Predictive Analytics in Retail: Transforming Inventory Management and Customer Insights
Keywords:
predictive analytics, retail, inventory managementAbstract
Predictive analytics influences customer understanding as well as supply for stores. This paper investigates how advanced prediction models might help to enhance retail supply chains management and focused marketing. Using historical data, statistical methods, and machine learning, retailers might be able to predict trends, avoid stock outs, and modify their marketing.
Time series study, regression models, and ensemble learning are covered. Large databases let retailers project demand. Time series study reveals long-term and seasonal trends; regression reveals inventory linkages. Using ensemble learning helps one to acquire dependability.
Article pushes retail predictive analytics forward. Starting supply control Predictive analytics keeps store stockouts far apart and overstocking away. Products' availability raises customer efficiency and satisfaction. Demand planning reduces markdown losses and overstocking, hence improving profitability.
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