Machine Learning and How it can Help Businesses

Published on: 15/March/2020

Machine learning is the most significant technology for future businesses. It’s because AI-powered software is already assisting businesses in increasing productivity, improving customer interactions, and increasing revenue.

Additionally, machine learning also enables an entrepreneur to do many more activities with the time they have available to devote to the market, resulting in significantly improved business efficiency.

Hence, in this blog, we’ll examine how machine learning may help businesses of all sizes.

What is Machine Learning?

Machine learning is a cutting-edge new field that combines fundamental aspects of mathematics, statistics, and artificial intelligence (AI). It is created to develop a system that is more than the sum of its parts.

The core idea of artificial intelligence and machine learning is that engineers should do more than build software to do a particular task. It should create an algorithm that teaches a computer how to build its code.

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More importantly, the software should be “intelligent” because it can learn from previous data and interactions. AI-powered software can author programs, learn from past events, and provide proactive solutions for the future.

Businesses employ machine learning to use the massive amounts of data they have collected to produce actionable forecasts that management can use to invest resources and expand their business.

7 Ways Machine Learning Helps Businesses 

 Manufacturing

Machine learning technology has already been integrated into all stages of production in the industrial business.

Since AI-driven technology may help organizations save money by improving inventory management, it can also help increase production efficiency and anticipate equipment faults before they occur.

The vast volume of data collected every day provides one benefit for the industrial business. Innovative businesses like Seebo hire Python developers to construct cutting-edge data analytics applications. In these systems, machine learning is used to predict annual production peaks and troughs and advise process changes. They also develop cost-effective maintenance programs to assist businesses in avoiding unplanned interruptions.

According to McKinsey, machine learning will help industrial companies decrease material delivery times by 30 percent and save 12 percent on fuel by improving their operations. The company also forecasts that businesses may raise their gross revenue by 13% if they completely incorporate AI-driven solutions into their operations.

Deloitte estimates that machine learning might save businesses millions in a maintenance program. According to Deloitte, AI-driven systems may help firms reduce unexpected downtime by 15% to 30% and service expenses by 20% to 30%.

Consumer Data

Executives are particularly interested in how the increased collection and evaluation of customer data will affect earnings and future growth. Businesses have spent decades accumulating billions of data points on their clients, including information such as purchasing patterns, ethnic characteristics, income, and more.

AI-powered software is finally allowing these businesses to make use of this data. Executives are working with Python software development firms to create cutting-edge data analytics software to collect data and make valuable and actionable forecasts.

Machine learning is used by the online retail site Etsy to improve the user experience. The corporation used the technology to develop personalized client profiles, improve search results, and enhance user interface design.

The company’s unique use of analytics is one of the reasons it has attained yearly revenues of $603 million while facing intense competition from larger retail giants such as Amazon and Target.

Netflix is also another business that has effectively exploited AI-driven technologies. The internet streaming platform uses machine learning to create detailed view profiles that correctly forecast which episodes and movies consumers will be interested in. Every time customers scroll through new videos, they engage with our application and offer vital data.

Simplifies Time-Intensive Documentation in Data Entry

Organizations looking to automate their data input process face considerable data duplication and inaccuracy challenges. Predictive modeling and machine learning algorithms, on the other hand, have the potential to improve significantly in this scenario. Machines can now do time-consuming data entry chores, freeing up your experienced personnel to focus on other value-added work.

Accurate Lifetime Value Prediction and Improving Customer Segmentation

Segmenting customers and estimating lifetime value are two of the essential concerns for marketers today. Sales and marketing departments will have access to massive volumes of relevant data derived from multiple sources, such as lead data, web traffic, and email campaigns. On the other hand, accurate forecasts for incentives and specific marketing offers are simple to produce using ML.

Marketing experts are now leveraging machine learning to minimize the guesswork associated with data-driven marketing. For instance, analyzing data reflecting a specific group of users’ behavioral patterns during a trial period would assist organizations in estimating the likelihood of conversion to a paid version. Such a methodology starts customer interactions to better engage consumers in the trial and encourage them to convert early.

Product Recommendation

Product recommendations, such as upselling and cross-selling, are significant elements of any sales and marketing approach. ML models will assess a client’s purchasing history and, based on that, identify the goods in your product inventory that the consumer is interested in.

The program will detect hidden patterns among the items and put similar things into clusters. It is referred to as unsupervised learning, and it is a form of ML method. A model like this will allow firms to provide better product recommendations to their consumers, encouraging product purchases. In this method, unsupervised learning contributes to developing a superior product-based recommendation system.

Logistics

The logistics and retail industries quickly become data analytics and machine learning professionals. Their success is frequently dependent on extracting every last cent from every item.

Machine learning helps businesses improve their logistics by increasing efficiency at every stage of the shipping, storage, and sales processes. This technology also enables forward-thinking enterprises to include self-driving vehicles in their fleets.

Most the international shipping today used machine learning to improve profitability. Thousands of components are installed on cargo ships, long-haul vehicles, and smaller equipment by these businesses. It assists management in identifying breakdown characteristics and developing preventative maintenance programs to keep their ships and vehicles moving.

Moreover, machine learning is also being pioneered by retailers such as Amazon. The online retailing behemoth is using machine learning to improve the efficiency of its delivery network and predict customers’ demands.

Improvements of Security

The world has become increasingly reliant on web services as web-based technologies have developed. Therefore, people’s lives are getting more integrated and comfortable. However, certain risks are connected with it, such as Phishing attacks, Identity theft, Ransomware Data breaches, privacy concerns, and more. 

Businesses use a variety of protection like antivirus software such as Avast Security Pro and control methods to protect the safety of their customers and employees. Examples are firewalls, intrusion prevention systems, threat management programs, and robust data storage standards. Dedicated security teams at large corporations continuously monitor, update, and repair vulnerabilities in web applications.

Since machine learning is continually growing, the more emails an algorithm evaluates, the more accurate the filtering gets. By implementing ML into their spam filter, businesses can significantly minimize the amount of spam or harmful emails in employee inboxes.

Threat assessment is another example of this, where most web applications face several different threats.

Before deploying an application to a production environment, development teams may include ML in the application testing process to analyze software vulnerabilities.

Conclusion

Machine learning is increasingly becoming a key technology integrated across several business sectors. Most organizations implemented this to address complicated business challenges while increasing an organization’s performance and scalability.

Even after all of the challenges involved with appropriate ML adoption, organizations are eager to engage in this time-consuming and potentially expensive process. It gives natural and significant advantages over any conventional intellectual approach.


AUTHOR:

Jennysis Lajom is an IT graduate and a fan of Korean dramas. Her interest in digital marketing motivated her to pursue a career in content writing, editing, and social media marketing. She is also a resident SEO writer for Softvire Australia and Softvire NZ.