A Flexible answer to Retail CRM: an Ensemble engine for Customer Management
An ensemble model is built on a Spark architecture that utilizes a range of models, including Gradient Boosted Trees, multi-layer Neural Network, Collaborative Filters and others. We show that by grouping the outputs of individual models and collecting their ensemble results from weaker models, rollout to multiple customer management use cases is extremely efficient and saves costs by 70%, yet preserving model robustness and reduces overfitting.The forever complex and versatile nature of retail requires a highly adaptive model with a robust ability to incorporate complicated and changing business rules. Therefore this paradigm allows us to collect and append past heuristics easily, and allow rapid rollout to multiple channels and business units.
From a very young age, Yu-Xi has always been motivated by the thrill of solving problems. This has led him to earn a doctorate in Complexity Science and subsequently entering the industry to solve business problems with Artificial Intelligence. Since then, He has tackled a range of problems, ranging from Gaming, Election Engineering and Retail. Apart from delivering products to bring business value, he has also built up Data Science Teams, training, recruitment and setting best practices. He is currently heading the Data Science Team in A.S. Watson, the biggest Health and Beauty Retailer in the world.