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

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!

6. It wasn’t me!

7. It wasn’t me! All I do is reporting!

8. The cast

9. The cast
Arnie
Web Analyst

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

13. The network chart

14. The network chart
Influences

15. The network chart
Influences
Reports to

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

26. Edward Bernays’ uncle

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

31. Edward Bernays’ achievements

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?

42. Cherry-picking data

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

90. Thank you!
http://www.albangerome.com
@albangerome