6 real-world examples of Machine Learning

Typically, Machine Learning is used to solve business problems in various sectors and areas where different algorithms are applied to automate processes and suggest the best solutions adapted to the needs of the companies.

This area of AI was developed from the mixture of different areas of knowledge such as biology and neurology, mathematics, statistics, computer science and robotics, among others.

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Machine Learning can, from a set of data and a target, learn to optimize the target. With new data it will give new results that will normally be used under human supervision. The more data it processes, the better it will adapt to the user's needs for a given product or service.

There are many factors that can affect the selection of the appropriate algorithm, i.e. the quality of the data or the correct selection of the algorithm depending on the data you have and the problem you want to solve or optimize.

Examples: Where is Machine Learning used?

This innovative area is used in many sectors: finance, business, science, marketing, IT... So let's look at some of them. examples of Machine Learning.

1. Recommend products

One of the examples in Machine Learning used today is personalization of purchases and product recommendations. They are one of the most important trends in e-commerce. Thanks to the use of artificial intelligence and the processing of huge amounts of data, you can thoroughly analyze the online activity of thousands of users. 

Depending on the information collected, the following can be done create product recommendations tailored to a customer or group specific.

By analyzing the data collected on the current traffic on the store's website, it is possible to determine on which pages the customer was active. 

You get an idea of what he was looking for and where he spent most of his time.. Based on various information, such as previous activity profile, preferences (e.g. favorite sneakers), internet activity or location, the algorithm will automatically recommend products that may be of interest to the shopper. Not only recommend the product, but also how to recommend it: color, promotion or payment method best suited to that customer.

It should be noted that the algorithm will be more effective the greater the number of similar customers it has analyzed.

2. Content customization

Properly personalized content on a website or mobile app increases conversion and customer engagement. As in the case of products, the algorithms of Machine Learning can find patterns in customer behavior and tailor content of the website accordingly.

3. Price optimization

Another great example of Machine Learning is price optimization, as it is one of the most important factors in making a purchase decision. Customers often compare the cost of products to find the best deal. 

Many stores or hotel establishments have recognized the benefits of introducing dynamic pricing. Of course, the actual process of customizing them is not easy, as there are many factors involved. 

Also Machine Learning algorithms can analyze issues such as competitor price, product demand, day of the week, time of day, type of customer, etc. 

This will enable them to detect effective patterns that will enable them to determine when and how to adjust the price.

A clear example here is Booking where they implemented Machine Learning to determine prices based on concrete patterns and a wide variety of variables in order to provide optimal rates for each day and thus maximize their profits.

4. A/B Testing

A/B testing allows you to adjust even the smallest elements of a website (such as the color of the "Buy Now" button) to ensure the highest possible sales and the best shopping experience. 

Unfortunately, implementing it requires a lot of time and the data obtained must also be properly analyzed. Therefore, Machine Learning gives some benefits such as:

• Automation of the process of selecting a page element to be improved through A/B testing. As a result, the time spent on optimization will be optimally rewarded.
• Automatic segmentation of customers into groups based on their characteristics (age, gender, budget, etc.). Different types of customers may react differently to changes made to the Web site. What may be beneficial for one group may not be advantageous for the other groups. However, the various examples of Machine Learning algorithms will be able to detect these subtle differences.
• Thanks to the algorithms, you will be able to find the best combination quickly and more efficiently.

5. Fraud detection for secure transactions

The greater the amount of data, the more difficult it is to detect anomalies. However, this abundance of data is an advantage for Machine Learning algorithms; they can detect changes in patterns and determine what the behavior is "normal"and alert managers when suspicions arise.

According to a studyFor every dollar lost to fraud, banks and other financial organizations spend nearly 3$ for every dollar lost in fraud as a recovery cost. Machine Learning has a wide range of use cases and applications in this area. Machine Learning techniques are applicable in different models to improve the security of transactions by early detection of fraud possibilities. 

This area can create predictive models capable of predicting fraudulent behavior in real time to constantly verify the possibility of fraud and generate alerts accordingly. In addition, classification algorithms are developed that can effectively label events as fraudulent or suspicious to eliminate the possibility of fraud. Currently, large banks already use anomaly detection, fraud and risk management systems. The outlier detection system based on artificial intelligence and machine learning improves security levels.

It may indicate a product that most likely suits our tastes, but it will not explain the motivations for a particular decision. For example, if two objects fall: one is very heavy and the other is light, the person interprets it in terms of greater or lesser air resistance. The computer will not draw such conclusions, but stores the properties of both objects.

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6. Other examples of Machine Learning in everyday life

Every day we can observe many examples of how machine learning works. Here we bring you other cases in summary mode:

• Machine Learning is also present in GPS. For example, Google Maps, with it, analyzes traffic in real time or predicts the arrival time if you leave at a different time, based on the estimated traffic at that time
• Detection of cybersecurity anomalies (protection against phishing emails, bots, facial detection, malicious websites, etc.).
• In e-mails, the auto-fill of the recipient or text when writing a message as labels or categories.
• Machine learning is present in social networks since with the established patterns it can eliminate offensive comments or specific words. It is also able to delete spam accounts or recommend relevant users/images.
• Financial advice and portfolio management, regulation of healthcare efficiency and medical services to determine the level of access employees have, translation from one language to another...

Conclusions

As you can see, Machine Learning has great potential. So don't be afraid to invest in solutions related to this technology, as they can greatly help in the development of your business and explore Machine Learning with GAMCOIf these topics are interesting to you and you want to deepen in concepts you can access our artificial intelligence glossary