Some Azure Machine Learning Implementations in the Retail Industry
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Its been interesting learning about the various implementations and solutions using Machine Learning in Retail over the last few months since we released Azure ML.
The implementation for Pier 1 Imports, by Max451, is one of my favorites. Operating over 1,000 stores, Pier 1 Imports aims to be their customers’ neighborhood store for furniture and home décor. They recently launched a multi-year, omni-channel strategy called “1 Pier 1” with a key goal being to understand their customers better and serve them with a more personalized experience across all interactions and touch points with the Pier 1 brand. Pier 1 Imports adopted Microsoft Azure Machine Learning to help them predict what their customers might like to buy next. The video below tells the story about the solution:
How Pier 1 is using the Microsoft Cloud to build a better relationship with their customers
Today, sensors (& the world of the Internet of Things), open data initiatives and social media sources provide a richness of information. Retailers are empowering employees by combining these sources of information with the structured information in business applications in order to provide better customer experiences and therefore become more profitable. The food retailer JJ Food Services are using Microsoft Azure Machine Learning services and Dynamics AX to predict the customer’s next purchase to minimize the time spent on the website as well as an increase in order volume. JJ Food Services combines click-stream analysis with historical transaction data from the business application to predict the next order. See more details about this story at LINK.
Neal Analytics is leading the charge with Azure Machine Learning across a variety of industries and scenarios. For retail and consumer goods, they have developed a predictive demand solution on Azure ML (SmartStock) that helps companies understand what really drives demand for their products and predict future demand more accurately by including data from a variety of influencers and indicators such as weather, social media, competitive promotions, socio-economic data and more. In one deployment, at a large soft-drink bottling company in Mexico, SmartStock was able to confirm that temperature is the most important influencer of demand for their beverage products. But it was also able to tell them exactly how many units of beverage product will be sold for each degree of increase/decrease in temperature. Beyond that, SmartStock was able to identify the impact of other variables like holiday stock-ups, sporting events and unemployment. This allows them to better plan for promotions and price changes and optimize delivery routes.
For digital marketers, Neal Analytics developed a solution that helps marketers optimize their SEM strategies and predicts the bid amount required to obtain a specific search result position in paid search scenarios. One ecommerce customer was able to recognize a $40M savings on digital marketing spend by eliminating the guesswork around paid search.
Pulling together over 400 billion real-life attributes across disparate sets of data such as purchase interests, social behavior, demographic data and financial information, Versium creates unique insights into customer behavior and helps companies leverage these insights in their promotion and marketing campaigns. Versium is working with a major retail customer to help them detect fraudulent purchases of gift cards. This retailer already has an existing rules-based system to detect such fraud, but it generated many false positives. Versium was able to quickly put together a predictive modeling solution on Azure ML, which, in a test run, showed that only 6 percent of 1000 transactions that had been denied by the old rules-based system were actually fraudulent – numbers that translate into much higher customer satisfaction, higher revenue and considerable value for this retailer. See details about this story at LINK
I am eager to learn more about companies building innovative solutions with Azure ML. Feel free to reach out to me if you have an interesting story or solution to share.