1. Social Media & Big Data:
Implications for Marketers
March 2013

2. What is Social Media?
• Merriam Webster Online defines social media as
“forms of electronic communication (as Web
sites for social networking and microblogging)
through which users create online communities
to share information, ideas, personal
messages, and other content (as videos)”

3. Total Users of Select Social Media Sites
(March 2013)
1,200,000,000
1,000,000,000
800,000,000
600,000,000
400,000,000
200,000,000
-

4. Social Media Usage
• Facebook
▫ 67% of online adults
• LinkedIn
▫ 20% of online adults
• Twitter
▫ 16% of online adults
• Pinterest
▫ 15% of online adults

5. Facebook
• Users
▫ 167 million unique visitors per month
▫ 500 million likes per day
▫ 24% aged 35-44
▫ 58% women, 42% men
▫ 350 million users suffer from
Facebook Addiction Syndrome
• Ad policies
▫ Advertisers will be able to sync CRM
database info with Facebook user info
 Brands will be able to more
effectively target users without
waiting for them to “like” the page
 Users can opt-out
• Marketers are much more interested
in data from Facebook interactions
than less prevalent sites
• Produces a “Gross national Happiness
Index” through text mining words and
phrases posted

6. LinkedIn
• Largest professional social
network
• 2 new members sign up every
second
• 42% of users update their
profile regularly
• 65% Male, 35% Female
• 82% of users are aware there
are ads
▫ 60% have clicked
• Corporate talent solutions are
used by 85 Fortune 100
Companies

7. Twitter
• Users
▫ Adults 18-29
▫ African Americans
▫ Urban residents
▫ “The disproportionate
African-American use of
Twitter has fascinated culture
commentators and scholars”
• Ad policies
▫ Advertisers can target users
based on broad categories
▫ Categories are not created
from contents of tweets, but
other actions and who the
user follows

8. Pinterest
• 12 million unique visitors per
month
• 79% women, 21% men
• 29% of users aged 25-34
• Users have higher average
income than Facebook &
Twitter
• Average time spent on
Pinterest
▫ 1 hour 17 minutes

9. What is Big Data?
• There is no pat definition for big data… In
fact, big data can be relatively small, but
represents a difficult processing-time issue.
Basically, you’ve got big data whenever you
exceed the capacity of a conventional relational
database to handle it.” –Jim Davis, Senior
VP/CMO at SAS
▫ Much of the data mined from social media can be
considered big data, because incorporating it into
current databases and CRM systems can be
problematic.

10. Social Media Data Mining
• Social media users share a considerable amount of
information about themselves through their
posts, likes, tweets, and connections. Social media
data mining allows marketers to:
▫ Discover new niches
▫ Tailor advertisements to best meet the needs of
smaller demographic groups
▫ Identify and/or predict buying patterns
▫ Manage customer issues before they become PR
problems
▫ Conduct research to aid in the development of new
products and services
▫ Conduct sentiment analysis

11. Sentiment Analysis
• “Social media is the canary in the coal mine. It
provides early warning of issues that can become
major problems if they are not detected quickly.”
–Catherin van Zuylen, VP of Products at
Attensity

12. Sentiment Analysis
• Sentiment analysis is “… one particular form of
social media data mining, involv[ing] the
application of a range of technologies to
determine sentiments expressed within social
media platforms about particular topics, in order
to arrive at a measure of the ambient, or general
sentiment”

13. Sentiment Analysis: How it works
• Text mining
▫ Natural Language Processing
 Determines whether comments are positive, negative or
neutral by analyzing word use, order, and combinations
• Often done by third-parties
▫ Provide clients with:
 Insight on how to engage with their customers
 Community management services
 Raw data
• Began as a score or grade for the business, however
the new trend is to use the data in real time to
deepen client/customer relationships

14. The good…
• Companies want to know what people are saying about
them
• Consumers are trusting advertisements less, and peer
recommendations more
• Insight into customer opinions was previously
unavailable on such a large scale
▫ Possible Outcomes
 Better customer service
 Quick resolution of customers’ problems
 Better products and services available to consumers
 Marketers can create better messages and identify the most
efficient means of delivery
 Businesses can gain a deep understanding of their target
audience’s psychographics

15. … and the bad
• Slang, abbreviations, sarcasm are common on social networks, and
are difficult to process
• Studies have shown that it is difficult to extract sentiment from the
things/ideas people tweet/post about most
• The analysis may be inaccurate
▫ 70% accuracy is considered good
▫ Data may not be “clean”
• Monetization of personal relationships
• Social discrimination
▫ Less desirable demographic groups may be marginalized
• Positivism problem
▫ People tend to give high ratings on many sites
• General public is largely unaware of this practice
• What data is considered public on social networks? When you join a
social networking site, are you implicitly opting-in?

