PROMOTING BUSINESS GROWTH THROUGH MACHINE LEARNING

Machine learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. It is evolving at such a rapid rate and is mainly being driven by new computing technologies. Machine learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics community. Factors such as growing volumes, easy availability of data, cheaper and faster computational processing, and affordable data storage have led to a massive machine learning boom. Therefore, organizations can now benefit by understanding how businesses can use machine learning and implement the same in their own processes.

ML helps in extracting meaningful information from a huge set of raw data. If implemented in the right manner, ML can serve as a solution to a variety of business complexities problems, and predict complex customer behaviors. We have also seen some of the major technology giants, such as Google, Amazon, Microsoft, etc., coming up with their Cloud Machine Learning platforms.

Customer lifetime value prediction and customer segmentation are some of the major challenges faced by the marketers today. Companies have access to a huge amount of data, which can be effectively used to derive meaningful business insights. ML and data mining can help businesses predict customer behaviors, purchasing patterns, and help in sending the best possible offers to individual customers, based on their browsing on online grocery stores and purchase histories. Furthermore, manufacturing firms regularly follow preventive and corrective maintenance practices, which are often expensive and inefficient. However, with the advent of ML, companies in this sector can make use of ML to discover meaningful insights and patterns hidden in their factory data. This is known as predictive maintenance and it helps in reducing the risks associated with unexpected failures and eliminates unnecessary expenses. ML architecture can be built using historical data, workflow visualization tool, flexible analysis environment, and the feedback loop.

Duplicate and inaccurate data are some of the biggest problems faced by businesses today. Predictive modeling algorithms and ML can significantly avoid any errors caused by manual data entry. ML programs make these processes better by using the discovered data. Therefore, the employees can utilize the same time for carrying out tasks that add value to the business. Also, Machine learning in detecting spam has been in use for quite some time. Previously, email service providers made use of pre-existing, rule-based techniques to filter out spam. However, spam filters are now creating new rules by using neural networks to detect spam and phishing messages. Alongside, with large volumes of quantitative and accurate historical data, ML can now be used in financial analysis. ML is already being used in finance for portfolio management, algorithmic trading, loan underwriting, and fraud detection. However, future applications of ML in finance will include Chatbots and other conversational interfaces for security, customer service, and sentiment analysis.

ML in medical diagnosis has helped several healthcare organizations to improve the patient's health and reduce healthcare costs, using superior diagnostic tools and effective treatment plans. It is now used in healthcare to make an almost perfect diagnosis, predict readmissions, recommend medicines, and identify high-risk patients. These predictions and insights are drawn using patient records and data sets along with the symptoms exhibited by the patient. Apart from that, ML can be used to increase the security of an organization as cybersecurity is one of the major problems solved by machine learning. Here, Ml allows new generation providers to build newer technologies, which quickly and effectively detect unknown threats.

ML can help in improving customer loyalty and also ensure a superior customer experience. This is achieved by using the previous call records for analyzing the customer behavior and based on that the client requirement will be correctly assigned to the most suitable customer service executive. This drastically reduces the cost and the amount of time invested in managing customer relationships. For this reason, major organizations use predictive algorithms to provide their customers with suggestions of products they enjoy.