My presentation at #BWELA 2011. Main points are: 1. Learn about the different social data types 2. Learn how to identify social customer segments 3. Learn how to integrate social consumer data with internal customer data

1. Social Data Analytics
Understanding social data types, segmenting social consumers and social data integration
Nov 2011

2. Agenda
• Learn about the different social data types
• Learn how to identify social customer segments
• Learn how to integrate social consumer data with
internal customer data

3. Social Data Types
(Way Beyond “Likes”)

4. Social Participation Skyrockets
And so does data generation….

5. Social Data Explosion
More than 900 million objects that More than 200 million
people interact with each day (pages, Tweets per day
groups, events and community pages)
These activities
generate
valuable social
data that can
reveal NEW
knowledge
about consumer
behavior
More than 3
million More than 3 billion
checkins per videos viewed per day
day
Sources: Facebook, Twitter, YouTube, Foursquare

6. Example: Anatomy of a Tweet
Tweet Meta-data
may contain:
-Demographic data
-Psychographic data
-Location data
-Intention data
-Referral data
-Sharing data

7. Companies Struggling to Cope
Data generation is rapidly
outpacing our ability to
manage and understand
how to use it.
Sources: IBM CMO Study http://bit.ly/shpHb3

8. Resources and Needs Not Aligned
The talent gap is widening.
Current supply is a shallow
pool that cannot meet
growing demands.
Hiring from the outside isn’t
the silver bullet (or even a
realistic option in many
cases). Internal education is
critical.
Sources: IBM CMO Study http://bit.ly/shpHb3

9. Use the Right Data for the Job
Q: Is social
contributing to our
enterprise goals?
A: Well, we have
2,834 Facebook
That’s not cutting it!
Likes…

10. The Social Data Tree
Location
Behavioral Sharing
Referral
Intention
Brand/Product
Psychographic
Demographic

11. Demographic Data
Age Education
Gender Income
Race
- Used for making broad generalizations about groups of people
- Most common data type used
- Has limitations. Not all individuals will conform to the profile
- Table stakes. Start here and layer on other data types

12. Psychographic Data
Personality Lifestyles
Interest
Values
s
Attitude
- Combined with demographics, used for highly targeted outreach and/or advertising
- Self reported by consumers on social platforms
- Gives companies opportunities to better understand and align with consumer needs, wants
and expectations. (a.k.a. to be helpful and relevant).

13. Intention Data
Desired Planned
State Events
Desired Planned
Product Activities
- Good for understanding what consumers “want” or “want to do”
- Least accurate data type, can lack contextual relevant details (ex: in-market)

14. Behavioral Data
Past actions, activities that can be used as a
predictor of future intentions
- Allows companies to more appropriately target segments for better marketing results
- Allows companies to use personal preferences and interests to move closer to a true one-to-
one relationship with their customers

15. Location Data
Physical location of a consumer
- Provides contextual understanding at a certain point in time
- Can trigger contextually relevant promotions and/or rewards
- Can be used to identify intent
- Provides opportunity for brands to improve offline customer experiences by understanding
behavior patterns in location and opinions shared with it

16. Referral Data
Ratings Rewards
Non-Verbal
Reviews
Gestures
- Can be used to identify brand promoters, detractors
- Provides insight into product/service attributes matter most

17. Brand/Product Data
Brand/Product related conversations
Product
Attribute Conversation
s Product types
Measures
- Can provide richer understanding of consumer perspective on specific aspects of products
and/or brand measures
- Represents unfiltered consumer feedback
- Helpful in optimizing product launches, product development roadmaps, and content
strategies

18. Sharing Data
Reach Clicks
Conversion
Shares s
Generational
Sharing
- Identify brand advocates that actively spread branded content
- Learn how branded content travels through generational sharing
- Understand word of mouth, what they share, why they share it

19. Social Data Segmentation
(Segment or Die)

20. Aggregate is the Enemy
“All data in aggregate is crap”
–Avinash Kaushik
All Visitors?
Total Pageviews?
Total Likes?
People Talking About This?
= Gratuitous Data Puking
Followers/Fans/Friends?
The list goes on…..

21. Social Segmentation Benefits
Why should Analysts care?
-Find the answers to the “why” and “how” questions of consumer
behavior
-Go beyond data puking! (no more “hits” reporting)
Why should Marketers care?
-Get closer to the nirvana of personalized, one-to-one relationship w/
consumers. Build value into every engagement!
-Experiences and information need the right audience first and
foremost Find the right audience.
-Improved targeting (outreach/advertising) Ex: Facebook uses a
combination of demo, psycho, product, location, and referral data
-Identify and convert the most profitable customer segments
-Prioritize consumer segments and align marketing investment against
it (we can measure their outcomes!)

22. Social Segmentation Methodology
1. Find a unique ID (email, twitter handle, linked profile, etc…)
2. Search social profiles using unique id
3. Calculate and match to individual
4. Expansion. Build out social profiles of specific individual, across data types mentioned above
5. Optional: content consumption/creation mapping, influence analysis
6. Scale across your unique ID list (aka customer)
7. Segment results.

23. Social Segmentation Tools/Vendors
Main Capabilities
Aggregates user information
by resolving email addresses
or social networking handles
Some offer APIs that can
match email addresses to
profile URLs across 125 social
networks
Can integrate with offline
data sources as well
(telephone, identity, location)

24. Social Segmentation Tools/Vendors
Capabilities FlipTop FullContact Qwerly PeekYou Rapleaf Rapportive Spokeo
Social affiliations (profiles)
Demographics
Geographic
Interests
Career/Education
Wealth/Finance
Health
Linked URLs
Reverse email search
Reverse phone search
API access
Partner with 3rd party info
Influencer Measurement
Available Available but not comprehensive Unavailable
2
4

25. Social Segmentation Examples
People search by
username/profile
name
Social profiles
identified and tied
to real identity

26. Social Segmentation Examples
Segment Non-Social Visitors from
Social Visitors
‣ Value of FB Fans and social
engagement
‣ Leverage messaging tactics for
performance improvement and
increased conversions

27. Social Segmentation Examples
Empirical research suggests that the lifetime value of a promoter is worth
at least 4-5x that of passives (neutral customers) or detractors (unhappy
customers).
Promoters buy more often, spend more, refer more, and cost less to
acquire and serve.
Net Promoter ScoreTM is a customer loyalty metric developed by Fred
Reichheld, Bain & Company, and Satmetrix. It categorizes customers
into three segments; promoters, passives, and detractors.

28. Social Data Integration
(bridging the gap)

29. This is just the beginning…
“The merger of unstructured and
structured data will fuel the next
revolution in business intelligence”
-Prasanna Dhore
Global Customer Intelligence, HP

30. Why Social Data Integration?

31. Social Data Integration Framework

32. Social Data Integration Example - HP
Unstructured data: product
conversations from social media
Structured data: purchase and service
history from crm and support
databases

33. Social Data Integration Example - HP
Classify each product conversation from social according to product attributes

34. Social Data Integration Example - HP
Connect the dots to structured sales and service data. Improve sales forecasting
and service/support resource management

35. Social Data Integration Example - HP
HP Social Data Integration Results:
-Strong social data signals about a product were a
leading indicator for increased sales and
registrations (use cases: marketing optimization by spend, audience and channel)
-Strong negative sentiment in media signals about
a product were a leading indicator of increased
support tickets/calls (use cases: operations efficiency. Managing customer support resources)

36. Ken Burbary
VP, Group Director, Social Strategy & Analysis
ken.burbary@digitas.com
@kenburbary
http://www.digitas.com
http://www.kenburbary.com