Every Monday, millions of Spotify users get a fresh, personalized mix of songs called Discover Weekly. Having never heard these songs before, they’re likely to fall in love with them at once. Every time you visit eBay, the marketplace seems to read your mind. The items it recommends are tailored exactly to your needs. Ever wondered how this happens? The secret lies in machine learning algorithms.
In our previous article, we talked about the true meaning of machine learning. Today, we’ll have a closer look at the best machine learning examples to learn the biggest benefits of machine learning in business and find out how it can help your company.
Machine learning isn’t a whim of market giants. It’s what companies of different sizes are using today to not only stand out but also improve business performance, save money, and make strategic decisions. Below, we’ve highlighted the best machine learning use cases that can help your business grow.
Today, users not only want an individualized experience but expect it. According to recent research by Epsilon, 80% of customers are more apt to partner with a company that delivers highly customized experiences. Valuable content encourages users to frequently participate on your platform, stay loyal to it, and trust your brand.
To ensure constant user participation, you should serve relevant content. This means that you should separate the good from the bad. But as the quantity of content in your product grows, it becomes harder to deliver content that produces significant value for each user. That’s why it’s important to provide great content curation, in particular on platforms. As an open access system encourages unrestricted production, poor curation or no curation may lead to abundance and noise. Users may become discouraged and abandon the platform.
Under Armour Record, a health and fitness app, uses machine learning to provide users with personalized diet plans and fitness guidance. To get a full user profile, the application retrieves information from Under Armour bands and the Under Armour heart rate monitor, third-party smartwatches, apps including MapMyFitness and MyFitnessPal, and a phone’s GPS to track speed and distance when running or riding. Users can also log workouts and exercises such as fitness classes, gym trainings, and runs. Then the app makes recommendations based on the data gathered and what has proven effective for other users with similar profiles.
Another interesting case is Edmunds. This application started off as a source of information about different cars. Today, Edmunds is a platform that matches car buyers and sellers. If a user is looking at an electric car like the Nissan Leaf, advertisements on the page will show other available models of Nissan Leaf or other electric cars.
For social networks and media platforms, personalization isn’t just desired but imperative. Content in the Facebook News Feed is curated based on a user’s past actions such as likes, clicks, and comments. Twitter uses machine learning algorithms to automatically crop image previews to show an image’s best parts. Additionally, Twitter curates tweets based on user preferences so users first see tweets they’ll probably like. Netflix helps users find the perfect thing to watch. The machine learning algorithms that provide recommendations on Netflix are driven by user consumption behavior (information about what users watch, when they fast-forward, rewind, or pause, what country they’re in, etc.) and tags (genres, micro-genres, and similar movies).
Object recognition is one of the greatest and most commonly used machine learning applications. Lots of companies across different industries use object recognition. In healthcare, object recognition algorithms are used to detect diseases. In the automotive industry, they’re used for robots and driverless cars. In the mobile industry, face recognition provides cybersecurity.
Facebook uses machine learning algorithms to recognize users in photos even when they aren’t tagged. Additionally, Facebook describes images with words for visually impaired people. CheXNet detects pneumonia from chest X-rays. The model defines areas that indicate pneumonia most and estimates the probability of the condition. CheXNet has already proved to be more accurate than radiologists.
Another great recent example of object recognition in use is Amazon Go ― a chain of stores in the US with no cashiers, no registers, and no lines. You just walk into the store with the Amazon Go application, take what you want, and go. Cameras inside the store detect each item you take and the system automatically adds them to your virtual cart. Your cart updates each time you change your mind and return an item to the shelf or take another product. Once you walk out, the Amazon Go app sends you a receipt and charges your Amazon account.
There is only one boss. The customer. And he can fire everybody in the company from the chairman on down, simply by spending his money somewhere else.
Samuel Walton, the founder of Walmart Stores Inc.
One aspect of a sustainable business is reducing churn and improving user retention. With so many apps on the market, you can easily lose customers to your competitors. Retaining users is therefore of absolute importance. Knowing what makes users leave your platform and when this happens helps you define at-risk customers and your most profitable ones. Gaining insights into customers helps companies make necessary adjustments to business strategy, attract new customers, and increase retention in a competitive environment.
PayPal uses machine learning to detect customer churn early on. Algorithms use historical data and the cadence of transactions to define if a user has churned, when, and what caused it. This helps PayPal retain users and reactivate inactive ones. Pinterest uses machine learning not only to moderate spam and curate content but also to lower the newsletter subscriber churn rate. One of the other practical examples of machine learning is customer behavior predictions. JPMorgan Chase uses machine learning algorithms to analyze transactions and finds customers that are more likely to purchase additional services.
For industries that involve financial transactions, detecting fraud is a challenge. Since digital transactions have greatly increased in recent years, the risk of fraud has too. With machine learning, businesses can review millions of transactions and identify suspicious activity faster than with human analysts.
A fine-tuned machine learning solution can detect up to 95% of all fraud and minimize the cost of manual reconciliations, which accounts now for 25% of fraud expenditures.
Feedzai, a data science company that deals with fraud detection
To catch fraud, PayPal segregates uncertain transactions from legitimate ones. Doubtful transactions thereafter pass through three machine learning models: linear algorithms, a deep learning network, and a neural network. Then the algorithms decide which payments are fraudulent. To detect fraudulent behavior, Huawei Technologies performs real-time analysis for credit card and mobile transactions. Each time a payment is made with a card or via a mobile app, the machine learning algorithms approve or decline the transaction based on past customer actions. Mastercard tracks the location, time, and amount for a transaction, the device the user makes the transaction from, and data on past purchases. Then machine learning algorithms make a real-time decision on whether the transaction is fraudulent or ordinary.
Language is an integral part of our daily lives. People think, communicate, and make decisions and plans in words. Speech technologies are starting to become a common way for people to interact with one another. Common technologies that deal with human languages include language analysis, chatbots, speech recognition, and machine translation.
In the eCommerce industry, product feedback is an important source of information for both customers and sellers. It’s hard for eCommerce employees to understand every product review since users come from all over the world and speak different languages. Alibaba, for example, uses machine learning algorithms not only to automatically translate customer reviews but also product titles, descriptions, categories, and emails.
Have you ever wondered whether your writing is too casual when communicating with clients? Or have you ever worried that your email may have errors? To save people from embarrassing mistakes, Grammarly uses natural language processing algorithms to check grammar, punctuation, and spelling. Google Docs autocorrects writing mistakes. Additionally, the Google search engine autopredicts search results to help users find information faster.
Skype makes it easier for people to communicate across languages. Skype Translator provides users with both voice and text translation in real time. As of February 2019, voice translation is available among ten languages: English, German, Spanish, Italian, French, Chinese, Arabic, Portuguese, Japanese, and Russian. Skype translates messages in over 60 languages.
Another use of natural language processing is information extraction. Processing a huge amount of information takes lots of time. And image detection combined with natural language processing helps save time and human resources. Take JPMorgan Chase as an example. For an international investment bank and financial services company with millions of customers in more than 100 countries, it’s hard to process documents manually. That’s why they decided to automate the process with machine learning. Their algorithm analyzes documents and aggregates data. It used to take personnel about 360,000 hours to process 12,000 credit agreements. Today, an algorithm does the same work in several minutes.
Machine learning is a powerful tool for your company. It provides lots of business solutions including content personalization and individualized recommendations, object recognition, user behavior analytics, fraud detection, and natural language processing.