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Machine Learning For your Business

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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 analytic 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.

5 Business Benefits of Machine Learning

  1. 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 customer behaviors, purchasing patterns, and help in sending best possible offers to individual customers, based on their browsing and purchase histories.

 

  1. 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. ML architecture can be built using historical data, work-flow visualization tool, flexible analysis environment, and the feedback loop.

 

  1. Eliminates Manual Data Entry

Duplicate and inaccurate data are some of the biggest problems faced by THE 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.

  1. 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. Here, the ML algorithms 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.

  1. 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. However, future applications of ML in finance will include Chat-bots and other conversational interfaces for security, customer service, and sentiment analysis.