application of predictive analytics is currently largely centered on UI/UX design?

It’s the main aspect of any mobile app that determines user flow, how smoothly it will fulfill the user’s needs. This sole feature could mean the difference between a user converting or not.

Predictive Analytics’s Role in Shaping the Mobile UI/UX of the Future

Much like the user’s journey through any type of UI/UX of an app, predictive analytics’s sojourn into the mobile industry has been met with open arms. Even now, most of the very popular apps available at present are already widely using AI in implementing predictive analysis in their respective platforms. Their developer’s goal, of course, is to make the UI/UX as responsive as possible to their users.

At the heart of AI is personalization. And the way predictive analytics is engineering UI/UX apparently leans more toward that notion. In turn, any mobile app that mainly uses this technology for UI/UX design will inevitably start looking and acting in an entirely novel – in short, an app that is no longer bound to the common attribution of “tool”.

Now with the help of predictive analytics, apps (whether they are for business or any other applicable endeavor) can provide a more personalized user flow, which almost always spells value to the user. App usage, in turn, becomes more important – to the point of becoming nothing short of necessary.

How Does AI Enhance UI/UX Design?

One of the best ways to underscore how sophisticated AI has become in a relatively short period of time is by explaining how AI can improve UI/UX. At best, most of the process relies a lot on AI algorithms. Harnessing the power of data and information processing, these intelligent programs liberally conduct their own improvements to any UX model with the ultimate goal of optimization.

Of course, “enhancement” wouldn’t have been the best word to use if predictive analytics lacks scalability or an ability to improve on its own. Obviously, these are innate qualities of artificial intelligence. AI can harness deep and continual learning to make the best adjustments to user experience.

Predictive analytics can take care of the vital step of making the UX more engaging and personalized overall. The deep learning capability also allows AI to come up with numerous variations in design and suggest equally myriad alternatives. And it does so without having to spend countless hours just to gather the right user data and cover the needed scope. In fact, such a complex and comprehensive form of data analysis was close to impossible before the advent of AI.

It doesn’t stop there for you can also count on AI to perform any one of these feats in UI/UX design via predictive analytics:

Better, more personalized advertising. Social apps, in particular, can make good use of AI’s ability to gather, over time, every page a user likes, the comments he makes, and other data regarding interests. With this, they can come up with more creative advertisements that they can integrate into their UI/UX.
With the help of automation, personalized product recommendations can be made in a staggeringly short amount of time and with minimal intervention. These automatically change based on user behavior and are tailored to suit their current needs. Indeed, automation serves as one of the main reasons behind the sheer efficiency of predictive analytics.

Conclusion

Predictive analytics’s trajectory is upwards, and all the stats prove it. Its market share is already slated to expand in the years to come. The mere fact that it is already determining the direction of mobile UI/UX design drives the point further.

App developers, in short, should begin looking for ways to leverage predictive UX using applicable platforms available to them (not least of which is Drupal). In the end, predictive analytics paves the way towards what the mobile industry is bound to become: a fundamental part of any person’s day-to-day life.