Value Delivery through RakutenBig Data Intelligence Ecosystem and Technology
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Rakuten, Inc. is one of the world's leading Internet services companies and offering a wide variety of services for consumers and businesses with the focus on e-commerce, finance and digital contents. Therefore, Rakuten, Inc. has extremely valuable data asset covering different fields. In order to make better use of these data asset cross the whole Rakuten group and delivery data value to meet various business needs, Data Science Department is founded to enhance our group-wide data platform to provide better business decision support through BI and develop better data science solutions to improve our servers or even create new services. For today's presentations, I will give an introduction to how this big data intelligence ecosystem is building and show some example of how we are delivering values by meeting business needs.
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Value Delivery through RakutenBig Data Intelligence Ecosystem and Technology
- 1. Value Delivery through Rakuten Big Data Intelligence Ecosystem and Technology Oct.28.2017 Xuebin MA Data Science Department Rakuten, Inc.
- 2. 2 Self Introduction Joined Data Science Department Received Ph.D Degree From the University of Tokyo Joined Rakuten Speech Processing Data & Englishnization Data Scientist - The sexiest job in 21st century
- 3. 3 Outline Introduction Rakuten Data Value Chain Utilization Example Centralized User Insight Platform
- 4. 4 Outline Introduction Rakuten Data Value Chain Utilization Example Centralized User Insight Platform
- 5. 5 About Rakuten Founded: February 7, 1997 IPO: April 19, 2000 (JASDAQ Stock Exchange) Office: Rakuten Crimson House (Tokyo, Japan) Employees: 14,134 (as of Dec. 31, 2016)
- 7. 7 Rakuten Super Point Reward System
- 8. 8 Some Figures of Rakuten
- 9. 9 Big Data Eco System
- 10. 10 Outline Introduction Rakuten Data Value Chain Utilization Example Centralized User Insight Platform
- 11. 11 Big Data Value Chain of Rakuten Collect (集) Arrange(整) Utilize(使)
- 12. 12 Big Data Value Chain of Rakuten Collect (集) Arrange(整) Utilize(使) Behavior Data Service Data SAP Oracle salesforce
- 13. 13 Big Data Value Chain of Rakuten Collect (集) Arrange(整) Utilize(使) Behavior Data Service Data SAP Oracle salesforce Data Mart Data Product
- 14. 14 Big Data Value Chain of Rakuten Collect (集) Arrange(整) Utilize(使) Behavior Data Service Data SAP Oracle salesforce Data Mart Data Product Personaliza tion Marketing Automation Data Intelligence AI
- 15. 15 Big Data Value Chain of Rakuten Collect (集) Arrange(整) Utilize(使) Behavior Data Service Data SAP Oracle salesforce Data Mart Data Product Personaliza tion Marketing Automation Data Intelligence AI Data Governance
- 16. 16 Outline Introduction Rakuten Data Value Chain Utilization Example Centralized User Insight Platform
- 17. 17 Customer Profiling Through Rakuten Group Data
- 18. 18 Rakuten CustomerDNA Ichiba Travel Mobile Kobo Showtime Points Rakuma Research Ad Marketing CustomerDNA Machine Learning ETL Centralized customer attributes platform is being built with ETL and Machine Learning approaches
- 19. 19 Rakuten CustomerDNA Utilization CustomerDNA Web Personalization Shop Recommendation Mail Targeting AD Optimization Customer DNA is utilized to support various personalization and marketing solutions
- 20. 20 Feature Example: Shopping Interest Features Purchase Behavior View Behavior Search Behavior
- 21. 21 Feature Example: Shopping Interest Features 犬服 チワワ ペット パラダイス 迷子札 ペット カート Purchase Behavior View Behavior Search Behavior Interests in Pet Goods
- 22. 22 Utilization for Email Targeting +5.3% Open Rate +270% CTOR A B A B https://travel.rakuten.co.jp/
- 23. 23 Feature Example: Shopping Price Preferences Density Map Unit Price PreviouslySpent High-end à High-end Previously Recently Low-end à Low-end Previously Recently 1000 10000 50000 UnitPriceRecentlySpent Using statistical approach to get user’s price preferece
- 24. 24 Utilization for Web Personalization High-End Top Brand Cost Effective Bargain Default https://www.rakuten.co.jp/category/110472/
- 25. 25 Feature Example: Feature Prediction Behavior Data Data Representation Machine Learning Approach Predicted Customer DNA Built the generalized prediction framework to predict Customer DNA features
- 26. Predicted Feature Example: Predicted Shopping Gender 26 30% 34% 27% 9% Predicted male Predicted female Predicted shared Predicted neutral 27% of Ichiba customers are predicted as shared Account in CustomerDNA
- 27. 27 Feature Example: Income Prediction Improvement 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 17 19 21 23 25 Accuracy VS Boundary Parameter LowIncome HighIncome Average Precision 90.98% Average Recall 81.06% Accuracy 81.06% ROC 0.624 Predict the income of customer income with accuracy of 81%, and accuracy & coverage could be adjusted Income PredictionAccuracy Accuracy Coverage Adjustment
- 28. 28 Outline Introduction Rakuten Data Value Chain Utilization Example Centralized User Insight Platform
- 29. 29 Customer DNA Analysis and User Expanding ID Target User Rakuten Data ID ID ID ID ID Expanded User Similarity Various Customer Analysis User Expanding with high Quality Data Clients could keep customer nurturing by using Rakuten Media like high traffic landing pages, provide AD with R-DSP, sending planned direct mail or e-mail
- 30. 30 Various Marketing Automation for Customer Journey Management Landing Page Optimization Page A Page B Page C Rakuten Data N＋1 Period N＋2 Period N＋3 Period N＋4 Period N＋5 Period Catalo g Camp aign Catalo g Camp aign Camp aign CRM with Email and DM Clients could keep customer nurturing by using Rakuten Media like high traffic landing pages, provide AD with R-DSP, sending planned direct mail or e-mail
- 31. 31 Marketing Optimization and Hypothesis Verification 1 2 3 Cluster C B A CBA 25%70% 5 % 5 % 15%80% 25%25% Creative 50% Provide different AD creative to different user segments, Bandit algorithm will discover the best match automatically and give valuable insights Matching Optimization with Bandit Algorithm
- 32. 32 Marketing Optimization × <What> 6 patterns<Who> 8 clusters Super Heavy ü Ichiba-heavy ü SP-heavy ü Campaign-heavy Super Light ü Ichiba-light ü SP-light ü Campaign-light This approach is used at super sales and got 5% CVR lift comparing common A/B test https://event.rakuten.co.jp/campaign/supersale/
- 33. 33 Aditional Insights could be Obtained 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% A B C D E F Traffic share Coupon1st In Super Heavy Cluster, incentive-coupon pattern has the highest CVR and Traffic share. E E 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% A B C D E F CVR E In Super Light Cluster, Campaign entry appealed pattern has the highest CVR and Traffic share. 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% A B C D E F CVR 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% A B C D E F Traffic share entry 1st D D D