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Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade
Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade

Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade

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RB is a multinational consumer goods company with more than 40,000 employees operating in 60+ locations and a portfolio of leading brands such as Airborne, Air Wick, Clearasil and Lysol. RB serves the ‘traditional trade’ markets globally which are a complex network of more than 1.2 million small retailers, corner stores, open markets and street vendors. This makes it difficult to drive effective sales strategies in a competitive market due to limited range, disparate data, and high attrition. To overcome these business challenges, RB developed a solution that analyzes years of buying patterns and market specific data, ties them to sales strategy and generates a weekly sales order at the individual store level. Using the scale-out compute power on Azure Databricks enabled us to quickly deploy the solution across multiple markets where we are able to process orders for up to 50,000 stores per hour. In this session we will share our approach to building this solution.

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