Customer churn is very costly to a business. Studies have shown that it typically costs hundreds of dollars to acquire a replacement customer. Early warnings of unhappy customers allows us to incentivize and engage with them to improve satisfaction and retention.
Goodman Gu shares a case study from a leading SaaS company that quickly and easily built, trained, optimized, and deployed an XGBoost churn prediction ML app at scale with Amazon SageMaker. Goodman explores the trade-offs in feature engineering, algorithm selection, and hyperparameter tuning, whether it’s better to use a classifier with Keras using a TensorFlow backend or XGBoost, and whether or not to use Spark MLlib. Along the way, Goodman discusses the benefits of Amazon SageMaker, a fully managed end-to-end machine learning service and production pipeline.
Goodman Xiaoyuan Gu is head of machine learning architecture at Boston-based Cogito, where he leads operations of large-scale real-time augmented intelligence platform. Previously, he headed marketing data engineering at Atlassian and was vice president of technology at CPXi, director of engineering at Dell, and general manager at Amazon, where he built marketing, analytics and machine learning applications. He has served on technical program committees of two IEEE flagship conferences and is the author of over a dozen academic publications in high-profile IEEE and ACM journals and conferences. Goodman holds a degree in engineering and management from MIT.
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