It seems you can’t go a day without reading or hearing about social’s newest sweetheart – Pinterest. The social network has exploded in the last few months. To get a handle on the opportunities, we needed to sift through the buzz, explore the usage patterns and analyze the data. So we worked with HPCC Systems from LexisNexis® Risk Solutions – an open source, enterprise-proven Big Data analytics provider.

1. Is Social Media’s Newest Sweetheart the Real Deal?
@interpolate @teresacaro
DOWNLOAD THE PAPER
www.engauge.com/insights/pinterest
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2. THE SPOTLIGHT
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3. YOUR CONSUMERS
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http://www.prav-talks.com/wp-content/uploads/2011/10/facebook-marketing.jpg

4. YOUR WEBSITE
@teresacarohttp://startinbusiness.com.au/wp-content/uploads/2011/05/website-building.jpg #pingauge
http://www.123rf.com/photo_7053272_a-vintage-theater-spotlight-on-a-white-background.html

5. YOUR CONTENT
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6. CONNECTIVE TISSUE
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7. AGENDA
• Definition: What is Pinterest?
• Trends: What is Driving the Pinterest Craze?
• The Data
• The Opportunity
• HPCC Systems for LexisNexis® Risk Solutions
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8. WHAT IS
PINTEREST?
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9. A VIRTUAL PINBOARD
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10. IMAGES FROM MULTIPLE SOURCES
Upload it - “Pin-it” - Pinmarklet
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11. FOR VARIOUS NEEDS
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12. CREATING DIY SUPER HEROS
A recent Engauge Power Panel study* showed that
90% of people said they use Pinterest to
get ideas. Some even said that it makes them “feel
like they can do anything.”
*External and internal polling group
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13. AND A POWERFUL MARKETING TOOL
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14. WHAT’S DRIVING
THE PINTEREST
CRAZE?
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15. LIFE IS FLUID & NEEDS TO BE SHARED
• Volume & availability of content
• Niche communities
• Growing comfort & interest in curating and sharing content
Planning Experiencing Recapping
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16. CONTENT CREATION TO CURATION
“People stare into a fire hose of information every
day, and it’s having an impact. They’re actively seeking
ways to not only filter and organize what they find,
but also to less stressfully consume more content.”
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17. THE DATA
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18. METHODOLOGY
We obtained the data below by reviewing publicly accessible Pinterest
user profiles and cleaned, processed and analyzed the data using the
HPCC Systems Big Data platform.
The bulk of the data discussed in this paper was obtained from spidering through
Pinterest user profiles obtained from Google search results and scraping the relevant
HTML. We also used frequency lists of gender and forename provided by the U.S.
Census to help determine the gender of Pinterest users. The scraping method was
chosen because Pinterest did not have an API at the time analysis began. After this
project had started Pinterest briefly announced an API, then pulled all information
about it, but left certain parts of the API publicly accessible.
@engauge @interpolate

19. THE AVERAGE PINTEREST USER
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20. WHERE PINS COME FROM
The top ten sources of pins by number of pins with the percentage of
total pins, and percentage of the top 10.
@engauge @interpolate

21. SOURCES OF PINS BY PERCENT OF TOTAL
AND PERCENT OF TOP 10
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22. CATEGORIES
* Top categories and percent of Top10 by category.
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23. LOCATION. LOCATION. LOCATION.
* Top locations and percent of Top10 by location.
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24. TOP 10 SOURCES BY CONTRASTING CITY
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25. OPPORTUNITIES
FOR BRANDS
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26. SHEER MOMENTUM
Month;over;Month(Referral(Traffic(Growth(
50%%
40%%
30%%
20%%
10%%
0%%
PINTEREST( LINKEDIN( YOUTUBE( FACEBOOK( TWITTER( GOOGLE( GOOGLE+(
!10%%
!20%%
@interpolate * January 2012 study by Shareholic #pingauge

27. SEARCH ENGINE OPTIMIZATION
PINS + BACKLINKS = VOTES
• http://hlcaldwell.wordpress.com/2011/10/22/an-interest-in-pinterest/
• http://www.can2010angola.com/wp-content/uploads/2011/10/backlinks.jpg
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28. CONNECTIVE TISSUE
Your website is your hub. Social is the connective tissue.
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29. YOU’RE ALREADY THERE
You don’t need a brand page to have a presence. People might
be pinning your stuff already.
Even if you don’t
have a page, you
might be there
anyway.
@interpolate Exchange your URL: http://pinterest.com/search/?q=coca cola #pingauge

30. FEMALE BUYING POWER
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31. FEMALE BUYING POWER
Women control 80% of household discretionary
spending (according to the U.S. Census Bureau).
Clearly, if women are important to your brand, then
you want to reach them here.
@teresacaro #pingauge

32. FEMALE BUYING POWER
Women control 80% of household discretionary
spending (according to the U.S. Census Bureau).
Clearly, if women are important to your brand, then
you want to reach them here.
Yet, women aren’t the only ones
@teresacaro #pingauge

33. FEMALE BUYING POWER
Women control 80% of household discretionary
spending (according to the U.S. Census Bureau).
Clearly, if women are important to your brand, then
you want to reach them here.
Yet, women aren’t the only ones
on Pinterest...
@teresacaro #pingauge

