Retail businesses are data-driven—they analyze data to get insights about consumer behavior, understand shopping trends, make product recommendations, optimize websites, plan for inventory, and forecast sales.
A common approach for sales forecasting is to use historical sales data to predict future demand. Forecasting future demand is critical for planning and impacts inventory, logistics, and even marketing campaigns. Sales forecasting is generated at many levels such as product, sales channel (store, website, partner), warehouse, city, or country.
Sales managers and planners have domain expertise and knowledge of sales history, but lack data science and programming skills to create machine learning (ML) models to generate accurate sales forecasts. They need an intuitive, easy-to-use tool to create ML models without writing code.
To help achieve the agility and effectiveness that business analysts seek, we’ve introduced Amazon SageMaker Canvas, a no-code ML solution that helps companies accelerate delivery of ML solutions down to hours

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