Sharing about how Lazada applies data science to improve customer and seller experience, and my personal journey to my current role in Lazada as Data Science Lead, VP

1. How Lazada uses Data Science
and my journey
INSEAD lunchtime sharing, 25th April 2018

2. Before I begin, any questions you would like
addressed?
I’ll answer throughout my sharing.

3. #1 Shopping Site in SEA
• One-stop shop in Southeast Asia for
customers, over 155,000 sellers and about
3,000 brands
• Local operations in 6 countries and regional HQ
in Singapore
• Best-in-class eLogistics ecosystem with more
than 100 partners to address key challenges in
Southeast Asia
• Majority owned by Alibaba Group – world’s
largest retail commerce company

4. • Enablers of our growth and innovation:
• Diverse talent base of approx. 6,600
employees
120+
Languages
80+
Nationalities
28
Average age
Shaping a culture that’s Open and transparent / Dare to fail /
Just get-it-done mentality / Anyone has the right to speak

5. DATA SERVICES
• Lazada's unified data platform
• User tracking
LOGISTICS
• Delivery address
• Logistics network radar
• Leadtime estimations
MARKETING
•Data management platform
•Customer personalization
CYBER SECURITY &
FRAUD MANAGEMENT
• Fraud prevention and detection
• User identity resolution
• Enablers of our growth and
innovation: Our Data and Tech teams
HCMC Tech Hub
since 2013
Singapore Tech Hub
since 2015

6. Lazada Data
Data App Devs expose, integrate, platform-ize
Data Scientists explore, prepare, model
Data Engineers collect, store, maintain
Start from bottom up

7. Problems we work on…

8. Product-related:
- Product Categorization
- Attribute Extraction
- Spam Detection
- Image Quality Checking

9. Consumer-related:
- Recommendations
- Product Ranking
- Consumer Segmentation
- Customer Lifetime Value

10. Seller-related:
- Price Elasticity
- Detecting Counterfeits

11. Operation-related:
- Delivery time forecasting

12. Automated Review QC

13. Product
Review
API
Spam
Classification
General
Classification
Model-based
Data sources
Rule-based
Keywords
Spam
Characteristics
Review
API
Manual QC
Input and post-processing
Audit

14. Overall results
Significant manpower cost savings (5-figures monthly)
Existing workforce can be diverted to difficult-to-automate tasks
Reduced lead-time before reviews are live on site

15. Product Ranking

16. Ranking
affects what
appears
on top

17. Ranking is
different
from recom-
mendation

18. Web Tracker
(JavaScript)
Mobile Tracker
(Adjust)
3rd Party
(e.g. ,ZenDesk,
SurveyGizmo)
Kafka Queues
Bulk Loaders
(Spark)
Hadoop
Hadoop
Data
Exploration
+
Data
Preparation
+
Feature
Engineering
+
Modelling
(Spark)
Manual
Boosting
(Django)
Local
Validation
A/B
Testing
Product
Seller
Transaction
Product rankings
Split traffic and measure outcomes
(Category Managers)
(User devices)

19. Overall results
Better ranking improved conversion (3 – 8%) and revenue per
session (5 – 20%)
Introducing new products improved new product engagement
(CTR increased 30 – 80%; add-to-cart increased 20 – 90%)
Emphasizing product quality had neutral to positive outcomes
(reduced return rate; increased product net promoter score)

20. My Data Science
Journey

21. Studied Psychology and Business at SMU
- Probability, statistics, experimental design
- Written and verbal communication
- SPSS & R

22. Economic and political analysis at MTI
- Written and verbal communication

23. Joined IBM to pursue passion
in working with data

24. First step into data analytics as a data analyst

25. Developed dashboards and analytics for
end-to-end supply chain optimization

26. Worked on anti-money laundering and entity
resolution system for global bank

27. Collected and analyzed tweets to provide
insights for electronics conglomerate

28. Was transferred to workforce analytics team,
working on data from IBM’s 450k employees

29. Forecast models for global job demand to
optimize recruitment and workforce allocation

30. Job recommender to increase internal transfer,
skill renewal, satisfaction, and reduce attrition

31. Current at Lazada’s Data Science team

32. How is my
time spent

33. Data
Preparation,
50%
Modeling,
20%
Productionizing,
30%
Coding Breakdown
Majority of time spent
coding (thankfully)
Coding,
55%
Engagment,
30%
Others,
15%

34. Data Preparation
- Merging data
- Imputing nulls
- Removing duplicates
- Handling outliers
- Fixing formats
- Etc, etc, etc

35. Building the model
- Feature engineering
- Machine learning
- Validation
- Iterate, iterate, iterate

36. Deploying to production
- Proof-of-concept
- Developing API
- Scheduling jobs
- Continuous integration
- Fixing bugs

37. Engagement (with stakeholders)
- Roadmap planning (quarterly)
- Aligning solution with problem
- Explaining and getting buy-in

38. Other tasks
- Providing assistance
- Research and brainstorming
- Team sharing

39. How to learn
data analytics tools?

40. SQL
- w3schools (step-by-step on basic SQL)
- sqlzoo (practice questions on SQL)
Underlined bullets are hyperlinks

41. Python / R
- Interactive programming in Python (Rice)
- Introduction to Computer Science in Python (MIT)
- R programming (Johns Hopkins)
- Introduction to Probability and Data in R (Duke)
Underlined bullets are hyperlinks

42. Spark
- Introduction to Apache Spark (Berkeley)
- Big data analysis with Apache Spark (Berkeley)
- Databricks education materials
Underlined bullets are hyperlinks

43. How to learn
data analytics skills?

44. Probability, statistics, and experiment design
- Inferential Statistics (Duke)
- Statistical Inference (Johns Hopkins)
Underlined bullets are hyperlinks

45. Machine Learning
- Machine Learning (Stanford)
- Statistical Learning (Stanford)
Underlined bullets are hyperlinks

46. Communication
- Written
- Spoken

47. How to practice
data analytics?

48. Start your own projects
- Learn what’s not taught at school
- Add to your portfolio

49. Volunteer with NGOs
- DataKind, an NGO that helps NGOs tackle
problems through data science

50. Write and Speak
- Write a blog on your learning
- Share at meetups

51. What can you
do today?

52. Steps to take
- Get very good at basic SQL
- Get very good at either R or Python
- Understand basic machine learning techniques
- Understand distributed systems and processing
- Improve communication by writing and sharing
- Get experience by doing projects or volunteering

53. Thank you!
eugene.yan@lazada.com
Our culture: http://bit.ly/datascienceculture
How we rank products: http://bit.ly/how-lazada-ranks-products