34. “BOARD OF MAN”
Started on a
lark, it now has
211,000
followers.
@interpolate #pingauge #pingauge

35. CHOBANI GREEK YOGURT
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36. WHAT THEY’RE DOING RIGHT
• Humanizing their brand
• Promoting yogurt consumption through recipes
• Allowing users to self-select content
• Limiting the number of pinboards and
pins
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37. GETTING STARTED
(OTHER STUFF TO KNOW)
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38. BEWARE OF BRAND SQUATTING
pinterest.com/
zapposdotcom
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39. COPYRIGHT MATTERS
Always read the fine print
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40. @teresacaro #pingauge

41. HAVE A PLAN
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42. FINAL WORD
• Know your consumers.
Where they want to engage and why
• Fully optimize your website and its content.
Your pictures are revenue drivers
• Leverage social to connect it all together.
An integrated effort drives powerful results
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@engauge

43. LEXISNEXIS
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44. INTRODUCTION
http://hpccsystems.com
LexisNexis® Risk Solutions
 More than 15 years of Big Data experience
 Provides information solutions to enterprise customers
 Generates $1.4 billion in revenue (2010)
HPCC Systems
 Launched in June 2011
 Open source, and enterprise-proven Big Data analytics platform
 Helps enterprises manage Big Data at every step in the Complete Big Data
Value Chain
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45. HPCC and Machine Learning
http://hpccsystems.com
HPCC is a massive parallel-processing computing
platform
Truly Parallel Machine Learning Algorithm Execution
E
S
P
Risk Solutions

46. THE COMPLETE BIG DATA VALUE CHAIN
http://hpccsystems.com
Discovery &
Collection Ingestion Integration Analysis Delivery
Cleansing
Collection – collecting structured, unstructured and semi-structured data
Ingestion – consuming vast amounts of data including extraction,
transforming and loading
Discovery & Cleansing - clean up, formatting and statistical analysis of the
data
Integration – linking, indexing and data fusion
Analysis – statistics and machine learning
Delivery – querying, visualization, and redundancy, enterprise-class
availability
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47. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
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48. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
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49. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
Risk Solutions
43

50. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
 What if you wanted to learn from it?
Risk Solutions
43

51. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
 What if you wanted to learn from it?
 Understand trends
Risk Solutions
43

52. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
 What if you wanted to learn from it?
 Understand trends
 Classify into categories
Risk Solutions
43

53. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
 What if you wanted to learn from it?
 Understand trends
 Classify into categories
 Detect similarities
Risk Solutions
43

54. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
 What if you wanted to learn from it?
 Understand trends
 Classify into categories
 Detect similarities
 Predict the future based on the past… (No,
not like Nostradamus!)
Risk Solutions
43

55. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
 What if you wanted to learn from it?
 Understand trends
 Classify into categories
 Detect similarities
 Predict the future based on the past… (No,
not like Nostradamus!)
 Machine learning is quickly establishing as an
emerging discipline.
 But there are challenges with ML in big data:
 Thousands of features
 Billions of records
Risk Solutions
43

56. MACHINE LEARNING IN BIG DATA
http://hpccsystems.com
 How do you extract value from big data?
 You surely can’t glance over every record;
 And it may not even have records…
 What if you wanted to learn from it?
 Understand trends
 Classify into categories
 Detect similarities
 Predict the future based on the past… (No,
not like Nostradamus!)
 Machine learning is quickly establishing as an
emerging discipline.
 But there are challenges with ML in big data:
 Thousands of features
 Billions of records
 The largest machine that you can get,
may not be large enough…
 Get the picture?
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57. ECL-ML: HPCC SYSTEMS MACHINE LEARNING
http://hpccsystems.com
 A fully distributed and extensible set of Machine Learning techniques for
Big Data
 State of the art algorithms in each of the Machine Learning domains,
including supervised and unsupervised learning:
 Correlation
 Classifiers
 Clustering
 Statistics
 Document manipulation
 N-gram extraction
 Histogram computation
 Natural Language Processing
 Distributed and parallel underlying linear algebra library
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58. TAKE AWAYS
http://hpccsystems.com
 A fully parallel set of Machine Learning algorithms on Big Data
gives you full insight
 Outliers matter, especially when those outliers are the exact
reason for the discovery effort (for example, in anomaly
detection)
 Dimensionality reduction can conduce to information loss:
why risk losing valuable information when you can have it all?
 Leveraging a fully parallel machine learning solution on Big
Data will help you find fraud, bring products to market faster,
and become more competitive
 Organizations that continue to do machine learning on
summarized data risk losing ground to their competitors
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59. Conclusion
http://hpccsystems.com
Q&A
Thank You
Web: http://hpccsystems.com
Email : info@hpccsystems.com
Contact us: 877.316.9669
LexisNexis and the Knowledge Burst logo are registered trademarks of Reed Elsevier
Properties Inc., used under license. Other products and services may be trademarks or
registered trademarks of their respective companies. © 2012 HPCC Systems. All rights
reserved.
Risk Solutions

60. THANK YOU.
Engauge is a full-service marketing agency for the
digital and social age. We help grow our clients'
businesses by leveraging creativity and technology to
connect brands and consumers through the most
relevant content and channels.
WWW.ENGAUGE.COM
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