{"id":2348,"date":"2018-09-06T00:00:00","date_gmt":"2018-09-06T07:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/current-use-cases-for-machine-learning-in-retail-and-consumer-goods"},"modified":"2023-07-31T15:01:40","modified_gmt":"2023-07-31T22:01:40","slug":"current-use-cases-for-machine-learning-in-retail-and-consumer-goods","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/current-use-cases-for-machine-learning-in-retail-and-consumer-goods\/","title":{"rendered":"Current use cases for machine learning in retail and consumer goods"},"content":{"rendered":"

This blog was co-authored by Marty Donovan.<\/em><\/p>\n

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Retail and consumer goods companies are seeing the applicability of machine learning (ML) to drive improvements in customer service and operational efficiency. For example, the Azure cloud is helping retail and consumer brands improve the shopping experience by ensuring shelves are stocked and product is always available when, where and how the consumer wants to shop. Learn more by reading Retail and consumer goods use case: Inventory optimization through SKU assortment + machine learning.<\/a><\/p>\n

Here are common use cases for ML in retail and consumer goods, along with resources for getting started with ML in Azure.<\/p>\n

8 ML use cases to improve service and provide benefits of optimization, automation and scale<\/h2>\n
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  1. Inventory optimization through SKU assortment + machine learning<\/a> ensure shelves are stocked and best products are always available for purchase.<\/li>\n
  2. Recommendation Engine<\/a> – Train Matchbox Recommender<\/a> to modernize engine capabilities for relevant product and service offerings which can generate incremental revenue.<\/li>\n
  3. Visual Search<\/a> capitalizes on mobile-first, content rich, customer-centric search capabilities.<\/li>\n
  4. Sentiment Analysis<\/a> can help companies improve their products and services by better understanding how their offering impact customers.<\/li>\n
  5. Fraud Detection<\/a> to detect anomalies and other errors that signal dishonest behavior.<\/li>\n
  6. Demand forecasting by pricing optimization<\/a> to meet consumer demand related by creating a demand forecast at various price points and business constraints to maximize potential profit.<\/li>\n
  7. Personalized offers<\/a> improve the customer experience by offering relevant information which in turn provides retailers with improved data about the customer’s brand engagement.<\/li>\n
  8. Customer Churn Prediction<\/a> to improve strategic decision making regarding customer engagement and lifetime value.<\/li>\n<\/ol>\n

    All of these use cases can be addressed using machine learning.<\/p>\n

    Machine learning on Azure<\/h2>\n

    Customers can build artificial intelligence (AI) applications that intelligently process and act on data, often in near real time. This helps organizations achieve more through increased speed and efficiency. Here are some resources to help you get started.<\/p>\n