Presentation from Eric T. Peterson of Web Analytics Demystified at ForeSee Results and WebVisions conferences in May 2009. Learn more at http://www.webanalyticsdemystified.com

1. Web Analytics Demystified
COMPETING ON WEB
ANALYTICS

2. Introduction
• Founder, Web Analytics Demystified, Inc.
• Author of three books:
– Web Analytics Demystified
– Web Site Measurement Hacks
– Big Book of Key Performance Indicators
• Focused on measurement process, organizational
structure, and overall digital analytics strategy
2

3. Introduction …
3

4. @ericTpeterson
4

5. @ericpeterson
5

6. Web Analytics Demystified
COMPETING ON WEB
ANALYTICS

7. 7

8. 8

9. “Businesses can create
sustainable and strategic
competitive advantages by
investing in analytics.”
9

10. What is Analytics?
“By analytics we mean the extensive use of
data, statistical and quantitative
analysis, explanatory and predictive models, and
fact-based management to drive decisions and
actions…analytics are part of what has come to be
called business intelligence: a set of technologies
and processes that use data to understand and
analyze business performance.”
10

11. What is Analytics?
“By analytics we mean the extensive use of
data, statistical and quantitative
analysis, explanatory and predictive models, and
fact-based management to drive decisions and
actions…analytics are part of what has come to be
called business intelligence: a set of technologies
and processes that use data to understand and
analyze business performance.”
11

12. 12

13. Key Ideas
• Depends on operational interpretation and
visualization, not data collection and reporting
• Managed globally for all processes and
functions, not departmentally or in silos
• Requires continual monitoring and response
based on observed changes, not episodic changes
and re-engineering
• Powered by people and process, not just
technology
13

14. Key Ideas
• Depends on operational interpretation and
visualization, not data collection and reporting
• Managed globally for all processes and
functions, not departmentally or in silos
• Requires continual monitoring and response
based on observed changes, not episodic changes
and re-engineering
• Powered by people and process, not just
technology
14

15. The Big Problem?
• It is very easy to
profess a great love
for data …
• … and still fail to
use that data to
inform action!
15

16. 16

17. 17

18. Consider Starting Small …
18

19. 19

20. ^
20

21. “Businesses can create
sustainable and strategic
competitive advantages by
investing in analytics.”
21

22. “Businesses can create
sustainable and strategic
competitive advantages
online by investing in web
analytics.”
22

23. “Businesses can create
sustainable and strategic
competitive advantages
online by investing in web
analytics.”
23

24. The Same Key Ideas
• Depends on operational interpretation and
visualization, not data collection and reporting
• Managed globally for all processes and
functions, not departmentally or in silos
• Requires continual monitoring and response
based on observed changes, not episodic changes
and re-engineering
• Powered by people and process, not just
technology
24

25. The Big Problem
Overly complex and expensive segmentation tools Expensive software
Web analytics and audience research solutions give different numbers
No real web analytics community association Data integration nightmares
No standard definitions of engagement and influence
No information about visitor and customer intent Crummy data export tools
No standard qualitative inputs Poorly defined key performance indicators
Poorly defined processes for using web analytics Lousy vendor documentation
Smarmy web analytics sales people Not enough great books on web analytics
Crappy implementations Too few relevant case studies
No data about statistical relevance Software is too complex
Too many reports, not enough information Expensive consultants
No help from Information Technology (I.T.)
No real tools for modeling and predictive analytics Few formal web analytics training courses
No standard definitions in the industry
Too analytics HiPPOs
many
Contradictory explanations of what the data means
Too few people who understand web
Cookie deletion, cookie blocking, and cookie-less mobile browsers
25

26. The Real Problem
Overly complex and expensive segmentation tools Expensive software
Web analytics and audience research solutions give different numbers
No real web analytics community association Data integration nightmares
No standard definitions of engagement and influence
No information about visitor and customer intent Crummy data export tools
No standard qualitative inputs Poorly defined key performance indicators
Poorly defined processes for using web analytics Lousy vendor documentation
WEB ANALYTICS IS HARD
Smarmy web analytics sales people Not enough great books on web analytics
Crappy implementations Too few relevant case studies
No data about statistical relevance Software is too complex
Too many reports, not enough information Expensive consultants
No help from Information Technology (I.T.)
No real tools for modeling and predictive analytics Few formal web analytics training courses
No standard definitions in the industry
Too analytics HiPPOs
many
Contradictory explanations of what the data means
Too few people who understand web
Cookie deletion, cookie blocking, and cookie-less mobile browsers
26

