two faces of data science

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Data Science in Marketing: cases, opportunities and misunderstandings

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Page 1: Two faces of Data Science
Page 2: Two faces of Data Science

Two Faces of Data Science:Assisting Humans, Replacing Humans

Andrey Sebrant

YandexDirector, Product Marketing

Lviv, UkraineOctober 2014

Page 3: Two faces of Data Science

Crossing the Trough

The period of disillusionment is dangerous,please take special care ;)

│ http://www.economist.com/blogs/babbage/2014/08/difference-engine-2

Page 4: Two faces of Data Science

What old tradition tells us

│ From ChaosBook.org & Wikipedia

Page 5: Two faces of Data Science

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)

Page 6: Two faces of Data Science

Data Science != Analytics

Classic:

•Assisting

Modern:

•Replacing

Page 7: Two faces of Data Science

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/

Page 8: Two faces of Data Science

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?

Page 9: Two faces of Data Science

Good news

When we do “reverse engineering” of decision-making by a machine, we often receive:

│ Sanity check│ Useful insights

Page 10: Two faces of Data Science

Look-A-Like in Ad Targeting(and what do they search)

Case study Yandex Crypta

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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

Page 13: Two faces of Data Science

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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

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Heavy TV viewers Light TV viewers

«тнт», «дом-2», «телепрограмма», «стс»

«С++», «wi-fi», «фотошоп», «torrent»,

«adobe»

Page 15: Two faces of Data Science

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Heavy TV viewers Light TV viewers

«спартак», «цска», «пиво» «загранпаспорт», «авиабилет», «виза», «самолет», «аэропорт», «ржд»

Page 16: Two faces of Data Science

Finding A Perfect Mate(and insights in psychology)

Case Study eHarmony

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• Bigger does not mean better (in terms of contact probability)

• And beautiful landscape does not help much ;)

Choose the userpic carefully

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Showing you exactly what you like to click

Respond to a person, not a device

Case Study Landing Page and Action Button

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Responsive design?

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»

No.Design, tailored for you (and your engagement)

Page 25: Two faces of Data Science

Jpg, Png

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 ;)

Page 26: Two faces of Data Science

Thank you! ;)

[email protected]

+7 (985) 762-4925 @asebrant

https://www.facebook.com/asebrant

Andrey Sebrant