10 Common Uses for Machine Learning in Business

Imagine you want to buy a new car. You type a query into a Google search, scroll through a few pages, and put your idea aside for later. Then things get interesting. You see ads for cars for sale on social networking sites. You get emails with great deals from companies that sell cars. Strange, isn’t it? 

How so? How do all these services know what to advertise for you and what you are interested in right now? That is how machine learning (ML) and artificial intelligence (AI) work. 

And if you are a business owner, these technologies will provide you with many benefits, including workflow automation, data processing, analysis acceleration and simplification, forecasting, avoidance of risks, etc. 

The essence of machine learning is to transform raw data into predictive models via algorithms. In this article, we will try to shed light on how to use ML in business. 

10 Interesting Machine Learning Applications in Business

If you think that machine learning is something new, you are wrong. The technology was developed in 1959 when the American programmer Arthur Samuel decided to write a self-learning checkers-playing program. However, ML has become widespread only in the 21st century. Nowadays, its popularity is constantly increasing. 

Machine learning and artificial intelligence are widely used in marketing, manufacturing, medicine, banking, logistics, multimedia, etc. Let’s come to the point and look at the most effective use cases.

Personalized Recommendations

Astoundingly, it’s a fact that Spotify recommends highly relevant music tracks, while Netflix suggests movies that match user requests. How is this possible? These platforms use machine learning. Based on previous user experience, algorithms select the most appropriate content. Retail works the same way. 

Algorithms analyze various data (viewed products, items in the cart, purchases) to offer consumers exactly what they need.

Fraud Detection 

There are many ways to lose your money due to fraud. Attackers have learned to steal passwords and personal data, to hack accounts. Machine learning helps to combat these actions. 

For example, in MasterCard, the program analyzes information (geolocation, purchases, transactions) to protect users’ money. And in the German Danske Bank, specialists have developed algorithms to detect suspicious transactions and block them immediately.

Security Treatment

Machine learning is widely used in the security industry. Algorithms are trained to detect malicious software in encrypted traffic, recognize internal threats, and protect user data in cloud storage. ML can work with large amounts of data and perform such tasks:

• assessment of potential threats in the network;
• detection of privacy policy violations;
• identifying malicious websites, files, and activities;
• IP reputation analysis;
• ensuring user safety while surfing the Internet, etc.

AI/ML, compared to security workers, can process much larger amounts of data and do it much faster. Algorithms spend only a few milliseconds searching for and blocking potential dangers. ML protects user data at all levels, allowing IT professionals to focus on more complex tasks.

Forecasting Opportunities 

Using machine learning, you will be able to predict customer demand for certain products/services. When analyzing data, models and algorithms consider such parameters as:

• seasonality;
• weather forecast;
• product characteristics;
• geolocation, etc. 

For example, Morrisons stores (Great Britain) operate algorithms that are trained to determine the demand for goods taking into account the time of year and holidays. Thus, it was possible to avoid the problem of products stocks shortage in warehouses and increase profits by 3 times.

Decision-making Streamline

Every business operates with many documents, the manual processing of which requires a lot of time and effort. Algorithms make it easier to work with documents. Using ML, you can quickly get the information you need to make the right decisions based on it.

Financial Analysis 

The financial market also reaps many benefits from the implementation of machine learning. Traders, brokers, lenders, and investors can use algorithms to solve their problems.

Let’s consider a practical example of the use of a neural network. The program can quickly and accurately determine the solvency of a client who wants to get a loan. Algorithms analyze data on previous debts, spending much less time on a loan decision than a manager.

Content Moderation

The amount of textual and visual content on websites is growing exponentially. A person must spend a lot of time sorting, analyzing, and processing it. Consequently, businesses should spend more money on organizing content moderation. 

The task is greatly simplified if you use machine learning. For example, CoStar processes content using Amazon Recognition’s Content Moderation API platform.

Data Segmentation 

Machine learning has been successfully used to segment and standardize data. For instance, you can segment customers based on criteria such as age, gender, interests, health status, lifestyle, etc. 

What is all this for? You will be able to divide your target audience into smaller subcategories, and based on the results, you will interact more effectively with consumers, offering them exactly what suits their needs in a particular period.

Medical Diagnosis 

Medicine is another area where machine learning is being implemented. Algorithms can process clinical symptoms, test results, and the patient’s medical history. ML tools use this data to:

• making accurate diagnoses;
• identifying patients who are at risk;
• treatment protocol development;
• prescribing medications that can have the best effect;
• hospitalization risk prediction, etc.

State hospitals, diagnostic centers, and private clinics are increasingly turning to machine learning. The technology is used not only for diagnostics. For example, scientists at the University of California have taught a robot to sew up wounds on a model and are now teaching it to perform similar operations on living tissues.

Cost Savings

Companies spend a lot of money on advertising and marketing. How to optimize costs? How to get the most out of the smallest budgets? Again, use machine learning. Algorithms analyze the behavior of potential buyers and determine the interests of the target audience. Using the received data, marketers can create relevant content, send discount coupons to customers, and set up effective advertising.

Final Thoughts

We have covered only the tip of the iceberg, but we hope you are convinced of the need for machine learning for business. Anyone who knows all the ins and outs can easily adapt machine learning tools to the goals and needs of a particular business. It is important to understand that ML/AI is not a panacea that works under any circumstances.

Do not deprive yourself of the benefits provided by machine learning and artificial intelligence. Think about using these innovative technologies today to go with the times and effectively achieve business goals.

Let’s drive innovation in your company! Share your business needs and we will help you develop the perfect-fit solution for your company.