A talk on how Web Analytics and User Experience research data can provide a lot of insights to derive an actionable strategy. Methodology - Reality - Suggestions - Challenges.

2. SHIVAM
Web Strategy Advisor
Founder Arbunize
Me @ Shivam.Dhawan.biz
Talking about Web Strategy
with WA and UX DATA

3. OUR ONLINE
OBJECTIVES

4. Build Experience

5. Manage User Behavior

6. Maintain Outcomes

7. MaintainManageMake
Building
Experience
Understanding
Behavior
Leading
Outcomes
User research
/ UX data
collected for
building a
desired user
experience
Analytics /
user behavior
data
collected for
building a
desired user
experience
Data that
measures
outcomes /
results in
form of
revenues,
leads, etc.

8. Click Stream
Key Metrics
Segmentation
Search data
“INTENT OF
CUSTOMER”
Design Research
A/B testing
Usability Customer
Satisfaction
“VOICE OF
CUSTOMER”
Orders/Leads
Revenues
Conversion Rates
Problem Resolution
“RESULTS”

9. Click Stream
Key Metrics
Segmentation
Search data
“INTENT OF
CUSTOMER”
Design Research
A/B testing
Usability Customer
Satisfaction
“VOICE OF
CUSTOMER”
Orders/Leads
Revenues
Conversion Rates
Problem Resolution
“RESULTS”
Behavior

10. Click Stream
Key Metrics
Segmentation
Search data
“INTENT OF
CUSTOMER”
Design Research
A/B testing
Usability Customer
Satisfaction
“VOICE OF
CUSTOMER”
Orders/Leads
Revenues
Conversion Rates
Problem Resolution
“RESULTS”
Behavior

11. Click Stream
Key Metrics
Segmentation
Search data
“INTENT OF
CUSTOMER”
Design Research
A/B testing
Usability Customer
Satisfaction
“VOICE OF
CUSTOMER”
Orders/Leads
Revenues
Conversion Rates
Problem Resolution
“RESULTS”
Behavior

12. Click Stream
Key Metrics
Segmentation
Search data
“INTENT OF
CUSTOMER”
Design Research
A/B testing
Usability Customer
Satisfaction
“VOICE OF
CUSTOMER”
Orders/Leads
Revenues
Conversion Rates
Problem Resolution
“RESULTS”
Behavior

13. User Experience Data
User Behavior Data
User Driven Outcomes

14. DATA IS MYSTERIOUS

15. DATA:
Light blue
White
Black
Sleeveless
Neck bow

16. DATA:
Light blue
White
Black
Sleeveless
Neck bow

22. DATA
DRIVES
DECISIONS

23. DATA
DRIVES
DECISIONS

24. DATA
DRIVES
DECISIONS

25. DATA
DECISIONS

26. DATA
DECISIONS

27. DATA
DECISIONS

28. DATA
DECISIONS

29. DATA
DECISIONS

30. DATA
DECISIONS

31. DATA
DECISIONS

33. APPROACH
DATASOURCE
Adapted from work of Christian Rohrer 2008
X Multivariate (live
A/B) testing
X Eye Tracking
X Usability
Lab Studies
X Customer
email Feedback
X Intercept
Surveys
X Data Analysis /
Mining
X Live UX
assessments
X Participatory
Design
X Focus Groups
X Card
sorting
X Desirability
studies
X social
Listening
solutions
X Message
board mining
X Email
Surveys

34. THAT’S A LOT OF DATA

35. WA: WHAT happens and WHEN

36. WA: WHAT happens and WHEN
UX: HOW it happens and WHY

37. It is not much use to know
WHAT is happening if you don’t
know WHY

38. It is not much use to know
WHAT is happening if you don’t
know WHY
You can’t know WHY things are
happening if you don’t know
WHAT is happening

39. WA: Top Down Analysis

40. WA: Top Down Analysis

41. WA: Top Down Analysis
UX: Bottom Up Analysis

42. WA: Top Down Analysis
UX: Bottom Up Analysis

43. What’s in the Middle?

44. What’s in the Middle?

45. DECISIONS
What’s in the Middle?

46. UX
WA

47. Till now,
> How user experience and user behavior
work
> WA and UX approach and methodologies
towards DATA
> What informs our decisions

48. IT
WAS
SIMPLE

49. IT
WAS
SIMPLE

51. What’s in the Middle?

52. REPORTS
What’s in the Middle?

54. 

55. 1. ‘WHY questions’ are target driven, not Analysis driven
2. ‘WHY questions’ are driven by Business, not by Analysts
3. Standard reports become STANDARDS
4. Numbers become VERY important for WA
5. Numbers become VERY uncomfortable for UX
6. Ownership of Strategy comes to the FAT GUY, who is neither
WA nor UX

56. 

58. ‘WHY questions’
are target driven,
not Analysis driven

60. Target
25K

61. Target
25K

62. Target
25K
X
XOutcomes

63. ‘WHY questions’
are target driven,
not Analysis driven
> Numbers may mean different at different times,
season, trend, campaign
> External factors contribute, other metric influence,
PV-Visits-Visitors, Bounce-Time

65. ‘WHY questions’
are driven by business,
not by Analysts

67. What Business?

68. ‘WHY questions’
are driven by business,
not by Analysts
> Analysts should get acquainted with business
objectives
> Joint ownership of ‘Why questions’

70. Standard reports
become
STANDARDS

74. ‘Standard’ is the Starting point

75. Standard reports
become
STANDARDS
> Drill Down
> Customize your own
> Question, Question, Question

77. Numbers become
VERY important
for WA

79. Tell Stories

80. Get Visual

81. Show Data

82. Numbers become
VERY important
for WA
> Go deep > Understand > Explain
> Tell Stories > Get Visual

84. Numbers become
VERY uncomfortable
for UX

85. Tools Help

86. Get Access

87. Understand

88. Numbers become
VERY uncomfortable
for UX
> Get Comfortable > Demand adhocs
> Ask Questions > Get Answers
> Play with tools

90. Ownership of Strategy
comes to the FAT GUY,
who is neither WA nor UX

92. Decision Support vs Decision Making

93. Ownership of Strategy
comes to the FAT GUY,
who is neither WA nor UX
> Understand the value of intuition and mistakes.
Allow some.
> Talk with UX / WA about project goals
> Hire a Web Strategy Advisor
Ps - Sorry, for calling you FAT

94. Play . Perform . Own

95. Parting Challenges
How do we…
Bridge cultural gaps
Design and Manage integrated teams
Develop a unified methodology
Generate awareness
Improve on tools, terminologies and standards

96. Takeaway
WA: Stories and emotions can make stronger cases than
Data and for Data
UX: Most things can be quantified. Speak to the numbers
and listen to them
You: Hire a Strategist who understands both

97. THANKS
Follow me @ShvmDhwn
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