There’s a good chance you take part in machine learning every day, as it’s become increasingly ubiquitous across major platforms. As a digital agency, it is vital for you to take note of what this technology is, so you can learn to use it in your own business.
Machine learning is becoming more affordable thanks to open-source tools. This accessibility means that digital agencies can take advantage of its power without breaking the bank. To get started, you can begin visualizing different ways to use your agency’s data by studying what other companies do.
In this article, we’ll introduce you to the main concepts behind machine learning. Then we’ll show you how three major online companies are profiting from this technology. Let’s get started!
An introduction to machine learning
Machine learning gives computers the ability to learn new things without explicitly equipping them with that knowledge. For example, a human can easily guess that an apple is probably going to be red, green, or yellow. Machines need the right data and tools to make the same guess.
In other words, machine learning helps programs identify patterns in data and make predictions or decisions. This relies on having large amounts of quality training data, or data where we already know the correct answers. Algorithms are built using these training results, which in turn provide a foundation for predicting the outcome of new data.
Email spam filters are a practical example. To begin, you need to show the program examples of what is and isn’t spam. The program will attempt to identify specific characteristics that are more common in the spam emails, such as misspellings or certain vocabulary. These elements will become part of the algorithm for filtering new incoming mail. What’s more, the program can constantly analyze new techniques and patterns used by spammers, and update its flagging system accordingly.
Creating such an algorithm manually would be much more difficult, and impractical to maintain over time as massive amounts of new data stream in every day. Machine learning allows services to deliver a better product in ways that aren’t feasible for humans to do by hand. This increasingly-popular technology releases humans from time-consuming rote tasks, so they can focus on creative solutions.
If you’re interested in getting started with machine learning, there are powerful open-source tools that enable developers to build their own programs. A few strong examples are R and Google’s TensorFlow. If you can identify new ways to use your agency’s data but need help with the implementation, you can find freelance developers on sites like Upwork.
3 Real-World Examples of How Machine Learning Applies to Digital Agencies
Machine learning enables computers to predict outcomes based on historical data. For instance, let’s refer back to our earlier example about spam filters. First, the program is taught what to look for with existing spam mail. Using the resulting algorithms, it can then filter new emails to separate the good from the bad.
The process of machine learning typically follows the same pattern:
- Identify what you want the machine to be able to predict.
- Feed labeled training data into your machine learning program.
- Identify a strong prediction algorithm.
- Test whether the algorithm works.
- Refine the program and repeat the process.
Just about any company with data can find ways to apply this technology, depending on its goals. Here are three examples of companies that already use machine learning, to serve as inspiration.
1. Google Search Results
As you probably know, Google specializes in internet-related services and products, ranging from online search to cloud computing. The company is a big fan of machine learning, and even runs its own machine intelligence publication.
Machine learning is primarily used within Google to improve its search features. By analyzing existing search data, machine learning can help identify new ranking signals. Google can look at what users searched for, whether they found what they needed, and what sites were presented in what order. Then machine learning algorithms can determine whether different ranking signals would have made those searches more successful. Machine learning helps Google examine millions of searches all at once to find new correlations between searches and results.
Without machine learning, it would be impractical (if not impossible) for Google to use all this data to improve its services with such accuracy. Since the goal of Google Search is to predict the best results based on a few search terms, machine learning is an invaluable tool.
Do you offer content to your users that’s suited to a similar search interface that could be improved over time? If you work with any kind of publication, offering an easy-to-search index may put you ahead of the competition.
2. Facebook Newsfeed
Without machine learning, Facebook as we know it would not exist. For example, the technology is instrumental in its newsfeed. Machine learning helps the program choose what user posts, activity, and ads to show in what order, depending on each user’s profile, usual behavior, and browsing device.
While we know Facebook relies heavily on machine learning, the exact newsfeed algorithm is unknown. However, using what we know about machine learning, we can make an educated guess. Facebook checks your likes, dislikes, and content you’ve engaged with. Using this data, along with the average results of other users like you, Facebook can then examine new content to decide whether you are likely to find it interesting.
This allows for high personalization, which is the future of digital marketing since it offers a customized experience for every user. If you regularly work with clients who publish a lot of content, you could implement similar algorithms to display relevant ads to visitors or offer your own news feeds.
3. Netflix Recommendations
Netflix was first founded in 1997 to let people rent DVDs by mail, but now focuses on streaming media and video-on-demand. One of its core features is that it recommends the next best thing to watch once you’ve finished a movie or TV show. How successful the company is at this helps to determine how long customers will continue paying for its services.
Machine learning helps Netflix build complex algorithms to factor in individual user behavior, alongside trends in ratings and completion rates for watching shows. This way, it can often accurately guess what show you will be most likely to binge watch next. It knows that the typical Netflix member loses interest after reviewing 10-20 titles. Netflix must recommend something worthwhile during that window, or it risks users abandoning the service entirely.
This level of personalization is what keeps Netflix in business. Much like Facebook, how personal the service is helps entertain and engage users over the long term. What does your company offer that would benefit from a similar recommendation system? For example, if you run an online store you could automatically offer clients products or services bought by customers with similar tastes.
Put your own machine learning strategies in place
Massive online companies are not the only businesses that can profit from machine learning. As this technology becomes more accessible, your digital agency can learn by example how to put your own machine learning strategies in place.
In this article, we’ve explored how three industry giants use machine learning:
- Google: Creates complex, nuanced ranking algorithms for search results, which keeps it at the top of its field.
- Facebook: Offers an extremely customized news feed, so content is always fresh and interesting.
- Netflix: Recommends the next best show to watch, allowing each user to experience the service in a unique, personal way.
Image credit: Siyan Ren.