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Retail Business Review | Monday, April 11, 2022
AI/ML will continue to upgrade forecasting capabilities, resulting in more tough decision-making processes that better satisfy customer requirements.
FREMONT, CA: The Covid epidemic wreaked damage on companies worldwide, causing unmatched disruption and uncertainty. Retailers, especially, recognized that conventional forecasting techniques based on past sales data were insufficient to predict sales during the COVID-19 epidemic.
Due to changing demand, retailers must concentrate on forecasting future store sales across longer planning horizons and toward more precise short-term planning. Also, they discovered a wealth of external market data, such as COVID-19 infection rates, mobility indices (Google, Apple), demographics, and macroeconomic data that can be used as drivers to explain demand patterns and increase forecast accuracy.
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Market information to increase the accuracy and explainability of forecasts
Retail forecasting uses increasingly external data and industry knowledge via publicly available data on consumer demographics, macroeconomic indicators like Gross Domestic Product (GDP), interest rates, social media buzz, and worldwide trade. Additionally, as demand sensing levers, leading demand indicators such as news, product evaluations, search engine data, and website views are becoming more prevalent.
By incorporating local weather, activities occurring near their locations, and traffic conditions. Forecasting approaches are evolving away from classical time series methods and toward intelligent forecasting enabled by AI/Machine Learning and cloud computing, which can consider a diverse set of external market forces and scale to retail numbers.
These next-generation systems can take leading indicator data and generate a forecasted picture devoid of human bias or manipulation. Retailers are continually learning which leading indicator data best predicts changes for a more precise forecast, down to the minute detail, such as the store, item, day/hour, and consumer fulfillment choice—purchase in-store, ship from store, or click-and-collect.
AI/machine learning combined with solid feature engineering
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