Social Insights 2014: New York - EdgeRank Checker

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Social Insights 2014: New York - EdgeRank Checker

  1. 1. Socialbakers + EdgeRank Checker
  2. 2. Facebook’s News Feed Main Facebook experience 1,500 available objects High competition
  3. 3. We study & analyze the news feed EdgeRank Checker
  4. 4. Key Study : Organic Reach Decrease
  5. 5. Key Study : Hashtags Impact
  6. 6. ● Size ● Category ● Post Frequency Studying the Influence of the Algorithm ● Historic Engagement Levels ● Historic Reach Levels ● Historic Negative Feedback ● Content Type Distributions With this data, we can further extrapolate more important information. What we know about any given Facebook Page:
  7. 7. Studying the Influence of the Algorithm The input goes into this black box that we'll call "the algorithm," this is our variable X. Facebook then gives us an output, which is your Insights API data. We view this as our input.
  8. 8. Input * X = Output
  9. 9. Measure the impact of the algorithm Use large shifts in the data to analyze changes Keep track of over time Studying the Influence of the Algorithm
  10. 10. SB + ERC In-depth news feed analysis is now available This approach and logic to the News Feed is now accessible to the Socialbakers family!
  11. 11. The ERC Product ● Actionable Insights ● Deep Learning for Recommendation Engine ● Post Grading ● Negative Feedback Analyzer
  12. 12. Actionable Insights
  13. 13. Deep Learning for Recommendation Engine
  14. 14. Post Grading
  15. 15. Negative Feedback Analyzer
  16. 16. What Does SB Do For ERC? Significantly Improve Backend Increase Site Speeds Ability to Truly Scale New API Opportunities
  17. 17. The Future The key is to understand what causes us to interact with a piece of digital content.
  18. 18. ● Facebook’s API provides the richest data set to begin to analyze these tendencies ● Interaction Optimization will become Platform Agnostic The Future
  19. 19. Q & A @chadwittman

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