Machine learning abstract representation

If you think of your favourite online platforms that you use every day, most of them use machine learning to offer you the service you know. Whether you use Facebook, Twitter or Netflix, all of these use machine learning to find patterns within vast amounts of data. We examine machine learning and how you can apply it to future software projects to create a more personalised service for users.

What is machine learning?

A machine-learning algorithm uses various tactics to find patterns in even the largest amount of data. This data doesn’t just have to be statistics. It can also include words, images, interactions, and anything else used online. Anything that is stored in the digital world can be used within this algorithm. Many of the world’s most popular services use machine learning to offer users personal recommendations and curated content.

When you visit your Facebook feed each day, you don’t find hundreds of posts that aren’t related to your friends and interests. Instead, you’ll find selected posts that are mostly relevant. This is also true when you watch Netflix, as recommendations are based on your recent viewing sessions.

Many companies are already employing machine learning within their software. These companies use their software platform to collect data about you when you use their site or app. Whether that’s what you enjoy interacting with, things you watch or listen to, and the links you click through.

This is all information that is stored to create the best experience next time you visit. While it seems more complex on the surface, it’s actually a reasonably straightforward process of finding and applying patterns.

Creating an algorithm

Machine learning works by using an algorithm. This can easily be done with any software you are creating. When a user logs into your site or app, you can see what they enjoy and interact with. By defining what offers the highest value to a user on your site, you may optimise the site for future visits.

An equation is then created to predict whether an individual will like the information based on their relationship with other users or previous interactions on the site. Social media sites recognise who interacts and use this information to optimise feeds. This will create an experience and display information that users will enjoy seeing.

You can use a single value or multiple values within the algorithm which can make things more complex. Start by considering the query inventory and then score the user on each prediction of their interactions. Out of many different predictions, you then combine this to create a single score.  This will create a personalised feed of information for your end-user. However, if you are looking to use this within your new software, a team of professionals will create this algorithm for you.

Benefits of machine learning

Some companies avoid machine learning.  They feel it encroaches on personal information too much. However, it can result in an improved user experience for visitors to your site or app. Machine learning can help your company target the right users, especially if you are using advertisers on your site. This is crucial for reaching the desired audience.

If you’ve noticed on Instagram that you receive adverts targeted to your interests, this is due to machine learning. For paid advertisements, this can be a huge benefit and help keep your clients happy.

While this may sound counter-intuitive, many companies are realising security benefits from machine learning. It helps to detect spam and bad links which can quickly be taken down if they pose a threat.

Machine learning can help to enhance the quality of images or videos. Advertisers and users benefit from machine learning in this way. You’ll also find data you collect allows you to share your product to its full potential. You can offer or advertise new features to the intended audience who are more likely to interact with them and enjoy the experience.

Machine learning also minimises the need for human interaction in both safety and personalisation processes. Data is automatically collected and manipulated to create the desired effect.

How to use machine learning

When it comes to your software, you probably have an intended use or goal in mind for your product. To keep up with current consumer demands, you’ll want to provide a personal experience for all. Use machine learning to offer a product that is easier to navigate and more intuitive to your user’s needs.

Similar to your social media feed which is updated to suit your tastes, you’ll find this can be applied to any app. You can use machine learning to improve marketing efforts, targeting the right customers.

Machine learning is one of the most popular additions to many of our favourite tools and sites today. It’s unimaginable that companies like Facebook and Instagram could operate without machine learning. It’s something you can easily apply to your next software project too.

Provide your users with a unique and personal experience that will keep them returning to your app or site over and over again. If you would like to find out more information or would like to see examples of where BSPOKE Software created efficiencies for other business, click here. And if you’d like to get in touch, click here.