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Persuasion

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How to get buy-in for your web analytics actionable insight in a world only interested in reporting

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Persuasion

  1. 1. Persuasion How to get buy-in in a world only interested in reporting Alban Gérôme @albangerome MeasureCamp Bratislava 24 March 2018
  2. 2. So, tell me…
  3. 3. So, tell me… where are your ideas really coming from?
  4. 4. Because they look as data-driven as la pasta di Mama to me!
  5. 5. It wasn’t me!
  6. 6. It wasn’t me! All I do is reporting!
  7. 7. The cast
  8. 8. The cast Arnie Web Analyst
  9. 9. The cast Arnie Web Analyst Isabelle Published Author, Speaker and Entrepreneur
  10. 10. The cast Arnie Web Analyst Maggie Stakeholder Isabelle Published Author, Speaker and Entrepreneur
  11. 11. The cast Arnie Web Analyst Maggie Stakeholder Bill Chief Operating Officer Isabelle Published Author, Speaker and Entrepreneur
  12. 12. The network chart
  13. 13. The network chart Influences
  14. 14. The network chart Influences Reports to
  15. 15. The network chart Influences Reports to Gives credit
  16. 16. The network chart Influences Reports to Gives credit Actionable insight
  17. 17. Maggie loves Isabelle’s ideas
  18. 18. Bill gives the credit to Maggie
  19. 19. Arnie finds actionable insight
  20. 20. Maggie ignores or rejects it
  21. 21. Maggie requests data extracts
  22. 22. If Arnie bypasses Maggie…
  23. 23. Bill thinks they are data-driven enough already
  24. 24. Edward Bernays’ uncle
  25. 25. Edward Bernays’ uncle Sigmund Freud
  26. 26. Edward Bernays’ uncle Sigmund Freud Sigmund Freud’s nephew
  27. 27. Edward Bernays’ uncle Sigmund Freud Sigmund Freud’s nephew Edward Bernays
  28. 28. Edward Bernays’ achievements
  29. 29. Edward Bernays’ achievements • Founder of Public Relations
  30. 30. Edward Bernays’ achievements • Founder of Public Relations • Got women to start smoking
  31. 31. Edward Bernays’ achievements • Founder of Public Relations • Got women to start smoking • Convinced millions of families to get eggs and bacon for breakfast
  32. 32. Edward Bernays’ achievements • Founder of Public Relations • Got women to start smoking • Convinced millions of families to get eggs and bacon for breakfast • Got millions of housewives to start using cake mixes
  33. 33. Add one fresh egg
  34. 34. What’s in it for me?
  35. 35. What’s in it for me? A basket full of lemons?
  36. 36. Cherry-picking data
  37. 37. Cherry-picking data • Captain Obvious says “People hate being proven wrong”
  38. 38. Cherry-picking data • Captain Obvious says “People hate being proven wrong” • Confirmation bias: data confirming prior beliefs is correct, contradictory data is wrong so it gets ignored
  39. 39. Cherry-picking data • Captain Obvious says “People hate being proven wrong” • Confirmation bias: data confirming prior beliefs is correct, contradictory data is wrong so it gets ignored • Belief persistence: faced with facts contradicting one’s belief, one tends not to change their beliefs and come out with reinforced beliefs
  40. 40. Cherry-picking data • Captain Obvious says “People hate being proven wrong” • Confirmation bias: data confirming prior beliefs is correct, contradictory data is wrong so it gets ignored • Belief persistence: faced with facts contradicting one’s belief, one tends not to change their beliefs and come out with reinforced beliefs • Cognitive dissonance: the gap between facts and beliefs causes discomfort, one will do anything to reduce that gap
  41. 41. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity
  42. 42. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales
  43. 43. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales • Daniel Kahneman’s System 1: Do you own cryptocurrencies?
  44. 44. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales • Daniel Kahneman’s System 1: Do you own cryptocurrencies? Do you understand how they work?
  45. 45. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales • Daniel Kahneman’s System 1: Do you own cryptocurrencies? Do you understand how they work? And you bought them anyway?
  46. 46. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales • Daniel Kahneman’s System 1: Do you own cryptocurrencies? Do you understand how they work? And you bought them anyway? • Hans Rosling’s data visualisation demo: Great at condensing a large amount of data and bringing people up to speed
  47. 47. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales • Daniel Kahneman’s System 1: Do you own cryptocurrencies? Do you understand how they work? And you bought them anyway? • Hans Rosling’s data visualisation demo: Great at condensing a large amount of data and bringing people up to speed but getting buy-in?
  48. 48. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales • Daniel Kahneman’s System 1: Do you own cryptocurrencies? Do you understand how they work? And you bought them anyway? • Hans Rosling’s data visualisation demo: Great at condensing a large amount of data and bringing people up to speed but getting buy-in? • Nancy Duarte’s storytelling principles: Helps bridging the gap between a qualitative and quantitative view
  49. 49. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales • Daniel Kahneman’s System 1: Do you own cryptocurrencies? Do you understand how they work? And you bought them anyway? • Hans Rosling’s data visualisation demo: Great at condensing a large amount of data and bringing people up to speed but getting buy-in? • Nancy Duarte’s storytelling principles: Helps bridging the gap between a qualitative and quantitative view. Good for evangelising