16. Issues in Social Media Data Mining
• While there have been few cases of the use of
data from social media sites for illegal or
unethical purposes, many in the industry believe
it is more of a matter of when, not if.
• Companies mining data from social media sites
are also very secretive about what they do, and
how they do it. This is partially due to the fact
that it is a new frontier and they do not want to
give away trade secrets, however the opacity
makes some experts nervous.

17. Issues in Social Media Data Mining
• Privacy issues
▫ Thoughts and feelings shared become part of a “vast market research project”
▫ Data is readily available through social networks and aps. The more data, the more of a chance for problems
 Employees leaking customer information
 Hackers
• Mobile
▫ Over 50% of Americans own a smartphone
 Aps have location data and access to address books in phone
 Companies can predict where users will be throughout the day
 Companies know who your friends, family, and coworkers are
• Ethics
▫ How are companies obtaining their data?
▫ Do consumers know they are being tracked?
• Legislation
▫ US
 Consumer Privacy Bill of Rights
 Federal Trade Commission legislation
▫ Loose framework
▫ Opt-out and privacy notices
▫ European Union
 “Do not track” policy
 Consumers must opt-in

18. Data Applications
• The best way to analyze data mined from social media is to use a
combination of computational and manual methods. Analytics programs
can be used to clean and help code large datasets. Human coders are then
used to check for accuracy, as computers cannot pick up on contextual
clues, sarcasm, or humor.
• Some programs that can be used for mining big data include:
▫ Apache Hadoop
▫ Apache HBase
▫ Apache Hive
▫ Cassandra
▫ Cloudera
▫ Greenplum
▫ Hadoop Distributed File System
▫ Hortonworks
▫ MapReduce
▫ MongoDB
▫ NoSQL

19. Best Practices in Social Media Data
Mining
• Employ a combination of computer technology and human analysis
▫ Even sophisticated programs have difficulty extracting meaningful insight
because of the prevalence of slang, abbreviations, humor, and sarcasm on social
media sites
• Ethical collection of data
▫ US policy is a very loose framework
▫ Collect only data that is considered public
▫ Use a reputable third party company for social media data mining to avoid ethical
issues
• Do not collect personally identifiable information
▫ Unless it will be used to resolve customer issues
• Focus on using data from big groups to create psychographic profiles and
uncover the general sentiment
• React quickly to customer problems
• “Listen” to what customers are saying to provide better products and
services
▫ As opposed to monitoring to keep control of the online conversation

20. Companies offering Social Media Data
Mining Services
• 33Across
• Attensity
• Get.It
• McKinsey Global
• Media6Degrees
• Pentaho
• PHD
• Place1Q
• SAS
• Skyhook
• WiseWindow

21. Social Media Data Mining for Marketing
• Mining and analyzing the vast amount of information
available on social networks will be a win-win situation for
marketers and consumers if ethical issues can be avoided.
Currently, companies who mine big data average 6% higher
productivity than those that do not.
• Marketers can:
▫ “Listen in” on online conversations to get a better understanding
of:
 Who their customers are
 What products they want and need
 What advertising messages are most effective
 What channels are most effective
▫ Change their messages or target groups in real-time
▫ Help manage PR issues through quick customer service
▫ Aid in the development of new products and services

22. References
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• Brenner, Joanna. "Pew Internet: Social Networking (full detail)." Pew Internet. Pew Internet, 14 Feb 2013. Web. 17 Mar 2013.
http://pewinternet.org/Commentary/2012/March/Pew-Internet-Social-Networking-full-detail.aspx.
• Delo, Cotton. "Startups Mining Social Data take on Facebook." Advertising Age. 09 Apr 2012: 3. Web. 17 Mar. 2013.
• Delo, Cotton. "You are big brother (but that isn't so bad)." Advertising Age. 23 Apr 2012: 1-19. Web. 17 Mar. 2013.
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<http://techcrunch.com/2013/02/17/social-media-statistics-2012/>.
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• “Social Media.” Merriam Webster Online, Merriam Webster, n.d. Web. 17 Mar 2013.
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