1. Big Data, Big Analytics and Informed Actions
Dr. Wanli Min
Director for Data Science
Alibaba Group
12/18/2014

2. Agenda Introduction Data Revolution
Use Cases of Big Data in E-Commerce
Beyond E-Commerce

3. Introduction Dr. Wanli Min 闵万里 （山景）
PhD in Statistics from the University of Chicago, 2004
IBM T. J. Watson Research Center, New York
IBM Singapore, Singapore
Google, Mountain View, California
Alibaba, Hangzhou , China

4. Technology Drives Innovation+
Intelligent
Instrumented
Interconnected

5. Booming Ecommerce China has the largest online population
Online shopping is gaining popularity among a wide range of social groups

6. Ecommerce Leader: Alibaba
Notes:
(1) Though minority investments or joint ventures
(2) Through contractual arrangements with Ant Financial Services Group, our related company that operates Alipay

7. Ecommerce Reaches Offline Shopping
12.12 shoppers got 50% off with Alipay Wallet at offline retail stores

8. Ecommerce Big Data Ecom Offline retail Consumer
Internet
Internet of Things
Offline
retail
People-centric: Internet enables ecommerce and drives big data

9. Ecommerce Big Data
Source: New York Times, August 5, 2009

10. Data Revolution is Ongoing
Social evolution leads to inevitable Data Revolution
Big data
IT tech
Semiconductor
Modern Physics
Industrial Revolution
Agricultural
IBM sold PC business 2005,
2006: Smarter Planet
Smarter City
2004
Y2K
1980
1967
1947
1937
1905
1844
1730

11. Information Explosion Drives Data Revolution
Internet drives big data： Information Explosion
Early days of Internet： “Copyright Do not redistribute.”
Nowadays, information sharing is pervasive
2014
Click to share

12. Rising Power of Data Data Influence Exploded in the Past Decades
New Product
New Company
New Politician
New Geo-Politics

13. Data Processing Product
In 1995, processing data of MB

14. Data-driven New Business
2004, processing data of GB, Google went public

15. Data Enables Politician
In 2013, processing data of TB, the U.S. President invited you to town hall meetings to discuss hot issues

16. Big Data Reshaped GeoPolitics
Democracy is a perfect case for big data usage
Obama Campaign in 2008, 2012
Source: CNN, November 8, 2012
Source: Uchicago News, April 17, 2013
Facebook， Twitter，Blog，Poll…
Where are the persuadable voters ?

17. IT to DT IT ——> DT （Data Technology） August 2009, New York Times
“ The sexy job in the next 10 years will be statisticians”
- Hal Varian, Google Chief Economist
“What’s ubiquitous and cheap?” “Data.”
“What’s scarce is the analytical ability to utilize that data.”
In October 2008, Alibaba set its long-term strategy:
Alibaba is a data company
Alibaba empowers itself to make cloud computing as utility available to the public
In February 2014, Alibaba announced its latest strategy ：
云+端，Data Technology

18. Outlook of Big Data How big is “Big Data”?
- If sample data covers nearly everywhere in the entire probability space, then the inference from such sample data is less dependent on specific model
Volume, Velocity, Variety, Veracity
Sufficient Statistics vs. Big Data
Ergodicity

19. Outlook of Big Data Internet  Internet of Things  Big Data
Data Application vs. Data Storage/Data Warehouse
Unstructured data vs. Structured data
User behavior data , personalized offering
Online advertisement: mass display user targeting
Real-time bidding (RTB): pay for traffic  pay for audience

20. Environmental feature
Value Add by Variety of Data
Advertisement CTR lift
1,235%
550%
132%
Environmental feature
Demographics feature
Behavioral feature
Source： Acxiom Chief Analytics Officer, Dr. Jie Cheng

21. Connect & Combine Data
Connect multiple data sources to generate collective & collaborative value
Mutually beneficial
Waze： crowd sourcing
Airline companies: code share / alliance

22. Alibaba Embraces Big Data Revolution
Data Scientists are very popular at Silicon Valley and worldwide
Alibaba Data Science Team Mission
Improve business efficiency by enabling data-driven operation in LOB
Create new data product to support multiple LOBs
Utilize data to drive business innovation

23. Data Science in E-Commerce
Product Planning
Reduced JuHuaSuan manual workload
Increase Tmall “瞄一眼”revenue per slot
User Targeting
Sina Weibo Targeting
O2O
Xiami Music
Product
Buyers
matching

24. Apps Supported by Data Science

25. Apps Supported by Data Science
Walle Model

26. A Graph-based Framework
Construct dynamic activity graph
Buyers
Product i
wij u
Puv
Wij could be multi-dimension
vector.
Could be at clustering level j v
Sellers
Wij is a summary statistics of links between buyers i & j conditioning on given basket of product.
Puv is a summary statistics of product u & v joint activity conditioning on given group of buyers.

