AI & UX: The Future with Predictive UX

Welcome to #GetUX 23, where we discuss the future of Predictive UX ??

What if your product or service could be more than what the user wants — if it could predict what they want, even before they know they want it?

We designers are no stranger to the concept of personalization. In today’s world, where ever growing amounts of content and information are presented to the user every day, personalization is becoming more and more important. Businesses that don’t deliver risk failing behind.

Fortunately, we already have a slew of artificial intelligence powers ready to wield. AI-powered predictive UX leverages data and machine learning to anticipate user needs and create seamless, proactive experiences that keep users coming back to your product.

Examples of predictive UX can already be found in your everyday life. Netflix, for example, uses its algorithms to suggest you content you might like to watch based on your previous watching history. Travel apps also often offer add-on activities that you have been known to personally like, on top of what you ordered manually.

Prediction Matters

Predictive UX matters exactly because it is personalization at scale. Customers want experiences that are tailored to them. AI enables you to do this without much human effort.

The increased satisfaction also provides a reason for users to return; therefore they’re more likely to return to interact with your product. It also reduces friction to buy and therefore leads to higher revenue, satisfaction and loyalty.

Moreover, today, these kinds of ultra-personalized experiences are becoming very common. As its usage becomes more and more widespread, companies that fail to adopt it risk falling behind their competitors.

How it works

Data collection

The lifeblood of machine learning and AI is data. Gathering this is no simple task, but if you already have an infrastructure where users engage in interactions with your service, you could gather those interactions (within limits). In e-commerce sites, for example, the company could decide to track clicks, searches, purchases, etc.

Machine learning

Algorithms then learn from this data, analyzes it, and identifies patterns and connections. It could, for example, predict when a user is likely to churn based on previous inactivity or activity and trigger a retention campaign.

Real-time adaptation

Besides learning from pre-existing data, AI can also dynamically adjust experiences to reflect changing user behavior. For example, if you’re doing a fitness app and there’s a slight drop in a certain user’s activity levels, the AI could suggest easier workout routines to get them back on track.

Challenges

While tempting, predictive UX can also be challenging. Its challenges mainly lie in getting consensual data, avoiding over-personalization, and the bias present in many AI models.

Respecting data privacy and consent for data

AI needs data to function, but this data needs to be gathered responsibly. Some jurisdictions already penalize data collection without careful handling, like Europe’s GDPR — General Data Protection Regulation.

On this, companies should consult with available legal and data agencies to ensure that the data they collect is compliant with effectual laws. Else, besides the criminal risk, companies also risk losing the trust and respect of their users, leading to large churn.

Avoiding over-personalization

There’s a fine line between helpful and intrusive. Predictive UX may start to feel intrusive when it becomes too deeply intertwined with user behaviors and attitudes.

You wouldn’t mind an e-commerce app giving you the right suggestions every so often, but it giving you correct suggestions on overly private stuff might feel bad on you. A more aggressive way of saying this is that there’s a line between helpful and creepy.

Overcoming this requires you to be able to train the artificial intelligence correctly. If you’re unsure, it’s a great bet to hire data agencies that are specialized in machine learning and artificial intelligence.

Bias in AI models

Besides the risks of data collection and the AI becoming too intrusive, there is also a risk that’s inside within the AI model itself — the bias. AI predictions, in short, are mechanistically predicated on the data they’re trained on.

Poor-quality data can lead to inaccurate or unfair results. In extreme cases, this might even feel like discrimination. Cases abound, but the particularly more egregious and public ones involve large-language models behaving in a rogue manner that discriminates upon ethnic groups.

The future is Predictive UX

AI will make experiences even more personalized. They won’t only be able to predict what a customer needs, but also to know how they prefer to engage.

As these experiences grow across many platforms and services, more users will get accustomed to them. The best bet for a business these days is to join the movement, but doing so requires a thoughtful approach and careful consideration. For predictive UX to work well and leave a great impression on users, it has to do so with data that is collected ethically and thoughtfully, avoiding over-personalization, and avoiding bias that might happen with artificial intelligence models.

Header photo by Mahdis Mousavi on Unsplash

Maturis is a UI/UX design studio geared towards making better #ArtificialIntelligence apps. Get in touch with us through LinkedIn Messages to take your idea to the next level!

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