Value Delivery through RakutenBig Data Intelligence Ecosystem and Technology

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

4

Outline

Introduction Rakuten Data Value Chain

Utilization Example

Centralized User Insight

Platform

5

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

11

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

Predicted Feature Example: Predicted Shopping Gender

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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%

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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

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A B C D E F

Trafficshare

Coupon1st

In Super Heavy Cluster,incentive-coupon pattern has the highest CVR and Traffic share.

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E

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CVR

E

In Super Light Cluster, Campaign entry appealed pattern has the highest CVR and Traffic share.

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CVR

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Trafficshare

entry 1st

DD

D

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