If you have ever misspelt a word on Google and got a correct result, you have experienced machine learning in practice. It is a hot topic nowadays as businesses start to take advantage of big data and explore new ways to improve their interactions with customers and grow revenue.
What is machine learning?
Lets first define what actually machine learning is and how it differs from artificial intelligence because although these two are very closely related, it is not the same thing. Artificial intelligence is a computer's ability to perform tasks like understand language, distinguish objects and sounds, learn, solve problems and do other things that imitate human logic. Machine learning is a branch of computer science which uses algorithms to find patterns in data without being told by humans how to do that and then predict the probable outcome. Loosely speaking it is a way of achieving artificial intelligence.
But it all starts with gathering lots of data from web analytics, customer demographics and usage information to purchase behaviour, pricing and inventory systems—all on their own not being particularly helpful. Finding relevant insights into data and applying machine learning to those data enables companies to truly optimise their processes, react to their customers' needs proactively and gain a significant competitive advantage.
How businesses use machine learning
With the evolution of computer systems, increase of cloud-based capacity and computer power machine learning has become much easier for companies to use. Any industry can take advantage of this technology and here are a few examples of machine learning in practice:
- Target, a large department store retailer in the United States, adopted a machine learning model to identify pregnant women based on their purchasing behaviour. Using those insights, they can send highly personalised offers on pregnancy supplements and baby products instead of running ads at times when a prospect was not ready to buy. They managed to encourage people to buy a wider variety of items from them rather than their competitors and establish more trusted customer relations.
- Ocado, one of the biggest online grocery stores in the world, enables shoppers to purchase items through their mobile application. They use automated warehouse that picks and packs the products and ships them directly to customers within an hour after buying. To make their business model even smarter, they use machine learning to categorise customer emails and then prioritises them for response resulting in 4x faster response rate than before.
- North Face, an American outdoor product company, adopted machine learning technology to help consumers find the best item for them. Instead of scrolling through pages and trying to figure out which of their 350 jackets is the right one, their app asks a series of questions like location, season, gender and what sort of activities they are planning to engage in and shows them the most relevant product. Result? Increase in sales conversions.
- A Japanese food manufacturer Kewpie used to employ four people to check around five tons of food daily to identify faulty potatoes on the production line. By building a machine learning model, they managed to automate this process and ensure even better quality standard during manufacturing.
- PayPal became pioneer in risk management by using three different machine learning models (linear, neural network, and deep learning algorithms) to judge whether users pose a risk of fraud. Their intelligent model is quickly able to determine trustworthy customers and put them in the express lane to a transaction.
While machine learning still cannot do everything, it can make businesses more efficient and profitable as these examples show. Not sure where to begin? If you have data, a lot of data, we can help you find and build the right solution for your business.