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Data Science
1.
> Data Science
< Where does a Physics degree fit?
2.
> Life as
a Data Scien/st § What is it? § Data Science Examples § Why you should think about it? § About Datalicious (my current company) August 2011 © Datalicious Pty Ltd 2
3.
101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > What
is it? August 2011 © Datalicious Pty Ltd 3
4.
> Mixing the
old to produce new Business Marke/ng / Performance Adver/sing IT / Coding Sta/s/cs Data Science August 2011 © Datalicious Pty Ltd 4
5.
> By Defini/on
Data: “Facts and sta)s)cs collected together for reference or analysis” Science: “The systema)c study of the structure and behaviour of the physical and natural world through observa)on and experiment” Hmmm, so what? August 2011 © Datalicious Pty Ltd 5
6.
> Sounds like
nothing new § Data, Science, ObservaPon, Hypothesis, Experiment, Analysis, PredicPon à are all nothing new. § BUT, the digital age has created new opportuniPes where scienPfic methods can be applied to massive, real world digital data sets. August 2011 © Datalicious Pty Ltd 6
7.
> And Data
is Exploding “Every 2 Days We Create As Much Informa)on As We Did Up To 2003” – Eric Schmidt. CEO, Google “AdMob Seeing 2 Billion Ad Requests Per Day; Up 300 Percent Over Past Year” – TechCrunch “The amount of digital informa)on created annually will grow by a factor of 44 from 2009 to 2020, as all major forms of media -‐ voice, TV, radio, print -‐ complete the journey from analog to digital” August 2011 © Datalicious Pty Ltd 7
8.
> EMC -‐
"The Digital Universe Decade -‐ Are You Ready?" “In 2009, amid the "Great Recession," the amount of digital informa)on grew 62% over 2008 to 800 billion gigabytes (0.8 Zeabytes).” But how much is that really? 707 trillion copies of the more than 2,000-‐page U.S. PaPent ProtecPon and Affordable Care Act signed into Law in March 2010. Stacked end to end, the documents would stretch from Earth to Pluto and back 16 Pmes or cover every inch of the United States in paper 3 feet deep August 2011 © Datalicious Pty Ltd 8
9.
[ Mo/va/on ]
Data = © Datalicious Pty Ltd 9
10.
101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Data
Science Examples August 2011 © Datalicious Pty Ltd 10
11.
> Search and
the product lifecycle Nokia N-‐Series Apple iPhone October 2010 © Datalicious Pty Ltd 11
12.
> Financial Predic/on
© Datalicious Pty Ltd 12
13.
> Trigger based
Sales 2 years on the beach Iden/fies themself User visits User visits (e.g. sale or Looks at lots of Site Site again registra/on) ‘widgets’ anonymously ‘anonymously’ Cookie Web Cookie Analy/cs Database Hi John, long /me no talk, we have a special Name: John Example on widgets! Interest: Widgets History: Business -‐Last visit 2 years ago. Intelligence -‐Purchased 10 blue widgets Database -‐High value band Loca/on: 2000, Sydney © Datalicious Pty Ltd 13
14.
> The science
of Banner Ads Control Group Normal Display Adver/sing (90%) (10%) Impressions = 1 M Impressions = 9 M Visitors = 1,000 Visitors = 10,000 Sales = 50 Sales = 550 Cost = $1,000 Cost = $9,000 $ per sale = $20 $ per sale = $16 Without display With display adverPsing, 1 in every 20,000 people 22% adverPsing 1 in every 16,363 people will convert uplie will convert -‐ Banner Ads do influence sales despite what people think -‐ Even if you don’t click on them © Datalicious Pty Ltd 14
15.
[ Affinity targe/ng
in ac/on ] Different types of visitors respond to different ads. By using category affinity targePng, response rates are lieed significantly across products. Click-‐Through Rate By Category Affinity Message Postpay Prepay Broadb. Business Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - + © Datalicious Pty Ltd 15
16.
14/11/12
© Datalicious Pty Ltd 16
17.
101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Why
should you do it? August 2011 © Datalicious Pty Ltd 17
18.
> The start
of a trend… August 2011 © Datalicious Pty Ltd 18
19.
> Big Data
also… August 2011 © Datalicious Pty Ltd 19
20.
> Supply is
way behind demand… August 2011 © Datalicious Pty Ltd 20
21.
IT needs more
Scien/sts August 2011 © Datalicious Pty Ltd 21
22.
> Difficult to
believe? § Marketers typically just don’t get it, but their bosses now know you can measure the ROI of digital Ads, so they’re screwed without the data scienPsts § The business guys mostly (not all) see the value, but the Internet wasn’t around when they were back in Harvard, so they can’t do it alone § Developers are predominantly good at following a spec. They rarely understand data, staPsPcs and how to go about sejng up and analysing an experiment August 2011 © Datalicious Pty Ltd 22
23.
> You can
work across all industries August 2011 © Datalicious Pty Ltd 23
24.
> Work on
wide ranging issues… August 2011 © Datalicious Pty Ltd 24
25.
> Work from
Anywhere August 2011 © Datalicious Pty Ltd 25
26.
101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > About
Datalicious August 2011 © Datalicious Pty Ltd 26
27.
> Across major
data categories Campaign data TV, print, call center, search, web analyPcs, ad serving, etc Campaigns Customers Customer data Direct mail, call center, web analyPcs, emails, surveys, etc Consumer data Search, social media, trends, Compe/tors Consumers research, news, etc Compe/tor data Search, social media, ad spend, news, offers, etc August 2011 © Datalicious Pty Ltd 27
28.
> Defining data
strategies Con/nuous tes/ng and op/miza/on AcquisiPon Up-‐Sell RetenPon Advocacy Analy/cs and metrics frameworks August 2011 © Datalicious Pty Ltd 28
29.
> Guiding the
customer journey To transac/onal data To reten/on messages From suspect to prospect To customer Time Time From behavioural data From awareness messages August 2011 © Datalicious Pty Ltd 29
30.
> Summary §
Do you: 1. Know how to idenPfy trends in numbers and to graph data 2. Know how to write reports and validate experimental predicPons 3. Understand some business thinking, i.e. cost of sales, maximising return, etc 4. Understand the principles of wriPng code § If yes, then Data Science may be for you. August 2011 © Datalicious Pty Ltd 30
31.
Contact us
hogilvy@datalicious.com Learn blog.datalicious.com Follow twiner.com/hamishogilvy twiner.com/datalicious August 2011 © Datalicious Pty Ltd 31
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