From open communication to closed interaction, the ecosystem of social data is constantly changing and evolving. As a social analytics provider, how do you adapt and capture the new norms of social insights? Facebook topic data is born in the wake of shifting consumer behaviors and growing privacy concerns. With its privacy first model, the new type of aggregated and anonymized data coupled with its multi-dimensionality allow for virtually unlimited number of ways to surface audience insights from the largest source of public opinion. The good news is that we already handled the heavy lifting in processing the billions of daily interactions on Facebook. The rest lies in how you can leverage PYLON and the tools we created for you to innovate and differentiate your product in the new paradigm of audience insights. Join us for our upcoming webinar and learn: About the difference between public and non-public data sources and the philosophy behind our PYLON design Explore the tools and techniques we developed to help you innovate and differentiate your product Have your questions about Facebook topic data answered

1. How to Build
Innovative Products
with Facebook Topic Data

2. Tim Budden
VP, Data Science
DATASIFT
Jay Krall
Director of Product Management
DATASIFT
Dary Hsu
Product Marketing Manager
DATASIFT

3. Intro to Facebook topic data
Agenda
1
2
3
4
5
Evolution of social data
Philosophy of Facebook topic data
Product Differentiation
Q&A

4. Intro to Facebook Topic Data

5. For years, companies struggled to get a complete view of their
audience on Facebook and turn that information into useful insights
until….

6. DATASIFT + FACEBOOK Partnership
ENGAGEMENT ACROSS FACEBOOK
FACEBOOK TOPIC DATA
Topic Data Unlocks Unique Insights for Marketers

7. What is Facebook Topic Data?
What’s on your mind?
CONTENT DEMOGRAPHICS LIKES and SHARES
Anonymized and aggregate topic data
• Posts
• Pages Posts
Plus engagement data
• Likes on Posts
• Shares on Posts
• Comments (no text) on Posts
Data enriched with
• Demographics
• Topics
• Sentiment
• Real-time access to the entire newsfeed with over 4.75 billion pieces of content shared a day.
• Gain anonymous & aggregated insights about specific activities, events, brand names, and other
subjects that people are sharing on Facebook.

8. Insights From a Network of 1.59 Billion People
WITHOUT FACEBOOK TOPIC DATA + FACEBOOK TOPIC DATA
Analysis across public social data sources
Example: Analysis of automotive brand
6x
Analysis includes Twitter, Tumblr, blogs, forums.

9. Evolution of social data

10. The evolution of social data
From public to non-public spaces:
Public Walled 1 to 1 Image-based

11. Public
Where brands and consumers most commonly engage
directly. This is where customer support and brand
perception can be addressed directly by a brand.

12. Walled garden
Users engage each other in a non-public but large network. This
is where users are more candid about their aspirations and
attitudes toward brands.

13. 1 to 1
Users engage each other directly on a one-to-one or small
group basis. Thus far this space has been considered largely
off limits to brands, but that is starting to change.

14. Image-based
Public spaces where people showcase their best
visual content.

15. Philosophy of PYLON

16. How can information useful for businesses be extracted from
these non-public spaces, while wholeheartedly respecting
people’s privacy?

17. Think in terms of audiences and demographics not individuals
17
Djokovic
Federer
female male
Henman Hill at Wimbledon
Come on
Djokovic! Come on
Roger!
Great shot
Federer! Go for it
Novak!

18. Think in terms of topics and attitudes not verbatim
Sumptuous
interior!
Lots of
storage
Beautiful
lines!

19. How does PYLON support this?
User identity is removed from posts and
engagement data processing.
Text and meta data from anonymized posts
are indexed within Facebook’s infrastructure
for analysis.
Developers query data collected in real-
time to perform analysis. Data is aggregated at
query time to provide aggregate results.
Privacy controls ensure results only provided
if audience size thresholds are met.

20. CONTENT
Gender: Male
Age Range: 35-44
Region: California, USA
CONTENT
Negative
Neutral
Positive
DEMOGRAPHICS
SENTIMENT
Automatic classification
of related topics
e.g. Star Wars VII (Film)
TOPIC ANALYSIS
CONTENT
LINKS
Analyze
URLs shared
across Facebook
Engagement and Demographics
around Likes, Comments and Shares
ENGAGEMENT
Can’t wait to take the kids to watch Star Wars VII
CONTENT
Privacy-safe
aggregate analysis of
text
TEXT ANALYSIS
Topic Data is Multi-Dimensional.
Build Insights into Content, Engagement, Audiences

21. Product differentiation

22. VEDO custom tags
Create custom tagging and scoring rules using VEDO
to apply your unique understanding of the industry
and product to add value to the data and surface
deeper insights.
Example:
• Expressions of intent
• Expressions of emotions
• Product features (style, cost, reliability …)
• Media types (blogs, news, video …)
• Domain expertise

23. Baselining comparisons
Example:
• Comparing engagements with a car
maker vs engagement around
automotive in general.
Baselining is a technique for understanding data in
context that allows you to compare one set of results
to another and find the outliers.

24. Complex queries
Nested analysis queries allow each result of a
frequency distribution analysis to be broken down by
the values of another target with only a single request
to the API.

25. Industry-specific indexes
Build industry specific insights by leveraging your
domain expertise to create repeatable indexes specific
to the needs of the market segment you serve.
Example:
• Film
• TV
• Fashion
• Sports

26. Historical archive of insights
Export your analysis results and build an archive of
insights to measure the evolution of topics or simply
understand the impact of a topic at any given time in
the past.

27. Q&A

28. THANK YOU