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Gleb Zakhodiakin

End-to-End Business Analytics and Optimization in Ingram Micro's Two-Tier Distribution ... - 0 views

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    "Ingram Micro, the world's largest technology distributor operates in a high volume low margin environment. The company started its Business Intelligence & Analytics practice in North America (NA) about 6 years ago. Since then the group has built and deployed a scalable highly innovative price optimization engine, imprime™, for NA's $8B spot business, a set of analytics apps, imsmart™ for its internal sales organization and an integrated digital marketing platform, intelligence.ingrammicro.com, to run data-driven marketing campaigns to its customers and end customers. Since 2011, these products and analytical programs have yielded a cumulative benefit of $1.12 Billion of incremental product revenue and $28M of incremental gross profit. These solutions have been effective in driving profitable growth and are scalable. Our next steps are to continue to drive these best practices within the other regions outside NA and continue our sales enablement activities."
Gleb Zakhodiakin

Analytics for an Online Retailer: Demand forecasting and Price Optimization at Rue La La - 0 views

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    "We partnered with the online flash sales retailer Rue La La to develop and implement a pricing decision support tool that sets initial prices for new products. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (typically 2-3 days) on designer apparel and accessories. Our approach is two-fold and begins with developing a demand prediction model for new products. The two biggest challenges faced when building our demand prediction model include estimating lost sales due to stockouts, and predicting demand for styles that have no historical sales data. We use descriptive analytics (clustering) and predictive analytics (regression) to address these challenges and predict future demand. Regression trees - an intuitive, yet non-parametric regression model - prove to be effective predictors of demand."
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