27. The Real Problem
Overly complex and expensive segmentation tools Expensive software
Web analytics and audience research solutions give different numbers
No real web analytics community association Data integration nightmares
No standard definitions of engagement and influence
No information about visitor and customer intent Crummy data export tools
No standard qualitative inputs Poorly defined key performance indicators
Poorly defined processes for using web analytics Lousy vendor documentation
WEB ANALYTICS IS HARD
Smarmy web analytics sales people Not enough great books on web analytics
Crappy implementations Too few relevant case studies
No data about statistical relevance Software is too complex
Too many reports, not enough information Expensive consultants
No help from Information Technology (I.T.)
No real tools for modeling and predictive analytics Few formal web analytics training courses
No standard definitions in the industry
Too analytics HiPPOs
many
Contradictory explanations of what the data means
Too few people who understand web
Cookie deletion, cookie blocking, and cookie-less mobile browsers
27

28. 28

29. There is a Solution!
29

30. How to Compete on Web
Analytics
1. Architect Your Web Analytics Technology
2. Manage Your Web Analytics Talent
3. Focus Your Analytical Efforts
30

31. How to Compete on Web
Analytics
1. Architect Your Web Analytics Technology
2. Manage Your Web Analytics Talent
3. Focus Your Analytical Efforts
31

32. Architect Your Technology
• Web analytics absolutely depends on technology
• You can put too much emphasis on software
• Key challenges:
– Right data
– Right systems
– Right output
32

33. What is the Right Data?
• The “right” data
Visitor
comes from
multiple systems
Scope of Insight
• Integration of
these systems is
the new
opportunity
Session
Quantitative Type of Data Qualitative
33

34. What Are the Right Systems?
Visitor
Scope of Insight
Session
Quantitative Type of Data Qualitative
34

35. What is the Right Output?
Analytics
Decision Optimization What’s the best that can happen?
Competitive Advantage
Predictive Analytics What will happen next?
Forecasting What if these trends continue?
Statistical models Why is this happening?
Alerts What actions are needed?
Query/drill down Where exactly is the problem?
Ad hoc reports How many, how often, where?
Standard reports What happened?
Reporting
Degree of Intelligence
35

36. Unfortunately …
Analytics
Decision Optimization What’s the best that can happen?
Competitive Advantage
Predictive Analytics What will happen next?
Forecasting What if these trends continue?
Statistical models Why is this happening?
Alerts What actions are needed?
Query/drill down Where exactly is the problem?
Ad hoc reports How many, how often, where?
Standard reports What happened?
Reporting
Degree of Intelligence
36

37. The Competitor’s Toolbox
• Simple presentation tools
• Powerful data manipulation
environment
• Rich analytical modeling
capabilities
• Robust ETL support
• Flexible data repositories
37

38. The Competitor’s Toolbox
• Simple presentation tools
• Powerful data manipulation
environment
• Rich analytical modeling
capabilities
• Robust ETL support
• Flexible data repositories
38

39. The Competitor’s Toolbox
• Simple presentation tools
• Powerful data manipulation
environment
• Rich analytical modeling
capabilities
• Robust ETL support
• Flexible data repositories
39

40. The Competitor’s Toolbox
• Simple presentation tools
• Powerful data manipulation
environment
• Rich analytical modeling
capabilities
• Robust ETL support
• Flexible data repositories
40

41. The Competitor’s Toolbox
• Simple presentation tools
• Powerful data manipulation
environment
• Rich analytical modeling
capabilities
• Robust ETL support
• Flexible data repositories
41

42. How Are You Doing with
Technology?
• Four signs of the “right” technology:
1. Nobody says “if only we had Solution X”
2. Nobody questions the money you’re spending
3. Nobody uses Excel because they have to
4. Nobody is forced to whine on Twitter for help!
42

