We’re drowning in data in the eCommerce world. We can and do measure everything. But how do we get the most out of those numbers? Those mountains of data can be full of gold if we mine them correctly, or they can just be big piles of useless dirt. All too often, we misuse the valuable data we have and end up flailing away. Many of the reasons we aren’t happy with the results of the analyses come down to fundamental disconnects in human relations between all parties involved. Groups of people with disparate backgrounds, training and experiences gather in a room to “review the numbers.” We each bring our own sets of assumptions, biases and expectations, and we generally fail to establish common sets of understanding before digging in.

1. #monetatesummit
11 Ways
HumansKill
GoodAnalysis
KevinErtell, SurLaTable

8. FOCUSED

9. FOCUSED
ACTIONABLE

10. FOCUSED
ACTIONABLE
MANAGEABLE

11. FOCUSED
ACTIONABLE
MANAGEABLE
ENLIGHTENING

15. FOCUSED

19. ACTIONABLE

22. MANAGEABLE

25. ENLIGHTENING

28. FOCUSED
ACTIONABLE
MANAGEABLE
ENLIGHTENING

32. 11 Ways Humans Kill
Good Analysis

33. 1. We hire reporters not analysts

35. Logical
Sequential
Rational
Objective

40. 2. We turn analysts into reporters

44. Why is conversion
down on Google
paid search?

45. Why is conversion
down on Google
paid search?
What’s'the'op+mal'
marke+ng'mix'to'use'
to'launch'Brand'X?

46. Why is conversion
down on Google
paid search?
What’s'the'op+mal'
marke+ng'mix'to'use'
to'launch'Brand'X?
Why'are'return'
rates'growing?

47. 3. We expect the data to be perfect and the
analysis to be flawless

54. A man with one watch
knows what time it is;
a man with two watches
is never quite sure.

56. 4. We fail to define objectives
and state our assumptions

59. 5. We want numbers for number’s sake

61. KPIs
Suppor+ng'Metrics
Forensic'Metrics

63. Supporting metrics

64. KPIs
Supporting metrics

66. 6. We insist on simplicity

67. How likely are you to recommend this business?

68. • Large margins of error
• Low precision
• Low detection of movement
• Interpretation problems
• Not very predictive
How likely are you to recommend this business?

70. All'we'did'was'quan+fy'this'
common'sense'in'a'way'that'
made'sense'to'business'leaders—
the'target'audience'for'my'book.'
These'prac+cal'leaders'have'liJle'
interest'in'advanced'sta+s+cal'
methods.'

71. All'we'did'was'quan+fy'this'
common'sense'in'a'way'that'
made'sense'to'business'leaders—
the'target'audience'for'my'book.'
These'prac+cal'leaders'have'liJle'
interest'in'advanced'sta+s+cal'
methods.'
These practical leaders have
little interest in advanced
statistical methods

82. correlations

86. 7. We just want the number

89. “Plans based on average assumptions
are wrong on average.” -Sam Savage

93. 8. We aren’t multilingual in the
languages of business & statistics

94. Standard deviations

95. Standard deviations
Variances

111. 9. We expect answers immediately

113. You’re approaching a Coast
Guard security zone. … If you
don’t stop your vessel, you will
be fired upon. Stop your vessel
immediately.

114. You’re approaching a Coast
Guard security zone. … If you
don’t stop your vessel, you will
be fired upon. Stop your vessel
immediately.
bang,'bang,'bang,'bang
Bang! Bang!
Bang! Bang!

116. regression to the mean

117. 10. We ignore our guts

118. Prefrontal cortex

120. 11. We blow the presentation

144. Questions?
Kevin'Ertell'
@kevinertell
Kevin.Ertell@SurLaTable.com