Our interactions with computers are getting smarter and more human-like. With the advent of artificial intelligence technology, computers can learn more and do more to make our lives easier and our business operations more efficient. While we hear a lot about concepts like AI and machine learning, are these just buzzwords, or can they have a practical use in your business? Read on to learn more about the basics of machine learning, where it's being used, and how you can utilize it during your app development.

Machine Learning 101

You may have heard about machine learning in different business applications, but what exactly does it entail? To understand, first, envision all the data that your business encounters. This could be customer data like visitors to your website, how they interact with your site, where they click, etc. This data can be interpreted by algorithms to pick out statistics and patterns to make predictions. Think of, for example, Netflix keeping track of what you watch or Amazon tracking your purchases and browsing history. Using this info, these platforms provide their customers with helpful recommendations about what products they may be interested in. This is the core of machine learning - using data to make predictions.

In business applications, machine learning is being used to predict customer behavior so that businesses can better cater to potential customers. Your machine learning algorithms can interpret the massive amounts of data that your business collects, making it easier to land more sales and turn leads into customers. Machine learning can also make your app more engaging and can help provide a better overall user experience.

Machine learning and related technology have been in the news lately because the algorithms that power it are getting smarter, faster, and easier to use. This means that machine learning is becoming more applicable to businesses in nearly every industry.

Who Is Using Machine Learning?

As mentioned above, machine learning has been widely used in the streaming entertainment industry for a while now. Any platform that tracks what you watch and makes recommendations based on that is using machine learning to do so. But as machine learning technology advances, more and more businesses are putting it to use. Machine learning isn't just about making recommendations or tracking customer behavior - it's about using data to solve problems. Here are some top applications that use machine learning:

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  • Fraud detection‍
    • Banks, insurance companies, and other businesses in the fintech sector can use machine learning to spot fraud. For example, a machine can be taught to accurately identify the likelihood of a fraudulent insurance claim. It could also be used to monitor fraudulent activity on someone's checking account.
  • Medical applications‍
    • Even the best doctor's brain can only hold so much information. A computer, on the other hand, can hold exponentially more. This means that computers, when fed the right data, can make more accurate diagnoses than a doctor, or even a highly-trained team of doctors.
  • Sales‍
    • With machine learning, you can classify all sorts of things. For example, a nonprofit organization can classify constituents based on how likely they are to donate. You can use data like their income, previous donations, interests, demographics, and how they interact with your website or emails to predict if they may donate. Then, once these leads are classified, someone from the organization can reach out directly to solicit a donation.

Machine learning is a huge opportunity for you to capture and use data to improve your business operations.

How To Use Machine Learning in App Development

If you're looking to make an app for your business, you'll want that app to serve you well and do its job for years to come. With machine learning technology becoming more prominent using it in your app development can help to keep you relevant and useful to your customers. But there are few things to keep in mind when getting started with machine learning. First, think about what kind of data you may be able to collect in your app. Will you be using it to make recommendations, understand consumer behavior, or predict trends?

Next, ask yourself if the data you're collecting is structured or unstructured. Structured data is data that's already organized and easily searchable. For example, customer names, addresses, etc., are examples of structured data. Unstructured data, on the other hand, may come in the form of audio clips, video, images, or other forms that aren't easily organized or searchable. Machine learning algorithms are complex enough to make sense of both structured and unstructured data, but it's important to know what type of data you're collecting so you can find the proper tool.

There are already plenty of available machine learning models that are pre-built to analyze your data. This means that, with the right team in place, you can begin using machine learning in your business without having to build anything from scratch. Here are some types of machine learning that you can get started with for your next app:

  • Supervised learning‍
    • The algorithm learns by example. You feed it relevant data and examples of the right response. Later, when you feed the machine data, it will be able to choose the correct response.
  • Unsupervised learning‍
    • You feed the algorithm data and it works to spot trends and patterns on its own.
  • Reinforcement learning‍
    • Developers teach the algorithm to make decisions on its own based on its environment.

Each of these types has specific applications that may be useful to your business. To decide which is best for you, think about the type of data you have available and what problem you'd like to solve with it.

Machine Learning In Action

Now that you've seen what machine learning can do, what are some successful examples of it in action?

  • Social media‍
    • Snapchat is known for its fun filters that can morph your face and your surroundings into something totally new. This requires expert facial recognition, which is a subset of image recognition. Facebook takes machine learning a step further with a few features. It makes friend recommendations based on your demographic info and shows the stories that are most relevant to you in your newsfeed.
  • Music apps‍
    • Platforms like Spotify and Pandora track what songs you listen to and which songs you love the most to make helpful and relevant music recommendations
  • Dating apps‍
    • Dating apps like Tinder strive to match you with people who you might be interested in, based on where you live, what you have in common, and profiles you've responded to in the past.

Ready to find out more about how machine learning can help your business grow? Contact us for more information.