This document discusses data-driven innovation at Tokopedia. It describes how Tokopedia used data analysis to optimize their TV commercials by tracking mobile traffic to their site after ads aired. It also discusses how they developed a machine learning model to detect fraudulent transactions, as manually reviewing large numbers of transactions was overwhelming their fraud team. The document notes they later expanded this approach to detecting fraud in messaging as well. It provides examples of the significant costs of fraud they were able to reduce through these data-driven approaches.

1. 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

5. ?
Online Forums
Social Media
Blog Platform

14. Data-Driven Mindset

16. TVC Optimization

17. Before

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).

22. Cancellation Rate

23. Overview

24. What did we do?

25. Result

26. Fraud Install Detector

27. Ads Agency

30. Machine Learning
@Tokopedia

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.

34. Transaction are not the
only place for fraud
What about
MESSAGING?

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