value delivery through rakutenbig data intelligence ecosystem and technology
TRANSCRIPT
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Value Delivery through Rakuten Big Data Intelligence Ecosystem and Technology
Oct.28.2017
Xuebin MA Data Science DepartmentRakuten, Inc.
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Self Introduction
Joined Data Science Department
Received Ph.D DegreeFrom the University of Tokyo
Joined Rakuten
Speech Processing
Data & Englishnization
Data Scientist -The sexiest job in 21st century
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Outline
Introduction Rakuten Data Value Chain
Utilization Example
Centralized User Insight
Platform
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Outline
Introduction Rakuten Data Value Chain
Utilization Example
Centralized User Insight
Platform
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About Rakuten
Founded: February 7, 1997IPO: April 19, 2000 (JASDAQ Stock Exchange)Office: Rakuten Crimson House (Tokyo, Japan)Employees: 14,134 (as of Dec. 31, 2016)
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Rakuten Eco System
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Rakuten Super Point Reward System
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Some Figures of Rakuten
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Big Data Eco System
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Outline
Introduction Rakuten Data Value Chain
Utilization Example
Centralized User Insight
Platform
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Big Data Value Chain of Rakuten
Collect (集) Arrange(整) Utilize(使)
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Big Data Value Chain of Rakuten
Collect (集) Arrange(整) Utilize(使)
Behavior Data
ServiceData
SAP
Oracle
salesforce
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Big Data Value Chain of Rakuten
Collect (集) Arrange(整) Utilize(使)
Behavior Data
ServiceData
SAP
Oracle
salesforce
Data Mart
Data Product
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Big Data Value Chain of Rakuten
Collect (集) Arrange(整) Utilize(使)
Behavior Data
ServiceData
SAP
Oracle
salesforce
Data Mart
Data Product
Personalization
Marketing Automation
Data Intelligence
AI
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Big Data Value Chain of Rakuten
Collect (集) Arrange(整) Utilize(使)
Behavior Data
ServiceData
SAP
Oracle
salesforce
Data Mart
Data Product
Personalization
Marketing Automation
Data Intelligence
AI
Data Governance
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Outline
Introduction Rakuten Data Value Chain
Utilization Example
Centralized User Insight
Platform
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Customer Profiling Through Rakuten Group Data
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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
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Rakuten CustomerDNA Utilization
CustomerDNA
Web Personalization
Shop Recommendation
Mail Targeting
AD Optimization
Customer DNA is utilized to support various personalization and marketing solutions
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Feature Example: Shopping Interest Features
Purchase Behavior
View Behavior
Search Behavior
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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
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Utilization for Email Targeting
+5.3%
Open Rate
+270%
CTOR
A B A B
https://travel.rakuten.co.jp/
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Feature Example: Shopping Price Preferences
Density Map
Unit Price Previously Spent
High-end à High-endPreviously Recently
Low-end à Low-endPreviously Recently
\1000 \10000 \50000
Unit Price R
ecently Spent
Using statistical approach to get user’s price preferece
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Utilization for Web Personalization
High-End
Top Brand
Cost Effective
Bargain
Default
https://www.rakuten.co.jp/category/110472/
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Feature Example: Feature Prediction
Behavior Data Data Representation Machine Learning Approach Predicted Customer DNA
Built the generalized prediction framework to predict Customer DNA features
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Predicted Feature Example: Predicted Shopping Gender
26
30%
34%
27%
9%
Predictedmale
Predictedfemale
Predictedshared
Predictedneutral
27% of Ichiba customers are predicted as shared Account in CustomerDNA
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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 Prediction Accuracy
Accuracy Coverage Adjustment
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Outline
Introduction Rakuten Data Value Chain
Utilization Example
Centralized User Insight
Platform
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Customer DNA Analysis and User Expanding
ID
Target User
RakutenData
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
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Various Marketing Automation for Customer Journey Management
Landing Page Optimization
Page A
PageB
PageC
RakutenData
N+1Period
N+2Period
N+3Period
N+4Period
N+5Period
Catalog
Campaign
Catalog
Campaign
Campaign
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
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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
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Marketing Optimization
×
<What> 6 patterns<Who> 8 clusters
Super Heavyü Ichiba-heavyü SP-heavyü Campaign-heavy
Super Lightü Ichiba-lightü SP-lightü 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/
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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
Trafficshare
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
Trafficshare
entry 1st
DD
D
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NOW HIRING!http://corp.rakuten.co.jp/careers/