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Data-Driven Innovation
Once Upon a Time
In 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
07 - IDNOG04 - Leontinus Alpha Edison (Tokopedia) - Data Driven Innovation

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07 - IDNOG04 - Leontinus Alpha Edison (Tokopedia) - Data Driven Innovation

  • 2. Once Upon a Time In Jakarta, Jan 2009
  • 3. 1 Product Guy 1 Half Engineer as co-founder Don’t have business background AT ALL
  • 4. 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
  • 6.
  • 7.
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  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 15.
  • 18. 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).
  • 19.
  • 20.
  • 21.
  • 24. What did we do?
  • 28.
  • 29.
  • 31. 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.
  • 32. 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.
  • 33.
  • 34. Transaction are not the only place for fraud What about MESSAGING?
  • 35.
  • 36.
  • 37. USD 2 million USD 230k USD 600k
  • 38. Don’t think “someone else will join and take care of this” — Mike Krieger of Instagram