27. A Graph-based Framework
Discover potential buyers of a particular product
Buyers
Product i
wij u
Puv j v
Sellers
Product search: for target product u, search its top KNN on {Puv} and
Construct such activity graph conditioning on top KNN
Transform edge distance as 1 /
Starting from the nodes of known buyers, vote by shortest path
Approximate Dijkstra's algorithm
wij

28. Use Case: sales planning
Predict product sales
Lots of features (DSR, user, price, etc.)
Large scale model training
Productized (app)

29. Predict Best Selling Product
Business Flow：
Sellers register for sales event: discount & quantity
Operators choose who will participate the sale event
Model：
Sales prediction for each proposed product
Objective：
Help operators select best-selling products

30. Who will buy this perfume？ Will he/she buy perfume?
Predict Best Selling Product
Gradient Boosting Decision Tree Parallelized Multiple Decision Tree，sample size of a billion
Who will buy this perfume？
age？
occupation？
rating？ 买 青 中 老 否 是 优 良 不买
信息量要求比较低：
我们最多只需要问两个问题，就可以给出结论
Will he/she buy perfume?

31. Use Case: User Targeting
Operators
Offline sellers
What is my target users’ profile ?
Where are my hidden customers?
How to prioritize dissemination to different groups ?
Is my brand image well aligned with targeted customers?
Do I need to reach potential homebuyers?
Do I need to reach lottery players？
Who are Nike fans？
What is the best channel to reach my targeted customers？
Where can I find the hidden elite customers？

32. Use Case: User Targeting
Traditional User Profiling (user tags):
KYC (Know Your Customer):
Demographics profiling
Post-event
Too many user tags, often confusing to users
Users’ intent is hard to infer
Cannot differentiate importance/relevance of different tags

33. Use Case: User Targeting
Big Data enables user targeting
CYC (Catch Your Customer)
Fuse data from multiple sources in the eco system
Propensity Model , semi-supervised / supervised
Give answer to: Who, when, where, what
Discriminative of different tags.

34. Use Case : User Targeting
Objective: show one product’s promotion ads to buyers
Take a typical Internet ads campaign record for example
- Post-event analysis：calculate targeted users’ likelihood and assign to groups
Note： Categorize users by likelihood into 6 groups corresponding to descending scores 5, 4, 3, 2, 1, 0
Source： Alibaba Group’s project in 2013

35. Many Challenges for Data Scientist
Help people discover products of distinctive characteristics

36. Personalization on Mobile Taobao
高大上 女性

37. Personalized Recommendation

38. Beyond E-Commerce: HealthCare
Source： news.alibaba.com, March 18, 2014 +
Intelligent
Instrumented
Interconnected

39. Pandemic Risk Map Background
Pandemic disease in highly populated city requires early detection, prompt action, mass awareness
Problems & Challenges
Reported cases are in isolation, no predictive view
Medical treatment cost, loss of productivity and workforce
Solution
Aggregate silo data in optimal resolution
Predict risk in future
Extrapolation to city-wide area
Visualization on map cross platform
Business Case
Singapore project of X-Dengue

40. Mobile: Connected HealthCare
Disease management
Medical Home Health
Wellness
Medication Adherence
Mobile Care Management – e-prescribing, doctor/hospital directory
Remote Video Coaching
Real-time Biometric Display
Wellness, Medical Devices Integration
Data Capture
Trend /
Chart
Storage, Store and Forward
Questionnaire
Alert
( , SMS)
Alarm
Video Chat
DATA
Analytics
Server Interface
Reminders

41. Prevention, Prediction, Participation, Personalization
Patient Info
Patient matching
4P Applications
Use
Diagnosis Support
assessment
analytics
Practice
Management
Resource Allocation
(Patient-Physician Matching)
EMR
Patient
characterization
Predictive Analytics
Care
management
Patient Segmentation
(Utilization Patterns) 41 41 41

42. Big Data got its limitations !
Leinweber, David J.: “Stupid data miner tricks: overfitting the S&P 500.”  The Journal of Investing 16.1 (2007):
S&P500 has 99% correlations with ：
1. Bangladesh Cheese production
2. American Cheese production
3. Total number of Sheep in USA & Bangladesh

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