How Machine Learning Generates Income for Businesses

More than 60 years ago, engineers realized that a computer program could “think” and “talk” like a human. That’s when the first chat-bot ELIZA conducted psychotherapy sessions. Today, these initiatives have evolved into something more sophisticated: artificial intelligence, computer vision, voice assistants, and so on. They are based on machine learning algorithms development. This is the technology that everyone is talking about, but nobody sees. To unfold the mystery, let’s look at how machine learning in business helps to make money. 

Machine learning is a technology that teaches a program to act like a human. Moreover, the model must independently improve its capacities based on data about the surrounding world. Scientists from Stanford University briefly and clearly explained ML as a field of study on how to make computers function without programming them. 

To what extent is this possible now? Let’s imagine a problem where the conditions and the correct answer are known. For example, machine translation. The condition is a word combination in Russian. The correct answer is a translation into English. Machine learning works as a black box. It takes an input condition and produces an arbitrary response. For example, a text in English. 

For the black box to process information correctly, it has additional parameters. When a person sets the most accurate parameter values, the neural network finds answers that are extremely close to the correct one. This is how ML “learns” to solve other tasks of the same type, even if it doesn’t know the answers to them. 

For ML to recognize a face, respond with Alexa’s voice, or perform other tasks, it needs the following components:

The essence of ML technology is to scan huge amounts of data and extract useful information from them. Therefore, for machine learning to function in businesses, specialists need to collect as much information as possible. These can be texts, calculations, statistics, metrics, or historical chronicles. The simplest example of such data collection is a captcha. 

This includes the characteristics that ML focuses on during training. Often before developing machine learning algorithms, engineers need to label the data. For example, you need to teach the system to identify horses in a photo. Data labeling specialists indicate a specific object, a horse, in thousands of images. The prepared data form a dataset. To train a more complex model, more data need to be prepared. 

A trained ML model loses its accuracy over time. When errors appear in its work, specialists retrain and retest it. So, the ML mechanism is clear. And what is machine learning business value?

In 2020, 34% of organizations in the US, Europe, and China were using AI and ML technologies. By 2024, the machine learning market is predicted to grow by 42%. 

McKinsey researchers found that artificial intelligence and machine learning can increase the productivity of certain industries from 30% to 128% (see the infographic below). That is why they are interested in developing machine learning algorithms.

Every day people use machine learning but don’t think about it. Email spam filtering, iPhone face recognition technology, Facebook tagging, and so on. They make things easy, don’t they?

Businesses use more complex ML models. Naturally, the amount of their “help” is larger. 

According to McKinsey, AI and ML in business can increase the financial performance of companies from different sectors.

Such massive shifts are due to the amazing versatility of machine learning. It helps businesses in the following ways:

A trained algorithm increases the profit of a company because it analyzes the behavior of customers. To those who are not marketers, this feature may seem unimportant. But one way or another, ML affects sales. 

For example, a film studio releases a trailer for a melodrama and monitors how it is spoken about on the Internet. The company analyzes the reaction of the audience and makes changes in the video. As a result, the audience gets what makes them buy a ticket to the cinema. 

A developer releases a new game from the popular series, but without a game mode. Fans who have been waiting for this opportunity are criticizing the creators on social networks. The company studies their reviews, postpones the release, and connects the game mode. Now, the game is selling like hotcakes. 

In the first and second cases, the managers involved a “detective” – machine learning. The technology determines the language of reviews, key phrases, and even sentiments. As a result, the customer receives sorted reviews and uses them to improve the product. 

When a company knows the behavior of customers, it is easier for it to predict demand for goods. ML technology analyzes information about past purchases and finds patterns: how consumer demand depends on the season, the emergence of new products, promotions, and other conditions. 

Then the system predicts which products should be purchased more next month and which can be ignored. So, the store does not delay orders during peak periods since the products are over. 

Thanks to ML, Amazon sends an average of 1.6 million packages per day without failures and backlogs. And Costco ML helps you to analyze sales history, advertising, weather, and holiday information to predict the demand for baked goods. The store works with fresh products and throws away a lot of waste if they are not bought. With ML, the company reduced waste and deployed the SAP system in 500 bakeries. 

ML processes customer data: purchase history, shopping cart size, search queries, clicks, and so on. 

Based on this information, the technology determines who will buy certain goods in the nearest future. It knows what kind of personalized advertising to offer to the client so that they will surely follow the link and purchase the product. 

ML offers customers more personalized service. This is important because 8 out of 10 regular customers will only buy from companies that personalize ads. And 90% of consumers are annoyed by irrelevant ads. 

