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
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
47.
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
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?
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
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