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Cornell 140721-open


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talk at Cornell University

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Cornell 140721-open

  1. 1. Ask Measure Learn @LutzFinger
  2. 2. Disclaimer The points of view in this presentation are my own and might not reflect LinkedIn’s opinion.
  3. 3.
  4. 4. Who is a Statistician?
  5. 5. “So$ware  is   ea,ng  the   world.”     -­‐  Marc   Andreessen  
  6. 6. Should  I  get  an  MBA  or  learn  to  code?  
  7. 7. two like one Good big go New way see data socialmedia | measure people customer Use example many often ASK might LEARN right chapter need just users bots start 1 real set ROI 10 9 3 However Facebook question Twitter online create information metrics Even time case used reach marketing Network public within Analytics also Thus company using business Figure metric influence sales different look system customers companies PR world book get well whether articles first Best number important content product care process know similar find measurement behavior Google page News questions make easily search service intent algorithm Research value message spread Friends market Take Lets brand much predict likely work person PART kind based unstructured user may now created discussed approach tools become better issue systems around answer Technology knowledge easy success sentiment insights trust given 2011 industry others recommendation want help today potential examples article Try tweets hard predictions targeting Another purchase someone long Buy every 2012 CRM future results impact computer following Since Team products relations bot something campaign needs context networks issues meanways human possible measurements discussion already Found financial always creating order structured high CEO share done million awareness web versus automated change cost specific purchasing still comments called YouTube personal video true certain difficult means via traditional Times Without read services Group available Open predictive word behavioral analyze looking messages Next negative click fake show correlation Courtesy Second discussions able interest University followers cases movie seen turn main learning link idea clear made viral three measuring things discuss everyone 5 end spreading started Tweet form level bad 4 actually predicting engagement wrong sources small Graph keyword far Yes consumers ones understand 2 focus rate website Consumer seems fourth stock variables described positiverecommendations V build Fisheye price highly saw privacy sometimes Support Software reviews 8 action therefore intention offer commerce shows asked call improve com spot successful engine words reading algorithms overall terms Despite ideas relevant machine quite give error short Score General Figure less connections Think common Reality Define community clients key Test Netflix effect channel large 2010 Forum points Moreover Economic 6 term relationship Internet back course several Step lot early situation Professor really neverPolitical probably analysis Blog selection source Models cause stories sharing either later Michael measures LinkedIn ability United least Pointneeded tool Higher per 7 tell response reason rates known De study trying simply al great Causal react Please et spam reduce hand place gaming email model hype paid put 11 uses view fact last day top sure risk ad soon gain type due surely line Post areas views cus lead text beginning actions Index causation power rather books blogs write sense serve matter Net yet ever come Note oil re con 12 TV seem Dell aim 13 ing free comes low size Act 30 peer 46 Item name must dont box fans went 20 16 ads im meas 80 car C add Goes 70 Jan 57 six 31 21 xi BP pay 17 Win 14 76 28 led E BBC 25 23 32 age run 91 got channels believe digital simple pages amount lists list Check recent days York shown role fast drop Likes Factor efforts Majorstrong summary movies earlier actual types exist Paul topic OReilly access site 2013 looks teams story else space thing goal sets play task owned sold noted 100 hit moment track CEOs
  8. 8.
  9. 9. ASK the right Questions. MEASURE the right data – even if it is not Big data. Take Actions and LEARN from them. ?
  10. 10. One Framework?
  11. 11. ? Products Market Research Customer Care Marketing PR Sales
  12. 12. ? Products Market Research Customer Care Marketing PR Sales
  13. 13. Iden,ty   Network   Knowledge   84%  
  14. 14. This is you…
  15. 15. This is you… On  Campus  Group   EMBA  2015   EMBA  2016  
  16. 16. 2nd Cluster hidden
  17. 17. This is you… On  Campus  Group   EMBA  2015   EMBA  2016   Cornell   Cornell  Tech  
  18. 18. The Connector •  Who is connecting all 4 Groups?
  19. 19. The Connector •  Who is connecting all 4 Groups? •  Who is connecting all 3 Groups?
  20. 20. Who is connecting all?
  21. 21. Actionable Insights
  22. 22. ASK the right Questions. MEASURE the right data – even if it is not Big data. Take Actions and LEARN from them. ?
  23. 23. Actionable Insights ASK is often the hardest Part
  24. 24. How to Spot a Good “Ask”?
  25. 25. Small not BIG Data •  Connec,ons   •  Skills   •  User  behavior   •  Subscriber  Data   •  Sales  Data  (geo-­‐localized)   •  Group  discussions   •  ….  
  26. 26. Data Science IRRELEVANT NOISE METRICS RELEVANT DATA Petabyte Gigabyte Byte Terabyte1   2   3   4  
  27. 27. •  Opinion leaders (Katz 1955) •  Influentials (Merton 1968) •  Law of the Few (Gladwell 2000) A Clear Ask: Influencer
  28. 28. •  Opinion leaders (Katz 1955) •  Influentials (Merton 1968) •  Law of the Few (Gladwell 2000) Measure: Connectivity
  29. 29. Measure: Influence Source: Kevin Lewisa, Marco Gonzaleza and Jason Kaufman (2012): PNAS Vol 109, no 1 •  4 years •  1001 Students on Facebook •  Traditional Self-reported Data •  How did taste Spread
  30. 30. The Influencer Myth Influence is often overestimated: •  Reach •  Readiness •  Topic Dependence
  31. 31. Ask & Measure is critical Do you remember? Why did they fail?
  32. 32. It changes industries Google had the right Question.
  33. 33. Learn Benchmark   Predic,ons   Recommenda,ons  &   Filter  
  34. 34. Competitive Benchmark Source: ‘Ask Measure Learn’ by O’Reilly Media
  35. 35. LinkedIn Recommendations People You May Know Groups You May Like Ads in WhichYou May Be Interested Companies You May Want to Follow Pulse Similar Profiles
  36. 36. Prediction Photo  by  Ludie  Cochrane    
  37. 37. Also, here the MEASURE
  38. 38. Is critical
  39. 39. Academy   Industry   Professionals   Ac,ve  Movie  Goers   All  Movie  Goers   The measurement Was social media Mentions… And not the right predictions Predicting the OSCARs
  40. 40. Avoid! •  Cause is not Correlation •  Cause is not Effect •  Probability is NOT the Truth
  41. 41. Cause is not Correlation
  42. 42. Cause is not Effect
  43. 43. Probability is NOT the Truth
  44. 44. Today’s presentation & More Insights