Persuasion
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This document discusses how to get buy-in for data-driven ideas in an environment focused on reporting. It suggests combining two approaches - letting teams tweak recommendations from analytics and avoiding cherry-picking data to confirm prior beliefs. Root causes of inertia against new ideas are seen as outdated brand recognition and lack of credit for disruptors. Fear of missing out can motivate implementing recommendations without understanding if combined needs to belong, loss aversion, bargaining and social proof are triggered.
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Persuasion
- 1. Persuasion How to get buy-in in a world only interested in reporting Alban Gérôme @albangerome MeasureCamp Bratislava 24 March 2018
- 2. So, tell me…
- 3. So, tell me… where are your ideas really coming from?
- 4. Because they look as data-driven as la pasta di Mama to me!
- 7. It wasn’t me! All I do is reporting!
- 8. The cast
- 10. The cast Arnie Web Analyst Isabelle Published Author, Speaker and Entrepreneur
- 11. The cast Arnie Web Analyst Maggie Stakeholder Isabelle Published Author, Speaker and Entrepreneur
- 12. The cast Arnie Web Analyst Maggie Stakeholder Bill Chief Operating Officer Isabelle Published Author, Speaker and Entrepreneur
- 16. The network chart Influences Reports to Gives credit
- 17. The network chart Influences Reports to Gives credit Actionable insight
- 18. Maggie loves Isabelle’s ideas
- 19. Bill gives the credit to Maggie
- 20. Arnie finds actionable insight
- 21. Maggie ignores or rejects it
- 22. Maggie requests data extracts
- 23. If Arnie bypasses Maggie…
- 24. Bill thinks they are data-driven enough already
- 27. Edward Bernays’ uncle Sigmund Freud
- 28. Edward Bernays’ uncle Sigmund Freud Sigmund Freud’s nephew
- 29. Edward Bernays’ uncle Sigmund Freud Sigmund Freud’s nephew Edward Bernays
- 32. Edward Bernays’ achievements • Founder of Public Relations
- 33. Edward Bernays’ achievements • Founder of Public Relations • Got women to start smoking
- 34. Edward Bernays’ achievements • Founder of Public Relations • Got women to start smoking • Convinced millions of families to get eggs and bacon for breakfast
- 35. 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
- 37. Add one fresh egg
- 39. What’s in it for me?
- 40. What’s in it for me? A basket full of lemons?
- 43. Cherry-picking data • Captain Obvious says “People hate being proven wrong”
- 44. 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
- 45. 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
- 46. 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
- 48. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity
- 49. Common beliefs about persuasion • Robert Cialdini’s principles of influence: Reciprocity, Consistency, Social Proof, Authority, Liking, Scarcity – better suited for sales
- 50. 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?
- 51. 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?
- 52. 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?
- 53. 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
- 54. 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?
- 55. 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
- 56. 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
- 58. Root causes of inertia
- 59. Root causes of inertia • Data-driven is seen as a poor substitute for decades of brand- recognition
- 60. 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
- 61. 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
- 62. 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
- 63. 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?
- 65. “I’m gonna make him an offer he can’t refuse.” Don Vito Corleone
- 66. The Fear of Missing Out
- 67. The Fear of Missing Out • Abraham Maslow – The Need to Belong
- 68. The Fear of Missing Out • Abraham Maslow – The Need to Belong • Amos Tversky and Daniel Kahneman – Loss Aversion
- 69. The Fear of Missing Out • Abraham Maslow – The Need to Belong • Amos Tversky and Daniel Kahneman – Loss Aversion • Elizabeth Kübler-Ross – Bargaining Stage
- 70. 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
- 71. 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
- 72. The Fear of Missing Out Including implementing your recommendations
- 73. The Fear of Missing Out Including implementing your recommendations even without understanding web analytics
- 74. The Fear of Missing Out Including implementing your recommendations even without understanding web analytics and
- 75. The Fear of Missing Out Including implementing your recommendations even without understanding web analytics and even with a bad analytics implementation
- 76. The Fear of Missing Out does not exonerate you from
- 77. The Fear of Missing Out does not exonerate you from explaining what web analytics is for
- 78. The Fear of Missing Out does not exonerate you from explaining what web analytics is for and
- 79. The Fear of Missing Out does not exonerate you from explaining what web analytics is for and having the best implementation you can get
- 81. Let’s combine both approaches
- 82. A web analytics department telling the various teams what to do, when and how without letting them tweak anything Let’s combine both approaches
- 83. 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
- 84. 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
- 85. 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
- 86. 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
- 87. 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