Machine learning (ML) technology is increasing efficiency and improving accuracy across business functions, from decision making to maintenance to service delivery.

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Benefits of machine learning

Machine learning - a subset of artificial intelligence AI , includes software systems that are capable of analyzing data, providing insights, and then acting on that analysis.

During data analysis, machine learning also continuously “learns” to improve accuracy over time.

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Machine learning is a powerful, versatile technology that powers many everyday services, from online product recommendations to customer service chatbots. In fact, many of the AI ​​tools used by businesses today are specifically related to machine learning, experts say.

The benefits of machine learning are grouped into four main categories:

• Increase productivity or optimize processes.
• Improve the quality of work.
• Help employees, customers, and other stakeholders have better overall interactions.
• Create new products, services and market opportunities.

According to Rackspace Technology's "The 2023 AI and Machine Learning Research Report," 72% of more than 1,400 survey respondents said AI and machine learning have become part of their business and IT strategies. About 69% of respondents described AI/ML as a technology with a high priority for use.

Companies that have adopted the technology report that they use it to improve existing processes (67%), predict business performance and industry trends (60%), and mitigate risk (53%).

Going forward, companies will continue to invest and deploy machine learning technology to support growing workloads at optimal cost.

12 Popular Applications of Machine Learning Today

While there are countless real-world use cases of machine learning, experts highlight the following 12 as the top applications of machine learning in business today.

1. Chatbot

Most people have interacted directly with machine learning in the form of chatbots. This type of software program uses machine learning and natural language processing (NLP) to mimic human conversation.

They process programmed scripts to engage individuals and answer their questions by accessing available databases, finding answers to those queries.

The first generation of chatbots followed established rules, performing necessary actions based on keywords. However, ML allows chatbots to interact with users more effectively.

Today, chatbots are better at responding to user needs, responding accurately, and providing a more human-like feel to the conversation.

2. Recommendation Engine

Machine learning applications power recommendation engines most commonly used in streaming services and online retail.

Here, algorithms process data — such as a consumer's past purchases, a company's current inventory information — to determine which products or services are appropriate to recommend to a customer.

Recommendation engines enable personalization of experiences, customer retention, and help businesses increase sales by providing products and services that are more relevant and accurate to what each customer likes and wants.

3. Flexible pricing

Machine learning allows sellers to adjust the prices they charge for products and services in near real time based on changing market conditions. This approach is called dynamic pricing.

Machine learning systems often use multiple datasets, such as social media and macroeconomic data, to calculate and reset prices.

This is commonly done for airline tickets, hotel rates, and ride-sharing fares. Prices that increase when demand increases and vice versa is a prime example of how businesses use ML algorithms to adjust prices as circumstances change.

4. Support marketing and sales activities

In many organizations, sales and marketing teams are the biggest users of machine learning. The technology supports much of their day-to-day operations. ML is often integrated into back-office software, such as customer relationship management systems.

Modeling customer churn

Machine learning technology is used to identify which customers are likely to be dissatisfied with a product, when that happens, and how to turn such situations around.

To do that, the algorithm must accurately identify patterns in large volumes of historical, demographic, and sales data to understand why consumers don't return.

Machine learning then continues to analyze the behavior of existing customers, alerting them to customers who are at high risk of churning, identifying the reasons, and suggesting steps to take to retain them.

Customer segmentation

Companies divide their customers into specific segments based on common characteristics such as age, income, or education level. This allows marketing and sales to tailor their services, products, advertising, and messages to each segment.

Additionally, machine learning supports sales by helping customers get the best price for their products, while ensuring they deliver the right products and services, at the right time, in the right place through inventory planning and customer segmentation.

5. Fraud Detection

Machine learning fraud detection applications are commonly used for banking and financial services, where organizations use the technology to alert customers to potential fraud on their credit and debit cards.

Machine learning's ability to understand patterns and instantly detect anomalies that fall outside of established patterns makes machine learning a valuable tool for detecting fraudulent activity.

