Examples of Machine Learning Use-Cases of Relevance to Retail


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Organization (Click on Logo for Story) Link Description Use Cases
Story logo http://bit.ly/1QcMS1z Why does one drink sell well at a location? Why do sales of six-packs spike the third week of the month? Why do water sales increase during the summer? Understanding the answers to questions like these is critical to business success for Arca Continental, which is the second-largest Coca-Cola bottler in Latin America and delivers its products to more than 900,000 customers across Mexico and South America. The solution was made in a fairly easy way using the power of Microsoft Azure Machine Learning (Azure ML). Demand Forecasting, Profitability Analysis
Story logo http://bit.ly/2h2YT0g Hershey will take advantage of Azure Machine Learning to optimize licorice production using intelligent sensors on factory line extruders. This Internet of Things (IoT) solution uploads data to the cloud to build predictive algorithms that ensure the extruders run at optimal efficiency. Process Optimization
IoT
Story logo http://bit.ly/2jLwoVQ Keeping track of the consumption of water onboard a cruise ship is a complex equation. Although the washing and cleaning schedule kept by the hotel operation may be fairly constant from one trip to the next, water consumption by passengers varies widely, with different nationalities showering at different lengths and temperatures and at different times of day. Other water consumers on board are even less easy to predict. Demand Forecasting
Story logo http://bit.ly/2kh9gSI Keeping drinks in stock is an important factor in engaging and retaining customers; it also means that more drinks can be vended. But that’s only part of the story: in Germany, vendors are required to keep machines well-stocked. By moving to Azure, MARS DRINKS has gained a platform to improve operational efficiency, drive business insights, and support global expansion across all its markets. Demand Forecasting
Iot
Story logo http://bit.ly/1J4lHEA Retailer Pier 1 Imports wanted to better connect with its customers using insights and data. To do that, the company took to the cloud to pilot a predictive analytics solution based on Microsoft Azure Machine Learning and Microsoft Power BI. As a result of the pilot, Pier 1 Imports may use data insights to predict which products customers will want in the future, create a dynamic website using predictive modeling and create more efficient and effective marketing campaigns. Demand Forecasting
Story logo http://bit.ly/1Eld1Jm What would make your next restaurant meal more enjoyable? Someone to recommend a rosé or entertain the kids? Or maybe a way to pay the bill without waiting for the waiter? The tabletop platform from fast-growing Ziosk® does all that—and now it’s set to do something more: Using Microsoft big data and cloud technologies, it’ll predict your preferences and serve them up as part of a better dining experience. The goal is to create personalized experiences that result in happier guests and more business for restaurants Personalization, Recommendations
Story logo http://bit.ly/1DPRt2H If a restaurant owner is shopping on JJ Food Service Ltd.’s website, he might not necessarily know what other restaurant owners are actually buying, or the full product names for his restaurant’s sector, so as the customer is shopping, by looking at the individual items going into the basket, Machine Learning assists in suggesting other products that are related to his market sector. Recommendations, Personalization
Story logo http://bit.ly/1HhkVT7 Taking inspiration from companies like Netflix and Spotify, French e-book subscription service Youboox saw its recommendation engine as its long-term-growth engine. But a home-grown upgrade was expensive and a Google service was too much trouble. Microsoft Azure Machine Learning offered a fast, simple, low-cost alternative that produced really great recommendations. The company now sees more user activity on its site, as well as more users coming to the site through recommendations.

Recommendations, Personalization

Story logo http://bit.ly/1QwugOB Rockwell Automation created a solution to monitor expensive capital assets and use that data to improve efficiency, drive better performance and enable innovation. Based on Microsoft Azure Internet of Things services, the solution collects, integrates, and organizes sensor data from remote equipment across global supply chains to support real-time insight, predictive analytics, and preventive maintenance. Preventive Maintenance, IoT
Story logo http://bit.ly/1PxSzXb Mendeley wanted to optimize its academic reference and networking platform so that it would best serve researchers, providing a faster and more flexible way to share and archive scientific findings and ideas. Measuring the activity of new site users and filtering that data through cloud-based predictive analysis models with Azure Machine Learning gives Mendeley the intelligence it needs to improve its product and fulfill its mission of advancing science. Personalization, Next Best Action
http://bit.ly/1SfzldQ ThyssenKrupp Elevator – a leading global provider of elevators – has 1.1 million machines operating in 150 countries, 49,000 employees, and reported sales of EUR €6.2 billion (US $8.4 billion) in 2013. With installations in buildings worldwide, TKE wanted to further improve its competitive advantage by increasing elevator reliability and uptime. With help from Microsoft partner CGI, TKE created an intelligent, cloud-based solution based on Microsoft Azure Intelligent Systems Services, Power BI for Office 365. Preventive Maintenance, IoT
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For Wash Laundry, using Azure ML means no more scouring through government spreadsheets to update their economic regression models. Wash takes into consideration census track level data, surveys laundry mats, and measures the data in its own rooms to determine pricing. After testing out its econometric regression models with Azure Machine Learning in markets closer to home, the results have been good enough that it will move the practices to Canada. Wash has boosted revenue to some of its stores by as much as 7.5 per cent – often by lowering the price for residents.

Price Optimization
Story logo http://bit.ly/1K7vBKg Real Madrid plans to use Azure Machine Learning to work directly with coaches and players to find out the status of players and also predict the price of seats during the season. Price Optimization, Forecasting

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