Tokopedia - How Tokopedia Became one of Indonesia’s Most Promising Startups
07 - IDNOG04 - Leontinus Alpha Edison (Tokopedia) - Data Driven Innovation
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Transcript of 07 - IDNOG04 - Leontinus Alpha Edison (Tokopedia) - Data Driven Innovation
Data-Driven Innovation
Once Upon a TimeIn Jakarta, Jan 2009
1 Product Guy 1 Half Engineer as co-founder
Don’t have business background AT ALL
China Japan Korea Indonesia
baidu.com yahoo.co.jp naver.com google.com
qq.com google.co.jp google.co.kr google.co.id
taobao.com amazon.co.jp google.com facebook.com
sina.com.cn youtube.com amazon.com youtube.com
weibo.com google.com daum.net yahoo.com
tmall.com fc2.com youtube.com detik.com
hao123.com rakuten.co.jp facebook.com kaskus.co.id
sohu.com nicovideo.jp tistory.com liputan6.com
360.cn twitter.com ppomppu.co.kr kompas.com
tianya.cn facebook.com gmarket.co.kr wordpress.com
?Online Forums
Social Media
Blog Platform
Data-Driven Mindset
TVC Optimization
Before
What did we do?
We found that people tends to play with their phone while watching TV.
IF we can captured number of people who visits our site, after watching our TVC. Means, our TVC works on that niche of people in that particular program.
How? 1. Filter traffic only from Direct and Organic. 2. Filter traffic only from Mobile, WAP, Android and iOS. (Desktop show spikes too, but not all the time. IF we put Desktop in the calculation, it will reduce the contrast, making it harder to read.) 3. Use Google Trend (to analyse search terms).
Cancellation Rate
Overview
What did we do?
Result
Fraud Install Detector
Ads Agency
Machine Learning@Tokopedia
Fighting transactional fraud at Tokopedia
Fraudulent transactions are handled MANUALLY using a backend system
Only using Rule Engine and later on we counting on MANUAL INSPECTION
However, number of cases overwhelms fraud team, it leaves a lot of unhandled cases.
Solutions?
Using machine learning to discover fraudulent pattern rather than manually checking similar cases over and over again.
We use machine learning model alongside the existing rule engine.
Transaction are not the only place for fraud
What about MESSAGING?
USD 2 million
USD 230k
USD 600k
Don’t think “someone else will join and take care of this” — Mike Krieger of Instagram
