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Driving In-Store Sales with Real-Time Personalization - Cyril Nigg, Catalina Marketing

Driving In-Store Sales with Real-Time Personalization - Cyril Nigg, Catalina Marketing

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Driving In-Store Sales with Real-Time Personalization. #h2ony

- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Driving In-Store Sales with Real-Time Personalization. #h2ony

- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

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Driving In-Store Sales with Real-Time Personalization - Cyril Nigg, Catalina Marketing

  1. 1. DRIVING IN-STORE SALES WITH REAL-TIME Presented by Cyril Nigg PERSONALIZATION
  2. 2. Who is Catalina? Personalized digital media Drive CPG volume & ROI In-store and online We’re a data company… 2 26 Billion Shopping Trips per Year 450K+ Checkout Lanes 280MM+ Unique Shopper IDs 2 Billion UPC Scans per Day Our in-store network
  3. 3. Retailer Problem - Circulars 100’s of promotions weekly Millions of dollars spent Average shopper buys <1% Need a better way 3
  4. 4. My Favorite Deals • Personalized Circulars –> Find relevant products • One-to-one communication • Delivered in-store prints, email, online • Measurable results –> incremental sales • Real-time Personalization in action! 4
  5. 5. More Analysis, Less Implementation Goal: Hands off execution 5
  6. 6. Three Elements to Success 6 DMP Aggregation API h2o POJO Delivery Monitoring 1 2 3 Real-time Personalization Execution
  7. 7. Aggregation API • Features: Realtime == Offline • Dimension / Metrics • Data handling 7 Aggregation API Category Brand Sales Trips Dimensions Metrics DMP Transaction 1 Transaction 2 Transaction 3 … data prep • nulls • dates • normalize
  8. 8. h2o POJO • Direct upload of model • No Dev work needed • No translation • Model flexibility 8 Data Science Env Python / R h2o Model POJO Execution Platform
  9. 9. Monitoring • Low touch -> fewer “eyes” • Not traditional monitoring – Server response vs data input / output values – Total Sales variable example 9 DMP Aggregation API h2o Delivery Monitoring
  10. 10. Better Real-time execution • Aggregation API -> Scalable features • h2o POJO -> Fast implementation • Monitoring -> Identify issues 10
  11. 11. QUESTIONS??

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