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How to Build Innovative Products with Facebook Topic Data

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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

Published in: Technology
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How to Build Innovative Products with Facebook Topic Data

  1. 1. How to Build Innovative Products with Facebook Topic Data
  2. 2. Tim Budden VP, Data Science DATASIFT Jay Krall Director of Product Management DATASIFT Dary Hsu Product Marketing Manager DATASIFT
  3. 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. 4. Intro to Facebook Topic Data
  5. 5. For years, companies struggled to get a complete view of their audience on Facebook and turn that information into useful insights until….
  6. 6. DATASIFT + FACEBOOK Partnership ENGAGEMENT ACROSS FACEBOOK FACEBOOK TOPIC DATA Topic Data Unlocks Unique Insights for Marketers
  7. 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. 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. 9. Evolution of social data
  10. 10. The evolution of social data From public to non-public spaces: Public Walled 1 to 1 Image-based
  11. 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. 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. 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. 14. Image-based Public spaces where people showcase their best visual content.
  15. 15. Philosophy of PYLON
  16. 16. How can information useful for businesses be extracted from these non-public spaces, while wholeheartedly respecting people’s privacy?
  17. 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. 18. Think in terms of topics and attitudes not verbatim Sumptuous interior! Lots of storage Beautiful lines!
  19. 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. 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. 21. Product differentiation
  22. 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. 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. 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. 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. 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. 27. Q&A
  28. 28. THANK YOU

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