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Generalized B2B Machine Learning by Andrew Waage

Abstract:- In this talk, we propose a generalized machine learning framework for e-commerce businesses. The framework is responsible for over 30 different user-level predictions including lifetime value, recommendations, churn predictions, engagement and lead scoring. These predictions provide a vital layer of intelligence for a digital marketer. Kinesis is used to capture browsing information from over 120M users across 100 companies (both in-app and web). A data processing and feature engineering layer is build on Apache Spark. These features provide inputs to predictive models for business applications. Different models each for Churn, Lifetime value, Product recommendation and search are written on Spark. These models can be plugged into any marketing campaign for any integrated e-commerce company leading to a generalized system. We finally present a monitoring system for machine learning called RS Sauron. This system provides more than 200 objective metrics measuring the health of predictive models, and depicts KPIs for model accuracy in a continual setting.

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Generalized B2B Machine Learning by Andrew Waage

  1. 1. Generalized B2B Machine Learning at Reten4on Science Andrew Waage (andrew@reten* Co-founder / CTO
  2. 2. 2012 Santa Monica ~50 (and hiring!)
  3. 3. AI Powered Marketing Automation Platform Step 1 Collect Data Step 2 Generate Predictions Step 3 Automation Powers Intelligent Campaigns Across Channels Ecom / Retail Behavioral Custom Demographic Email Campaigns On-Site Display Mobile Call Center
  4. 4. What kind of Scale? 100’s Clients 210M+ Customers Tracked 1000+ Client-specific Models 2 Billion+ Predictions Daily10K+ Actions / second
  5. 5. “Generalized ML Platform” What’s the challenge?
  6. 6. The Challenge •  Many Clients •  Dirty Data •  Sparse Datasets •  Custom Attributes •  Various Industries Clean PredictionsModel Layer! C1 C2 C3 C4
  7. 7. What Kind of Predictions? Purchase Probability: High-likelihood Lifecycle Stage: Ready to Buy Churn Time: 300 days Customer Future Value: $925 Contact Frequency: Every 3 days Optimal Time to Engage: Thursday 7-9PM Optimal Incentive/Discount: Dollars Off Product Recommendations: Based on interest Optimal Subject Line: Individual preference Optimal Template: Individual preference
  8. 8. Our Approach & Learnings 1.  Robust Ingestion Pipeline 2.  Common Feature Engineering Layer 3.  “Plug-in” Architecture for Models 4.  Evaluation / AB Testing 5.  Robust Monitoring & Visualization
  9. 9. 1. Robust Ingestion Pipeline 10K+ Actions Per Second auto-scaling! auto-scaling lambdas! •  Abstraction Layer: Data Ingestion •  Do not compromise for clean data •  Auto-scaling everywhere •  High confidence in upstream data Flume Kinesis
  10. 10. 2. Common Feature Engineering Layer •  Abstraction: Feature Layer •  Allow custom features •  Handle feature selection •  Modelers know what to expect Raw Data User Behavior Features Product Features User Sta4c Features Timing Model CLV Model Recommender
  11. 11. 3. Model Plug-in Architecture C6 C3 C4 C5 C7 • Plug-in Architecture • Tune model hyper-parameters • A/B test models per client C1 C8 C2 Client’s Model Execution Plan
  12. 12. Recommender System Multi-Layer Personalization: •  Layer 1: ML / Algorithmic •  ALS CF, Content-based, Item-Item •  Layer 2: User-level Domain Logic •  User-level predictions (gender, location, shoe sizes) •  Layer 3: Client-tuned Domain Logic / Controls •  Rank by profit-margin •  Increase discovery rate influxer 1. Algorithmic 2. User-Level Domain Logic 3. Client-Level Controls
  13. 13. 4. Model Evaluation / Fast Feedback A/B Framework M1 M2 M3 •  Start Simple •  Collect feedback data •  Skip long production cycle •  Unbiased policy generation is important M1 Campaign Predic4ons
  14. 14. 5. Robust Model Monitoring and Visualization “Sauron” (LOTR)! Monitor, monitor, monitor!!
  15. 15. Monitor Recs: Distributions, Coverage, Diversity REF: hQp://
  16. 16. Monitoring Subject Line Bandit Models
  17. 17. Churn Rates, ROC Curve, Reliability Curve
  18. 18. Our Data Science Stack Persistence! Pipeline / Process! Viz / Monitor! Code!
  19. 19. Takeaways 1.  Use abstraction layers -  Clean / common interfaces 2.  Monitor, monitor, monitor -  Fast feedback 3.  Start simple and keep iterating
  20. 20. Thank You!