If you’re a business owner and haven’t heard about Machine Learning you’re missing out on an opportunity to help your business grow, be more resourceful, save money and a more satisfied customer base. First of all, what is machine Learning and how can businesses make use of it? Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. It is based on the idea that artificial intelligent systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. ML algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so. ML 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. Some Business Benefits of Machine Learning Machine Learning helps in turning a huge set of raw data into meaningful information and the best part is you don’t need a degree in data science to use the tools that have already been developed by data companies. Customer Lifetime Value Prediction Customer lifetime value prediction and customer segmentation are some of the major challenges faced by the marketers today. Companies have access to huge amount of data, which can be effectively used to derive meaningful business insights. ML and data mining can help businesses predict consumer behaviours, purchasing patterns, and help in sending best possible offers to individual customers, based on their browsing and purchase histories. This is most useful in the insurance sector when deciding how risky it is to ensure a potential client. Predictive Maintenance 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. This can also help in the upkeep of stock and detecting theft and foul play. ML architecture can be built using historical data, workflow visualization tool, flexible analysis environment, and the feedback loop. Eliminates Manual Data Entry Duplicate and inaccurate data are some of the biggest problems faced by businesses today. Predictive modelling 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, with this machines can perform time-intensive data entry tasks, leaving your skilled resources free to focus on other value-adding duties. Product Recommendations Unsupervised learning helps in developing product-based recommendation systems. Most of the e-commerce websites today are making use of machine learning for making product recommendations. ML algorithms can use customer's purchase history and match it with the large product inventory to identify hidden patterns and group similar products together. These products are then suggested to customers, thereby motivating product purchase and increasing potential revenue. Financial Analysis 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. The finance industry is using it to mine useful data in order satisfy its large market of customers, all at a cheaper price with less than the labour force previously required. Medical Diagnosis 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 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. Improving Cyber Security ML can be used to increase the security of an organization as cyber security 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. Increasing Customer Satisfaction ML can help businesses improve customer loyalty and also ensure satisfactory customer experience. This is achieved by using the previous call records for analysing the customer behaviour 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 relationship. For this reason, major organizations use predictive algorithms to provide their customers with suggestions of products they enjoy. It also allows customers to find products quicker. In short it can improve advertising by only advertising relevant products to customers who have the highest chance of buying thus reducing advertising costs. Some Real Life Examples Machine learning brings a lot of personalization to the customers and helps to target company’s efforts. For instance, Facebook mixes statistical analysis and predictive analytics to find patterns based on data. It helps to personalize the newsfeed, suggest interesting content, posts, and improve user engagement. Also, Facebook uses neural networks to scan images and suggest members to tag in the picture. Netflix used machine learning to save $1 billion by the personalization of movies and TV shows to the subscribers. Additionally, ML may be used to detect spam. Earlier, email service providers took advantage of rule-based techniques to sort out spam. Nowadays, spam filters are now developing new rules by applying neural networks for these purposes. PayPal applies at least 3 machine-learning approaches to eliminate risk and fraud. Hello Barbie may successfully communicate with children by using machine learning and advanced analytics, natural language processing. A microphone records, the speech is analysed to find a needed response from 8,000 variations in under a second. Another example is IBM’s machine learning system, Watson that learned Gaudi’s work and his work involving Barcelona, song lyrics. Watson consumed all data and brought inspiration to the human artists who needed to create a sculpture in the style of Gaudi. Many companies such as Google, Amazon and Microsoft offer affordable Cloud Machine Learning services to enterprises small and great.