The document discusses how data science can be used both to assist and replace humans. It provides examples of how data and machine learning are used at Yandex to target ads, recommend matches on dating sites, and optimize websites based on user behavior and testing. The key takeaways are to use data for instant algorithmic decisions, mine data for insights to better understand users, and determine which decisions are best made by machines versus humans.

2. Two Faces of Data Science:
Assisting Humans, Replacing Humans
Andrey Sebrant
Yandex
Director, Product Marketing
Lviv, Ukraine
October 2014

3. Crossing the Trough
The period of disillusionment is dangerous,
please take special care ;)
│ http://www.economist.com/blogs/babbage/2014/08/difference-engine-2

4. What old tradition tells us
│ From ChaosBook.org & Wikipedia

5. Data Science != Analytics
Classic:
Deep understanding
Human mind
Building models
Long process
(sometimes months and years)
Modern:
• Machine learning
• Huge computations
• Predictive algorithms
• Real time
(often less than a second)

6. Data Science != Analytics
Classic:
•Assisting
Modern:
•Replacing

7. Data Science != Analytics
Classic:
• Human readable output
Modern:
• Machine readable output
http://blogs.hbr.org/2014/08/the-question-to-ask-before-hiring-a-data-scientist/

8. Here come Psychology
› Do you like anti-spam protection at your e-mail service?
› Do you like context ads next to your private e-mails?

9. Good news
When we do “reverse engineering”
of decision-making by a machine,
we often receive:
│Sanity check
│ Useful insights

10. Case study Yandex Crypta
Look-A-Like in Ad Targeting
(and what do they search)

11. 11

12. 12
Light TV-viewers: methodology
User Survey
•TNS forms
•4 questions
•Panel survey
by OMI
•28’000 users
Cookie matching
OMI-Yandex
•Matching OMI
panel users and
Yandex visitors
Online behavior
patterns across the
Internet
•Crypta
technology
•200 factors
of user
behavior

13. 13
Heavy TV viewers Light TV viewers
«сбербанк», «коммунальный»,
«шарлотка», «выкройка»,
«биглион», «irr», «заработать»
«книга», «переводчик»,
«словарь», «формула»,
«японский», «французский»,
«немецкий», «такси»
Больше запросов кириллицей Много запросов латиницей

14. 14
Heavy TV viewers Light TV viewers
«тнт», «дом-2»,
«телепрограмма», «стс»
«С++», «wi-fi»,
«фотошоп», «torrent»,
«adobe»

15. 15
Heavy TV viewers Light TV viewers
«спартак», «цска», «пиво» «загранпаспорт», «авиабилет»,
«виза», «самолет»,
«аэропорт», «ржд»

16. Case Study eHarmony
Finding A Perfect Mate
(and insights in psychology)

21. 21
Choose the userpic carefully
• Bigger does not
mean better (in
terms of contact
probability)
• And beautiful
landscape does not
help much ;)

23. Case Study Landing Page and Action Button
Showing you exactly
what you like to click
Respond to a person,
not a device

24. Responsive design?
No.
Design, tailored for you
(and your engagement)
1.Antic:
«I said THIS one!»
2.Advanced:
«We did A/B testing»
3.Data driven:
«We know which one you click
with higher probability»

25. Important takeaways
│1. Use data for making instant algorithmic
decisions whenever it’s possible
│2. Mine data to get insights which can make your
product unique: data will help you understand
your users better than any traditional research
│3. Learn which decisions are better made by
machines, and which – by humans. And never
mix the two ;)

26. Thank you! ;)
+7 (985) 762-4925 @asebrant
asebrant@yandex-team.ru
https://www.facebook.com/asebrant
Andrey Sebrant