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Machine Learning in Mobile Apps: The Ultimate Buddy

Imagine the mobile apps that you use most often. For the most part, you probably enjoy a seamless experience. Not only are you familiar with the app, but it also feels familiar with you.

ML in Mobile Apps

In other words, the more advanced an app is, the more likely it is to predict the actions we’re going to take at any given time.

Working quietly behind the scenes to power this type of predictive customization are machine learning algorithms. These powerful forms of AI study our every move, working with copious amounts of data.

From there, they condense these data points into patterns of use. From there, the AI algorithms powering our favorite apps start to customize our experience.

While there are hundreds of valuable uses of AI in the context of mobile apps, customization is one of the most robust and meaningful for users.

Let’s take a closer look at why personalization features from machine learning programs are the ultimate buddy, especially when it comes to entertainment.

Discovering the Next Big Thing

Personalization features are tailored toward a user’s interests, meaning one focus for many algorithms is making suggestions.

These are designed to help you uncover the next Big Thing in a specific sector. Let’s use the example of casino games.

In terms of the virtual casino, machine learning can help players uncover new titles that suit their interests. At the moment, many casinos are sampling new and innovative games.

There are branded titles from major franchises, from Game of Thrones to The Phantom of the Opera. Even arcade games are seeing added attention for casino fans on the hunt for a bit of nostalgia.

The same goes for hits like Amazon’s Kindle store. Readers can pay a low monthly subscription for access to millions of books.

But when it comes to sifting through the latest titles, there’s nothing quite like Amazon’s recommendation algorithm. The goal is to keep users interested by introducing them to their next favorite creator or content.

Suggestions for Every Niche

Let’s take this a bit further. We mentioned casino game recommendations and book recommendations above—but machine learning algorithms can dig a lot deeper than basic suggestions.

In fact, personalization features have become huge because they’re able to meet the interests of highly niche users.

Let’s shift over to the music industry. Spotify, for example, is well-known for its ‘Discover Weekly’ playlist. This playlist makes suggestions to listeners based on their most recent musical obsessions, along with their entire music library. It’s a worthy recommendation engine to any user.

But it’s especially meaningful to those who like the strange and unexplored. That might mean sticking solely to indie artists who don’t have major record deals.

Maybe it’s about exploring historical instruments like the theremin or digging deep into regional favorites like BlueGrass. Without handy machine learning suggestions, hobbyists must do the leg work themselves.

Machine Learning in Mobile Apps

Keeping Your Circle Small

We live in a globalized world, meaning our favorite content, products, and creators might come from all over the globe. Overwhelmingly, most people view this as a positive.

But when it comes to retail suggestions, the best machine-learning algorithms will keep your circle small. In other words, they won’t recommend products that don’t ship to your area.

These are newer features, as social media is becoming one of the primary spaces where eCommerce takes place—especially for younger generations.

However, it’s useless to have an algorithm in place if it doesn’t know where its targets are located. While this might sound simple, it can be a deceptive challenge.

That’s because predicting where a user is based requires a program to look at other types of data—but in places like the EU, for example, third party apps must ask permission to use location services.

That means that these types of algorithms must work overtime to study other information to predict where a user is located and where they would most likely want a product shipped.

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