5 Strategies From Top Firms on How to Use Machine Learning

With machine learning making disruptive innovation easier than ever before, it’s up to entrepreneurs to show the big kids how it’s done.

Machine learning is headed for a major growth spurt. After ticking past the $1 billion mark in 2016, the machine learning market is expected to hit $39.98 billion by 2025, according to a new report by Research and Markets. 

Where will all that growth come from? Everywhere! Machine learning was born in 1959, coined by computer scientist Arthur Samuel — but only recently has the larger business community come to understand its value. In the next few years, it will be adopted by everyone from Fortune 500 firms to mom-and-pop shops.

Of course, the first challenge of machine learning is identifying a use case. Not sure where to start? To make the most of this explosive technology, consider how today’s top companies, ranging in industry from retail to hardware to media, are using it:

Retail giant Target discovered that machine learning can be used to predict not only purchase behavior, but also pregnancy. In fact, Target’s model is so precise that it can reliably guess which trimester a pregnant woman is in based on what she’s bought. After a father discovered through Target’s persistent promotions that his 16-year-old daughter was pregnant, Target actually had to dial its initiative back by mixing in less specific ads.

Most companies’ promotions are driven by the seasons or holidays. Snow shovels go on sale in July, sunscreen in June. But consumers go through seasons in their own lives, too. The worst time to sell someone a car, for example, is right after she just bought one. It might be the best time, however, to market car insurance to that person. Machine learning can pick up on those rhythms, helping companies recommend their products to customers when the timing is just right.

My company has used machine learning to spur loyalty purchases. We discovered that if a customer is going through a life event (such as graduation or marriage), he is more likely to change his behavior than at other times in his life. An education company that knows 20 percent of its users leave every May, for example, might use machine learning to refer likely grads to a corporate partner or sponsor.

When someone posts a photo to Twitter, she wants people to see it. But if the thumbnail is 90 percent floor or wall, nobody is going to click on it. Twitter seems to have solved this problem by using neural networks. In a scalable, cost-effective way, the social media firm is using machine learning to crop users’ photos into compelling, low-resolution preview images. The result is fewer thumbnails of doorknobs and more of the funny signs just above them.

Give Twitter‘s thumbnail optimization a try for your next marketing campaign. Upload brand-aligned, user-generated photos, and let Twitter determine which elements of each image maximize engagement. Then, use the top-performing photo crops for your next Twitter campaign. Who doesn’t love free market research? 

We use machine learning to tweak images for conversational engagements. The challenge is making sure that rich images load quickly enough to keep up with a live conversation. A user who sends a question or search request expects a reply in the form of an image or GIF immediately. Using machine learning, we can deliver appropriate responses at scale in seconds.