Select Page
Blog - How Good User Personalization Stems From Quality Design Choices

How Good User Personalization Stems From Quality Design Choices

2016 was the year when artificial intelligence (finally) inserted itself into mainstream technology.

Here at Applause, we reviewed chatbots and artificial intelligence powered travel apps. The push of artificial intelligence is promising and will no doubt continue in 2017 and beyond.

The products we reviewed last year were innovative and appealing. Whether they truly delivered a digital experience that keeps people coming back is debatable.

When Machine Learning Is Not Necessary

trip.com app

A main motivator for using artificial intelligence, machine learning and neural networks is the desire to deliver an easy, useful and satisfying experience. A useful and satisfying experience includes tailoring the experience to the individual and making it easier for a user to find relevant information or reach their intended goal. Personalization and discovery are not new. Rather they are the core of what user experience as a discipline is supposed to be all about.

Using complex machine learning algorithms to reach these goals is not mandatory. It is but one of many directions. For instance, Trip.com’s mobile app provides a best-in-class tailored experience based on a user’s location, time of day and other information (such as current weather). Trip.com combines all of that information with user-provided preferences and creates a smart list of personalized options.

As an example, Trip.com recognizes my location and provides me with suggestions for restaurants, things to do and weekend getaways. And when it is a particularly cold day, there’s a “stay warm” category with ideas for cozy ways to pass the time.

The Design Challenge Of User Preferences

User-provided preferences have long been a thorn in the side of designers. Asking (sometimes forcing) the user to answer a list of questions is an onerous turnoff. Trip.com makes user preferences optional and provides a simple image-filled screen so the user can tap images to indicate their interests. Trip.com’s is a painless process and takes 30 seconds.

Another great example of simplicity through design is the redesigned Airbnb mobile app. The new Airbnb app narrows down a list of possible places to stay out of large data sets. The app then applies a substantial number of filters, which makes it a significant design challenge. Doing so on a mobile device, where screen real estate is limited, is beyond challenging. The redesigned Airbnb app does a great job of addressing that challenge. It focuses on asking relevant questions in a pre-meditated order to address a person’s needs and provides a digestible and relevant set of options.

A designer’s primary goal is to make the user’s experience intuitive, satisfying and worthwhile. Whether this is accomplished through smart design, use of available info, machine learning-powered deductions of large datasets or a combination is secondary to serving the user’s needs and wishes.

Want to see more like this?
View all blogs ⟶
Published: January 9, 2017
Reading Time: 4 min

Beyond Traditional Testing: Advanced Methodologies for Evaluating Modern AI Systems

As AI systems continue to demonstrate ever more complex behaviors and autonomous capabilities, our evaluation methodologies must adapt to match these emergent properties if we are to safely govern these systems without hindering their potential.

Integrating CX Into Everyday QA Testing

Enhancing quality through a focus on customer experience

European Accessibility Act: IAAP Brno Hybrid Event Recap

European Accessibility Act: IAAP Brno Hybrid Event Recap My Applause colleague Jason Munski and I attended the ...

Agents and Security: Walking the Line

Common security measures like captchas can prevent AI agents from completing their tasks. To enable agentic AI, organizations must rethink how they protect data.

Crowdtesting Pilot Blueprint: Onboarding the Right Way

Take a step-by-step look at the crowdtesting pilot process

How Agentic AI Changes Software Development and QA

Agentic AI introduces new ways to develop and test software. To safely and effectively make the most of this new technology, teams must adopt new ways of thinking.
No results found.