The machine learning market is growing at pace. According to Research and Markets it should reach $40 billion by 2025. Currently it is already over the $1 billion mark, but to reach the estimated value it will have to make a major leap in growth.
What will cause it to grow? Every company will start using it once they have identified a use case, and that is one of the barriers to adoption at the moment, but we can learn from the ways in which major companies are already using machine learning.
Apple is working on a cross-device personalisation tool and has already applied for the patent. It is rumoured that what this will do is allow your Apple Watch to connect with your iTunes playlist and find a piece of music to match your heart rate.
Twitter is working on visibility problems with thumbnail images. It is using neural networks to find a scalable, cost-effective way to crop users’ photos into compelling, low-resolution preview images.
This Chinese retail giant has 500 million customers and each of them uses the store in a distinct way. So Alibaba is using machine learning to track every customer’s journey. Furthermore, all Alibaba’s online storefronts are customised for each shopper and searches will bring customers the products they want to see. There’s also a chatbot who handles most of the spoken and written customer service inquiries. Every element of Alibaba’s business has been built for engagement with the shopper, and every action the shopper takes teaches the machine more about what the shopper wants. It’s extremely effective.
American retailing giant, Target, is using machine learning to reach and respond to its pregnant customers. 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.
Typically companies have been driven by the seasons, but machine learning can help businesses respond to ‘seasons’ in people’s lives. For example, a person who has just bought a car doesn’t want to see car ads, but motor insurance ads are appropriate. Basically, machine learning can pick up on those rhythms, helping companies recommend their products to customers when the timing is just right.