Data scientists use machine learning to understand typical customer behavior, such as when and where customers use their credit cards. Machine learning takes that information and other data to determine in milliseconds which transactions are within and outside of normal ranges.

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6. Detect network threats

Machine learning's ability to analyze complex patterns in large volumes of activity helps identify normal and abnormal behaviors, thereby detecting cyber threats.

Furthermore, machine learning's learning capabilities allow it to continuously improve its understanding of an organization's IT environment, network traffic, and usage patterns.

So, even as IT environments expand and cyberattacks increase in both number and complexity, ML algorithms continue to improve their ability to detect anomalous activity that indicates intrusion or threat, enhancing cybersecurity .

7. Optimization

Thanks to machine learning algorithms, businesses can analyze data and run simulations to determine optimal solutions, supporting decision making and action.

ML is used to optimize across all areas of business operations, from finance to software development, especially in supply chain, manufacturing and logistics, helping to speed up work and minimize human error.

8. Decision support

In assessing the level of benefits that ML brings, decision-making capabilities ranked fourth after innovation, cost reduction and efficiency improvement. Among them, Decision Support System (DSS) also helps to cut costs and improve efficiency.

To perform this function, ML algorithms are trained on historical and other relevant data sets, allowing them to analyze new information and consider many possible scenarios at a scale and speed that humans cannot match. The algorithms then make recommendations on the best actions to take.

9. Predictive maintenance

Supporting predictive maintenance is another long-standing use of machine learning. The company’s machine learning system takes data from various sources (historical operational data, performance data from IoT devices , supply chain data, market intelligence, etc.) to predict the optimal time to perform equipment maintenance.

Predictive maintenance differs from preventive maintenance in that predictive maintenance can determine exactly what maintenance should be performed at what time based on many factors.

This minimizes the impact of equipment downtime, while maximizing equipment investment by eliminating unnecessary maintenance schedules and equipment usage.

10. Quality monitoring and assurance

Machine learning has the ability to understand and discern data patterns at a scale, speed, and level that humans cannot match, which is especially useful in demand monitoring and quality assurance.

Machine learning is being used to monitor supply chain operations. Additionally, ML technologies such as deep learning , neural networks , and computer vision can be used to more efficiently monitor production lines and other outputs to ensure products meet established quality standards.

11. Sentiment Analysis

In this application, the machine learning model scans and analyzes human language to determine whether the expressed emotion is positive, negative, or neutral. The ML model can also be programmed to rate sentiment on a specific scale.

Companies often use sentiment analysis tools to analyze the content of reviews from customers as they interact with the company. This also helps the company know how to respond more appropriately to consumer needs.

12. Information mining

Information retrieval and extraction systems - built with ML technologies such as NLP, optical character recognition, and intelligent character recognition - automatically identify key pieces of structured data from documents even when the information is stored in unstructured or semi-structured formats.

This technology can also be used with speech-to-text conversion.

Using machine learning in this case brings greater efficiency and improved accuracy to document processing. It also frees humans from tedious, repetitive tasks.

In addition to the popular applications mentioned above, ML is also used to solve many other problems in business and will bring many more practical benefits in the near future.

If you want to introduce MC, we recommend EDUTO SOFTWARE

While it might seem daunting, EDUTO SOFTWARE offers a user-friendly platform that makes AI development accessible to everyone. With no programming experience required, you can create sophisticated algorithms using simple drag-and-drop operations. One of the biggest benefits of using EDUTO is that it seamlessly integrates into your existing systems, eliminating the need for costly infrastructure changes. 

If you want to introduce AI, we recommend EDUTO SOFTWARE

This means you can reap the rewards of AI without breaking the bank. In the US, where labor costs are often high, AI can help streamline operations and improve efficiency. Don't let concerns about complexity or expense hold you back. Take advantage of the opportunities that AI presents and start your journey with EDUTO today.