Why AI is the Secret to Mobile App Success

AI means a whole new level of intelligent mobile app personalization.

In the vastly oversaturated mobile app universe, creation means nothing — here, only survival counts.

That survival, though, is easier said than done. Despite people’s insatiable appetites for mobile apps, attention can be fickle and fleeting. While more than 300 billion apps have been downloaded, half are opened fewer than 10 times, and 20 percent are used once. When those apps are inevitably deleted, users say it’s because they weren’t “useful.”

This download-and-delete cycle doesn’t help anyone — audiences aren’t getting the mobile experiences they want, and brands aren’t generating ROI against their app investments. Bucking this trend requires brands to double down on both relevance and value — and that’s where artificial intelligence (AI) comes in.

AI, in support of personalization, adds value and enhances customer experiences. For example, Spotify — consistently the number one music app — serves up AI-powered music recommendations. Google integrates machine learning in its Gmail “Smart Reply” suggested responses. Though the feature didn’t begin rolling out until April, these responses now used in 10 percent of all email replies. American Airlines and United Airlines are even using AI assistants in their apps to answer customer flight status inquiries instantly — no lengthy hold times needed.

“The mobile apps we rely on now are providing a personalized service to their consumers — and they’re getting it right,” says Gina Casagrande, senior evangelist for Adobe Experience Cloud.

Small screen, big potential

While AI is finding its way into countless applications, mobile devices are particularly suited for this technology. With a built-in phone, camera, microphone, GPS tracking, and a premium position — literally, in people’s hands — mobile devices are optimal when it comes to understanding and engaging with a customer and their surroundings.

“Mobile apps have so much contextual data: a user’s location, the number of times she has opened the app, what she’s purchased before, as an example,” says Gina. “The data is incredibly rich, and those attributes get fed into the AI models automatically, which makes them more powerful.”

The appetite for deep connections, sticky content, and meaningful engagement is also there in apps — and it’s changing the way people consume content and buy products. Retail customers looking to buy from their mobile device, for example, prefer purchasing via mobile apps over mobile web — 57 percent to 43 percent.

“How you connect with customers and spend time with them depends on what your business priorities are and what you’re trying to do,” Gina says. “Regardless, connecting more deeply — and more often — through a mobile app can make your brand an integral part of the customer’s life.”

Personalize the recommendations you make in your mobile app

Given the contextual data inherent with mobile devices, this channel is also prime for personalization. And when powered by AI, you can deliver even more compelling customer experiences — the kinds of experiences customers demand from the brands they frequent.

“Customers want experiences tailored to them,” says Gina. “That means leveraging data on past behaviors, current context, location, time of year, time of day, everything — all to predict and personalize existing and future interactions.”

Personalizing now, she explains, means taking the smallest moments (i.e., how a customer navigates an app, their most-used tools, and when they receive push notifications) and using AI to maximize the relevance and value of each interaction.

Recommendations have morphed into powerful customer touchpoints thanks to mobile and AI. Early on in the mobile app landscape, recommendations were a one-size-fits-all process anchored in popular products or high-margin items.

Today, however, product recommendations are deeply relevant to the customer and their real-time experiences. From what’s in their cart, to purchase history, to how the customer arrived at the site, to third-party intel, brands can cater to the probable wants, needs, and goals of the customer.

The recommendation algorithm in Adobe Target is powered by Adobe Sensei, the AI and machine-learning framework that operates across all Adobe solutions. Target considers a customer’s preferences (from geography, to color, to purchasing habits, to engagement stats) and layers in data from the product universe and similar shoppers for a highly personalized recommendation geared to that individual customer.

Keep up with evolving customer preferences

Keep in mind that, even if you’ve perfected some personalized experiences, customers aren’t static. As they progress through various life stages or evolve their relationship with your brand, your app’s functionality may have to change and adapt as well.

To ensure your app’s personalization capabilities keep pace, plan to continually measure and optimize both engagement and conversions. “It can be as simple as setting up the A/B test and using Adobe Target’s one-click personalization, Auto-Target, to automatically deliver the most optimal experience to each visitor, based on their individual profile,” Gina says. You might zoom in on a certain user group, for instance, and notice that over time one type of content or user interface naturally elicits more engagement. With new personalization reports, it’s easy to turn all of that data into actionable insights.

That proactive, ongoing testing can inform future content and design so that the healthy engagement from that group doesn’t fizzle out over time. “The goal with AI isn’t to burn bright for one hot moment and then fade away, but, rather, to offer always-on experience optimization,” says Gina. “We want to help build apps that engage users again, and again, and again.”

Discover more ways mobile is driving unparalleled customer engagement.