43. How Are You Doing with
Technology?
• Four signs of the “right” technology:
1. Nobody says “if only we had Solution X”
2. Nobody questions the money you’re spending
3. Nobody uses Excel because they have to
4. Nobody is forced to whine on Twitter for help!
43

44. Architect Your Technology
Visitor
Scope of Insight
Session
Quantitative Type of Data Qualitative
44

45. How to Compete on Web
Analytics
1. Architect Your Web Analytics Technology
2. Manage Your Web Analytics Talent
3. Focus Your Analytical Efforts
45

46. Manage Your Talent
• People are critical to analytics
• But analysts alone cannot get the job done
• Analytics is a team effort
46

47. Analytical Executives
47

48. Analytics Amateurs
48

49. Analytical Professionals
49

50. The Description Has Not
Changed
50

51. How Much Should You
Spend?
51

52. The 50/50 Rule for Analytics
Investment
• For every dollar you invest in technology
• Spend one dollar for dedicated resources
– Using free tools? Estimate costs
– Hiring freeze? Consider consultants
– No budget? You get what you pay for
• This is how the analytical
competitors are getting it done
52

53. The Analytical Organization
53

54. How Are You Doing With
Staffing?
• Five signs of the “right” staffing model:
1. You have a senior person who “owns” analytics
2. You have dedicated resources for web analytics
3. You know who your “analytics amateurs” are
4. Your analytics hub supports the whole company
5. Your analytics hub produces insights and
recommendations, not just reports
54

55. Manage Your Analytical Talent
55

56. How to Compete on Web
Analytics
1. Architect Your Web Analytics Technology
2. Manage Your Web Analytics Talent
3. Focus Your Analytical Efforts
56

57. Focus Your Analytical Efforts
• Fewer than one-in-five companies have a
company-wide strategy for web analytics *
• Lacking strategy, chaos reigns
• Developing a strategy requires understanding the
Hierarchy of Analytical Needs
* Econsultancy 2008 57

58. The Hierarchy of Analytical
Needs
Recommendations
Insights
Information
Data
58

59. What Most Companies Get
Today …
Recommendations
Insights
Information
Data
59

60. What Companies Really Need
Recommendations
Insights
Information
Data
60

61. What Management Really
Wants!
Recommendations
Insights
Information
Data
61

62. Recommendations Require
Maturity
Stage 0 Stage 1 Stage 2 Stage 3 Stage 4
Relative Number of
Staffing Chasm
Companies
Investment
Chasm Process Chasm
Maturity of Analytics Use Source: JupiterResearch (8/05)
62

63. Cross the “Process” Chasm!
• To provide insights and
recommendations you
must first define
analytics processes
• There is simply no way
around this
63

64. Focus on Internal Analytics
Process
• Ask yourself?
– Does the business understand what you do?
– Do you present data or generate recommendations?
– Do you have clearly defined workflow?
– Do you have a governance model?
– Do you know where our internal
processes break-down?
64

65. How to “Kick-Butt” with
Analytics
• Six hallmarks of “Analytical Champions”:
1. Clearly defined analytics governance model
2. Appropriate technology investment
3. Appropriate resource allocation
4. Intense process-awareness
5. Ability to generate recommendations
6. Ability to measure impact of
recommended actions taken
65

66. Focus Your Analytical Efforts
Recommendations
Insights
Information
Data
66

67. 67

68. At the End of the Day it Takes
…
68

69. People …
69

70. Process …
70

71. … and Technology
71

72. … in Roughly Equal Measures
72

73. … to Become an “Analytics
Superhero”
73

74. Because Success Kicks Butt!
• “Our success is split evenly between building
authentic brands and using analytics to leverage
and innovate pull demand marketing online.”
– Dustin Robertson, CMO
Backcountry.com
74

75. 75

76. “If it’s worth doing, it’s worth
doing analytically.”
76

77. “If it’s worth doing, it’s worth
doing web analytically.”
77

78. Want to Know More?
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79. Eric T. Peterson
eric.peterson@webanalyticsdemystified.com
www.webanalyticsdemystified.com
Twitter: @erictpeterson
Join us at the X Change September 9, 10, 11 in San Francisco
www.xchangeconference.com
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