A succinct synopsis of predictive analysis applications in the contemporary period
DOI:
10.47709/ijmdsa.v3i4.4625Keywords:
Supply chain management, demand forecasting, customer segmentation, personalized marketing, sales forecasting, customer lifetime value, churn prediction, market trend analysis, sentiment analysis, operational efficiency, decision-making, data-driven strategies, business performance, and personalized medicine.Dimension Badge Record
Abstract
A potent subset of data analytics called predictive analytics is revolutionizing a number of industries by using historical data, machine learning methods, and statistical algorithms to predict future events and guide strategic choices. The uses and advantages of predictive analytics in the fields of finance, healthcare, manufacturing, energy and utilities, retail, and marketing are highlighted in this thorough overview. Predictive models improve market risk management, fraud detection, and credit risk assessment in the financial sector, promoting stability and confidence. Applications in healthcare include operational efficiency, tailored treatment, and patient risk assessment, all of which improve patient outcomes. Supply chain risk management, quality assurance, and predictive maintenance all help manufacturers maximize efficiency and reduce downtime. Demand forecasting, asset performance management, and regulatory compliance all help the energy and utilities sector by guaranteeing dependable and effective service delivery. Predictive analytics helps retailers satisfy customer requests and keep a competitive edge by assisting with inventory management, customer satisfaction, and competitive analysis. Customer segmentation, personalized marketing, campaign optimization, sales forecasting, churn prediction, customer lifetime value prediction, market trend analysis, and sentiment analysis all greatly improve marketing techniques.
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Accepted Date: 2024-09-02
Published Date: 2024-09-03
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