Machine learning is on the verge of exploding. According to a recent analysis from Research and Markets, the machine learning industry is predicted to reach $39.98 billion by 2025, surpassing $1 billion in 2016.
Where will all of this expansion come from? Everywhere! The term “machine learning” was coined by computer scientist Arthur Samuel in 1959, but it has only lately gained traction in the corporate world. Everyone from Fortune 500 companies to mom-and-pop shops will use it in the coming years.
Identifying a use case is, of course, the first hurdle of machine learning. Don’t know where to begin? Consider how today’s leading organizations, ranging in industry from retail to electronics to media, are utilizing this transformative technology:
Target: Learn from the present-day to invest in the future Target, the world’s largest retailer, discovered that machine learning can predict not only purchasing behavior but also pregnancy. Target’s model is so accurate that it can precisely predict which trimester a pregnant woman is in on the basis of her purchases. After a father found his 16-year-old daughter was pregnant as a result of Target’s relentless marketing, the retailer was forced to scale back its campaign by incorporating fewer targeted commercials. The majority of businesses’ promotions are based on the seasons or holidays. Snow shovels and sunscreen are on sale in July and June, respectively. Consumers, too, go through seasons in their lives. For example, the worst moment to sell someone a car is shortly after she has purchased one. However, it might be the greatest time to pitch vehicle insurance to that person. Machine learning can detect these patterns, allowing businesses to offer products to clients at the optimal time.
Twitter: Create the perfect preview When someone uploads a photo on Twitter, she wants it to be seen by as many people as possible. However, if the thumbnail is 90% floor or wall, no one will click on it. Twitter appears to have found a solution to this problem by utilizing neural networks. Machine learning is being used by the social media company to crop users’ photos into engaging, low-resolution preview images in a scalable and cost-effective manner. As a result, there are fewer doorknob thumbnails and more hilarious signs over them.
Apple: Embrace ensemble experiences Anyone who has more than one Apple product is fully aware of how effectively the gadgets work together. Machine learning is now being used by the IT giant to produce even more seamless consumer experiences. In non-technical terms, Apple just filed a patent that suggests it prioritizes cross-device customization. For example, in the near future, an Apple Watch could offer an iTunes playlist to match a user’s heartbeat target in another app.
Alibaba: Customize customer journeys Alibaba is used by 500 million individuals, which is more than the whole population of the United States. From searching to purchasing, each of those customers has a unique and different path. Alibaba has a system in place to track and adapt each of those 500 million travels. Of course, using machine learning. Every e-tailer should be envious of Alibaba’s AI. Each shopper’s virtual storefront is personalized. The products that come up in the search results are perfect. Most spoken and written customer service inquiries are handled by Ali Xiaomi, a conversational bot. Every aspect of Alibaba’s operation appears to have been designed with the customer in mind, and every action the shopper takes tells the machine more about the shopper’s preferences.
Spotify: Deliver personalized media Spotify is discreetly exploring new features for its fan-favorite music recommendation service after acquiring two machine learning firms in 2017. Spotify’s investment in Discover Weekly reflects the high value that music fans place on personalization, which is made possible by the company’s creative use of machine learning. Even Spotify is taken aback by the popularity of Discover Weekly, which wasn’t even on the company’s radar when it first began in 2007. After Discover Weekly’s debut in 2015, copycat services like Apple’s New Music Mix rapidly popped up, but they’ve struggled to surprise listeners with recommendations like Spotify’s service does.
Conclusion
Machines, of course, cannot learn everything there is to know about a company or its customers. Companies like Apple, Spotify, and Alibaba, on the other hand, are pushing that line further and further back. It’s up to entrepreneurs to show the big kids how it’s done now that machine learning has made disruptive innovation easier than ever before.
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