  50. 50. Root causes of inertia
  51. 51. Root causes of inertia • Data-driven is seen as a poor substitute for decades of brand- recognition
  52. 52. Root causes of inertia • Data-driven is seen as a poor substitute for decades of brand- recognition • Disrupted household brands became easy preys for disruption due to mismanagement
  53. 53. Root causes of inertia • Data-driven is seen as a poor substitute for decades of brand- recognition • Disrupted household brands became easy preys for disruption due to mismanagement, no credit for the disruptors
  54. 54. Root causes of inertia • Data-driven is seen as a poor substitute for decades of brand- recognition • Disrupted household brands became easy preys for disruption due to mismanagement, no credit for the disruptors • The C-suite is probably more complacent about their managers seemingly data-driven efforts than fooled by them
  55. 55. Root causes of inertia • Data-driven is seen as a poor substitute for decades of brand- recognition • Disrupted household brands became easy preys for disruption due to mismanagement, no credit for the disruptors • The C-suite is probably more complacent about their managers seemingly data-driven efforts than fooled by them What could possibly go wrong when a company’s perennial competitor suddenly combines brand-recognition and a data-driven approach?
  56. 56. “I’m gonna make him an offer he can’t refuse.” Don Vito Corleone
  57. 57. The Fear of Missing Out
  58. 58. The Fear of Missing Out • Abraham Maslow – The Need to Belong
  59. 59. The Fear of Missing Out • Abraham Maslow – The Need to Belong • Amos Tversky and Daniel Kahneman – Loss Aversion
  60. 60. The Fear of Missing Out • Abraham Maslow – The Need to Belong • Amos Tversky and Daniel Kahneman – Loss Aversion • Elizabeth Kübler-Ross – Bargaining Stage
  61. 61. The Fear of Missing Out • Abraham Maslow – The Need to Belong • Amos Tversky and Daniel Kahneman – Loss Aversion • Elizabeth Kübler-Ross – Bargaining Stage • Robert Cialdini – Social Proof
  62. 62. The Fear of Missing Out • Abraham Maslow – The Need to Belong • Amos Tversky and Daniel Kahneman – Loss Aversion • Elizabeth Kübler-Ross – Bargaining Stage • Robert Cialdini – Social Proof When someone feels these 4 emotions simultaneously, they will take any action to continue belonging
  63. 63. The Fear of Missing Out Including implementing your recommendations
  64. 64. The Fear of Missing Out Including implementing your recommendations even without understanding web analytics
  65. 65. The Fear of Missing Out Including implementing your recommendations even without understanding web analytics and
  66. 66. The Fear of Missing Out Including implementing your recommendations even without understanding web analytics and even with a bad analytics implementation
  67. 67. The Fear of Missing Out does not exonerate you from
  68. 68. The Fear of Missing Out does not exonerate you from explaining what web analytics is for
  69. 69. The Fear of Missing Out does not exonerate you from explaining what web analytics is for and
  70. 70. The Fear of Missing Out does not exonerate you from explaining what web analytics is for and having the best implementation you can get
  71. 71. Let’s combine both approaches
  72. 72. A web analytics department telling the various teams what to do, when and how without letting them tweak anything Let’s combine both approaches
  73. 73. A web analytics department telling the various teams what to do, when and how without letting them tweak anything is no different than conservatorship Let’s combine both approaches
  74. 74. A web analytics department telling the various teams what to do, when and how without letting them tweak anything is no different than conservatorship Bombarding the web analytics department with large and frequent data extract requests only to cherry-pick data that confirms prior beliefs Let’s combine both approaches
  75. 75. A web analytics department telling the various teams what to do, when and how without letting them tweak anything is no different than conservatorship Bombarding the web analytics department with large and frequent data extract requests only to cherry-pick data that confirms prior beliefs is not being data- driven but data-justified Let’s combine both approaches
  76. 76. A web analytics department telling the various teams what to do, when and how without letting them tweak anything is no different than conservatorship Bombarding the web analytics department with large and frequent data extract requests only to cherry-pick data that confirms prior beliefs is not being data- driven but data-justified and won’t cut it much longer Let’s combine both approaches
  77. 77. A web analytics department telling the various teams what to do, when and how without letting them tweak anything is no different than conservatorship Bombarding the web analytics department with large and frequent data extract requests only to cherry-pick data that confirms prior beliefs is not being data- driven but data-justified and won’t cut it much longer We need to combine the qualitative domain knowledge of the incumbent teams with the quantitative methods of the analytics department Let’s combine both approaches
  78. 78. Thank you! http://www.albangerome.com @albangerome

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