5 Advantages and Real-Life Use Cases of Machine Learning for Businesses

Machine Learning has taken over the business world. It is one of the most popularly used technologies which has a profound effect on numerous business sectors and continues to touch the lives of billions of people every day. Machine learning has enabled technological advances making it possible to develop new kinds of tools some of which were simply unimaginable a few years ago.

The implementation of machine learning in business processes fuels the ground-breaking inventions that underpin our contemporary way of life.

Machine Learning: Overview

The phrase "machine learning" is used to refer to a wide range of methods and tools that enable computers to learn and evolve independently. Without being expressly designed, machine learning techniques allow AI to learn on its own.

Machine learning algorithms are able to make predictions and carry out tasks without reference to any explicit programming instructions since they have learned the pattern from the incoming data. There are many scenarios when rigorous algorithms simply won't work, and machine learning is the only hope. Based on historical data, it will automatically implement the newly learned procedure.

The assistance we receive from our email providers in the fight against spam is one of the common examples of machine learning implementation. Inbound garbage email is automatically identified by a spam filter's algorithm and routed to the spam folder. Some online merchants utilize machine learning algorithms in tandem with other information technology security solutions to curb fraud and boost recommendation engine effectiveness.

Why Should Organizations Explore Machine Learning?

Improved Rate of Decision-Making

Machine learning facilitates quick decision-making by increasing the speed with which firms can process and evaluate data. For instance, a data breach can be immediately detected by a machine learning-based program that has been taught to recognize irregularities in a company's security environment and send an alert to the IT department. These platforms allow businesses to protect their customers' information, maintain their good name in the market, and avoid expensive damage control by acting swiftly and implement effective restoration.

Better Demand Prediction

Companies need to foresee shifts in the market and consumer preferences in order to remain competitive in today's dynamic business environment. Better demand forecasting means more efficient inventory management and significant savings for organizations that use machine learning models as part of their data analytics.

Customer Churn Analysis

A business spends a lot of money in acquiring customers and when they quit, the loss is double. Analyzing the customer churn rate which is also called as attrition rate can help them understand the reason why customers are leaving and what they can do better in retaining them. This will also help them in reducing the cost of customer acquisition by 5 percent as studied by Harvard Business Review.

However, if the customer churn rate is calculated wrongly it can impact negatively to the business. Machine learning models can help businesses here in evaluating this.

High Productivity

Machine learning helps firms save time and effort by automating mundane operations so workers may focus on more strategic initiatives. For instance, machine learning technology is capable of doing extensive document scans in a fraction of the time it takes humans to execute scanning and cross-referencing procedures. Having these features at their disposal helps businesses save money on information retrieval tasks like those involved in legal research and regulatory compliance.

Predictive Maintenance

Practices like preventive and corrective maintenance are important and common in the manufacturing industry to prevent any failure. However, these practices are very time-consuming and wasteful. But machine learning can help businesses in this field implement predictive maintenance strategy to check breakdowns and save unnecessary expense on repairs.

This can be done by mining production data to gain hidden insights and patterns. Using a combination of past data, a workflow visualization tool, a configurable analytic environment, and a feedback process, a machine learning framework can be developed.

Use Cases of Machine Learning in Business Processes

Despite being around for quite some time, machine learning is now more popular than ever in the age of Big Data. Why? Our systems are constantly producing an enormous quantity of data, and businesses require assistance sorting through and making sense of this information.

Machine learning allows firms to create automated models that can swiftly analyze large amounts of data and "learn" to apply this data to a variety of situations. Let's have a look at how machine learning is being put to use in diverse areas of a business.

Suggestions Regarding Products

Today, the way customers interact with platforms have changed. Rather than putting much time and effort in searching the ideal product on shopping sites they love to give a try to the products recommended to them.

Because those are the one matched with their interest and budget. How these recommendations are made? How do shopping sites know what their customers are expecting or would most probably buy?

AI-based machine learning recommendation engines are the answer. They analyze customers at each point, monitor their purchase patterns, usage rate, personality, lifestyle, interests, income, and more.

Then these recommendation engines identify the best products their customers may be interested in.

Amazon is the best example of it. The platform provides suggestions to the customers via different ways. It displays similar or better products via recommended for you, recently viewed products, bought together sections.

Not just e-commerce platforms, streaming platforms like Netflix is working on the same approach. It uses system based on machine learning algorithms to recommend the best content according to popularity, trend, or interest to viewers from different group.

Image Recognition

E-commerce, healthcare, manufacturing, transportation,security, telecom, to education, there is no single industry where AI image recognition is not used. It is one of the applications where machine learning is used in combination with artificial intelligence and a camera to provide a vision to computer.

Take an example of airport’s safety and security system. Whenever a passenger’s luggage enters the screening machine, the system checks whether the luggage is safe to pass or contains any dangerous object.

In manufacturing industry, machine learning has several applications, one such is AI-based vision inspection system that can inspect every component or product, right from the packaging material to the end product.

It helps them detect even the minor defects which cannot be seen with naked eyes and defects that are not pre-defined in conventional inspection method.  

In short, AI/ML based inspection systems give smart eyes to the manufacturing industry.

ML in Drug Discovery

Drug discovery is the complex process where achieving success is very tough. With the availability of huge data sets and machine learning and artificial intelligence, the chances of producing effective medicines at the right pace has become easier.

The new approach now helps researchers and scientists in medicine discovery, right from the initial stages to identify the right type and proportion of chemical to be used.

Fraud Detection in Financial Sector

Identity theft, credit card theft, email phishing, forgery of documents, and mimicking the buyers’ behaviour are some of the common fraudulent activities that affects both, customers and financial institutions.

With the help of AI and machine learning solutions, they are now shifting their approach to tackle such scams and provide a safe environment to the customers. How?

They are implementing AI-powered neural networks and machine learning algorithms to train their systems for fraud detection by suggesting risk rules and test them on the historical datasets.

When any unusual behaviour happens on the customer’s side for example, credit card theft, then the system automatic blocks the card to prevent the transaction.

Predictive Analysis for Marketing

Machine learning can be a great help for marketers in planning and implementing an effective marketing strategy. When coupled with predictive analytics, they can understand the customers behavior, qualify and prioritize leads, identify the right advertising channels and marketing resources, and get success in customer retention too.

Mastercard utilized the IBM’s AI-based platform that uses ML-based predictive analysis to target audience for their cancer awareness campaign- Stand Up to Cancer. The platform first detected which engaging elements got the most response from audience. They segmented the audience based on the geography, time, and device type.

Then they strategically decide a marketing strategy for each target audience. As a response, their CTR increased by 144 percent and they learned which CTA’s worked better for them.

This is the power of ML-based predictive analysis. It can be used in different industries like manufacturing to detect on-site failure, weather-forecasting to predict natural disasters, etc.

Conclusion

With its potential to solve difficult business challenges and boost efficiency and scalability, machine learning is quickly becoming a key technology that is spontaneously deployed across many business sectors. Although there are lots of It helps them to learn the customers behavior, understand important operational patterns, and accordingly develop the new products or plan the strategy. Although there are lots of machine learning frameworks but a few of them are best. There are many challenges in implementing machine learning effectively, it is worthwhile for firms to do so because it offers significant improvements over traditional systems for data analysis.

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