Machine learning methods for demand forecasting in a new normal

Machine learning-based methods of demand forecasting in the retail industry leverage historical data, but the limitations of that data are clear as we grapple with COVID-19. The current state of customer demand has significantly changed, and the forecast accuracy will be diminished.How can we adjust to predict the new demand paradigm? The following are three methods that may boost the accuracy of demand.Short-Term POS Data AnalysisOne proven and efficient technique for identifying shifts in patterns of demand is to analyze the most up-to-date point of sale data. The latest month or two months’ data can be combined with promotions, sales orders and upcoming shipments to make an earlier prediction of alterations in the behavior of customers.Natural Language Processing AnalysisNLP (Natural Language Processing) techniques analyze news and social media to identify customers’ mindsets through data mining and sentiment analysis. We can then zero in on what customers are buying most frequently, as well as their preferences and behavioral patterns through feedback in reaction to news events.With a large enough sample size of customer feedback, we can use NLP modeling to notice changes in customer purchasing decision-making and identify goods quickly being bought out of stock.Information Cascade ModelingThe technique of information cascade ...


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