The streaming service Spotify uses machine learning to personalize playlists for listeners based on their preferences. This made Spotify the world’s largest music streaming service with 365 million active users. 

ML improves the performance of industrial enterprises, utilities, and transport companies. This is how it happens: 

IoT sensors collecting data are installed on the equipment.

 ML analyzes information that comes from IoT sensors. 

The technology “notices” anomalies in the operation of the equipment and warns the manager. 

The manager solves the problem before the equipment breaks down. 

For example, a new machine performs 150 movements per minute. When it slows down and makes 140 movements per minute, the sensor sends a signal to the system. Algorithms detect non-compliance with the established norm and warn operators. The machine is not broken but needs maintenance. An engineer will repair the machine before the production line stops and the factory loses money. 

This mechanism of work is used in various industries. In logistics, it prevents trucks with cargo from breaking down on the way to the customer. In public utilities, it helps to avoid accidents at water pipes or power plants. In air transportation, sensors warn of wear on aircraft parts. Specialists repair equipment before a breakdown occurs. It is safer for people and more economical for companies that do not need to spend money on eliminating large-scale consequences of accidents.  

Deloitte calculated that preventive maintenance increases productivity by 25%, reduces breakdowns by 70%, and cuts maintenance costs by 25%. 

ML is changing the way manufacturing is done. The technology analyzes the operation of the equipment. It offers solutions on how to speed up individual operations in production. 

Google used machine learning in business to control the air conditioning system in server farms. As a result, the company began to spend 40% less electricity. 

The oil company Exxon Mobil relies on AI and ML to collect oilfield data and safely drill wells on the ocean floor. At this rate, the company will increase production by 50,000 barrels of oil equivalent per day by 2025. 

General Motors is building lighter, more reliable vehicles with AI and ML. Engineers enter the main parameters of the model into the program: materials, strength requirements, weight, and a possible method of parts manufacturing. The system offers hundreds of original design options. So, the automotive giant reduced the weight of the car by 40% and increased its strength by 20%. 

Based on ML, effective diagnostic tools and treatment plans are developed. IoT devices track everything from blood pressure and sleep patterns to oxygen and sugar levels. This data is collected and sent to an application for a doctor. The doctor prescribes treatment based on them. 

Algorithms find cancerous tumors in mammograms and detect skin cancer. They analyze the retina to recognize diabetic retinopathy. High-risk patients are identified from medical records and readmissions are predicted. 

For example, the Corti program recognizes cardiac arrest by an emergency caller’s voice and breathing. The system does this 20% more accurately and 31 seconds faster than a human.

An ML-based face recognition program successfully registers people in a hospital or clinic. A patient approaches the reception desk, and the administrator immediately issues a referral to the necessary doctor’s office. This solves the problem of queues and simplifies the work of operators. 

Not surprisingly, the AI and ML healthcare market will grow to $34 billion by 2025. Nowadays, machine learning app development companies are more relevant than ever.

Every minute, any company can become a target for hackers. Phishing, malware, DDoS, SQL injection, zero-day exploit… These are just a few of the cyberattacks that may threaten an organization. 

To protect their assets, organizations use the mechanisms of control and fight: firewalls, threat management applications, strict data retention policy, and more. 

Machine learning in business is becoming a valuable cybersecurity mechanism. Algorithms analyze an application and know the buying habits of customers. When a consumer pays for a product, the technology determines the transaction as legitimate or fraudulent. It notices abnormal purchases and alerts the bank. In turn, the bank contacts the cardholder and warns about identity theft. 

For example, researchers at the Massachusetts Institute of Technology have created a system based on ML and artificial intelligence called AI2. Engineers tested the program on 3.6 billion pieces of data. As a result, the application predicted cyber threats with an accuracy of 85% and reduced the number of false positives by five times.

Algorithms solve simple and complex problems of financial analytics. For example, they evaluate the company’s expenses, help to trade stocks on the stock exchange, or assess the solvency of bank customers. 

More than half of hedge funds use AI and ML to make investment decisions. Two-thirds of managers generate trading ideas and optimize portfolios. 

The mortgage giant Freddie Mac is also looking to capitalize on machine learning algorithms development. A model evaluates the solvency of customers. It determines which applicants with low credit scores can apply for a loan. 

We have mentioned the most common use cases for machine learning in business. The scope of ML application is huge: from sentiment analysis to smart grid management. To understand which of these technologies will improve your business, you need to do research and consult with ML development experts. They know how ML is changing businesses firsthand. Data scientists and analysts can create AI models for the needs of different industries.