Machine learning(ML) has the ability to extract meaningful insights from raw data to solve complex, data-rich business problems quickly and accurately. ML algorithms learn from data iteratively and detect hidden patterns and insights with having the need to be explicitly programmed. ML is progressing at an increasingly rapid pace and is being supplemented and supported by various new computing technologies.
Businesses use ML to enhance scalability and to improve business operations. Artificial intelligence tools and various ML algorithms have experienced incredible popularity in the field of business analytics. Growing volumes of data, easy availability of data, cheaper and faster computational processing and affordable data storage are some of the
key factors that have led to a major machine learning popularity. As a result, organizations are becoming more and more dependent on machine learning applications and how they can be implemented in their own business processes.
10 Business Benefits of Machine Learning
Machine learning enables businesses to extract meaningful information and insights by finding patterns from huge sets of raw data. When it is implemented correctly, ML can be an ideal solution to many of the business’s complex problems as well as informing them about future customer behavior.
Here are some of the key benefits of Machine Learning and how it can help a business:
1.Customer Lifetime Value Prediction
Some of the major challenges faced by marketers today are customer lifetime value prediction and customer segmentation. Companies can effectively derive meaningful business insights from the huge amounts of data that they collect or have access to. Machine learning and data mining help businesses to predict customer behavior, their purchasing patterns and accordingly suggest sending the best possible offers to specific customers according to their browsing and purchase history.
Most manufacturing companies conduct regular preventive and corrective maintenance practices, which are expensive and quite often, inefficient. With the implementation of ML, however, companies in this particular sector can discover meaningful insights and patterns hidden within their factory data. This is called predictive maintenance as it helps to reduce risks associated with unexpected failures and reduces any unnecessary expenses. ML architecture can be made using the companies historical data, flexible analysis environment, workflow visualization tools and the feedback loop.
3.Eliminates Manual Data Entry
Replicated and inaccurate data are part of the biggest problems faced by businesses today. By using predictive modeling algorithms and ML, companies can markedly avoid any errors caused by entering data manually. ML programs ensure that these processes become better by using existing data. As a result, the employees are freed up from doing manual data entry and can carry out tasks that create more value for the business.
ML is also particularly useful in identifying spam. Earlier, email service providers used pre-existing, rule-based techniques to identify and filter out spam. However, spam filters are now being created under new rules by using neural networks to detect spam and phishing messages.
The development of product based recommendation systems is enhanced by machine learning. Most e-commerce websites rely on machine learning for creating product recommendations. ML algorithms utilize the purchase history of the customer and match it with the products in their inventory to find hidden patterns and bunch similar products together. These products then get suggested to the customer, thus prompting them to purchase these products.
As machine learning uses large volumes of quantitative and accurate historical data, it can also be used in financial analysis. Many finance firms already use ML for portfolio management, algorithmic trading, loan underwriting and also fraud detection. In the future, we can expect ML applications for finance to include chatbots and other AI-based conversational interfaces to create better security, customer service and sentiment analysis.
Sometimes referred to as computer vision, image recognition has the ability to attain numeric and symbolic information from images and other such high dimensional data. This process involves the use of data mining, pattern recognition, database knowledge discovery and machine learning. ML is an integral aspect of image recognition and has found many applications in various industries like healthcare, automobiles, etc.
Medical diagnosis has also been improved by using machine learning. Several healthcare organizations use ML’s ability to improve patient’s health and reduce overall healthcare costs. This is accomplished using superior diagnostic tools and effective treatment plans. The applications of ML in healthcare are creating an accurate diagnosis, predict readmissions, recommend suitable medicines and detect high-risk patients. Such diagnosis and insights are achieved by using patient records and data sets as well as the patient’s symptoms.
9.Improving Cyber Security
Many organizations use machine learning to increase their cybersecurity. ML allows new generation software providers to build better technologies that possess the ability to quickly and effectively detect unknown threats.
10.Increasing Customer Satisfaction
Machine learning can also improve customer loyalty as well as creating a superior customer experience. ML uses the customer’s call records to analyze behavior and according to their client’s requirements, assigns the customer to the most suited customer service executive. This significantly reduces the cost and time spent in managing customer relationships. As a result, many major organizations are currently using predictive algorithms to give product suggestions to customers that will suit their needs.