Web Analytics Demystified - Competing On Web Analtytics

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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

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  • Tom’s central thesis …
  • Tom’s definition …
  • Tom’s definition …
  • Gary Loveman, Harrah’s CEO is said to repeat W. Edwards Deming’s quote, “In God We Trust; all others bring data!”
  • Reference Sisyphus …
  • The boulder has a tendency to roll down-hill …
  • So what can you do?
  • I would offer this small revision of Tom’s thesis, essentially “starting small” by working out the kinks in your online division first, then tackling capital “A” Analytics
  • The reality is that as more businesses shift online, the competitive advantage gained by Competing on Web Analytics will shift back into the larger business!
  • “Web analytics is hard” is a reminder to set your expectations, and those of your organization, appropriately. Too many people who profess to be “thought leaders” still try and obfuscate this simple truth, often with disastrous results
  • So what can you do?
  • I’d like to spend the rest of my time talking about three critical areas you all need to focus on to begin to Compete on Web Analytics
  • The eventual goal is to create an integrated view of visitor behavior that feeds a continual testing and improvement processThis is not a black box methodology: This is the process of UNDERSTANDING YOUR VISITORS and evolving that understanding over time
  • This slide is used with permission from Tom’s presentation on Competing on Analytics
  • Architecting your technology is not about buying the “next big thing” or spending money on technology because the UI is pretty …Architecting your technology is about determining which tools you need to actually have an positive impact on your online relationships
  • YouMUST have executive sponsorship to truly compete on analyticsSadly there does not appear to be any way around this observationWork to make the boss successful to reinforce the idea of competing on analytics
  • There are analytics amateurs throughout your organization but they’re too busy to learn the detailsAmateurs essentially need to be “spoon fed” information and insightsSome will self-identify as “web analytics geeks” --- work with them whenever possible!
  • Dedicated analytical professionals are the critical “must have” resourceYou can outsource if need be, but better are in-house 1.0 FTE analytical minds
  • I first wrote about the need for dedicated staff in 2005, back when “web analytics was easy” and all you needed was software … big hug for me!
  • The critical question on staffing has long been “how much should you spend?”Many have offered answers --- in 2004 I suggested “at least one full time resource”Based on my recent research the answer may be surprising but it provides clear guidance …
  • Prepare to spend at least another 50% (?!?) on partial FTE for your analytics amateurs and executive’s time!
  • What I have long recommended is the “hub and spoke” model for web analytics, a centralized/decentralized approachRegarding spending: depending on your organization you may spend another 50% in the spokes on partial FTE
  • Always keep in mind that ANALYTICS IS A TEAM EFFORTTeams are not transient groups of people > teams develop cohesion over time and learn to work togetherIf you’re in management you need to recognize this > ANALYTICS IS NOT ABOUT TECHNOLOGY IT IS ABOUT PEOPLE!
  • That playbook and a clear plan will help you move up the web analytics maturity spectrum …
  • There is a lot of hand-waving lately about “new” models for how analytics is done …But driving your company up the Hierarchy of Analytical Needs is, and always has been, the path to successAs an analyst, PUSH to provide LESS and MORE VALUABLE outputAs a manager, ASK for insights and recommendations, not data and information
  • Still with me?
  • Backcountry.comfrom Heber City, Utah~$3M in revenues and ~10 employees in 2001>$200M in revenues and 800 employees in 2008
  • Mention Coremetrics and Engagement white papersMention Twitalyzer
  • Web Analytics Demystified - Competing On Web Analtytics

    1. 1. Web Analytics Demystified COMPETING ON WEB ANALYTICS
    2. 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. 3. Introduction … 3
    4. 4. @ericTpeterson 4
    5. 5. @ericpeterson 5
    6. 6. Web Analytics Demystified COMPETING ON WEB ANALYTICS
    7. 7. 7
    8. 8. 8
    9. 9. “Businesses can create sustainable and strategic competitive advantages by investing in analytics.” 9
    10. 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. 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. 12
    13. 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. 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. 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. 16
    17. 17. 17
    18. 18. Consider Starting Small … 18
    19. 19. 19
    20. 20. ^ 20
    21. 21. “Businesses can create sustainable and strategic competitive advantages by investing in analytics.” 21
    22. 22. “Businesses can create sustainable and strategic competitive advantages online by investing in web analytics.” 22
    23. 23. “Businesses can create sustainable and strategic competitive advantages online by investing in web analytics.” 23
    24. 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. 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. 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. 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. 28
    29. 29. There is a Solution! 29
    30. 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. 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. 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. 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. 34. What Are the Right Systems? Visitor Scope of Insight Session Quantitative Type of Data Qualitative 34
    35. 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. 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. 37. The Competitor’s Toolbox • Simple presentation tools • Powerful data manipulation environment • Rich analytical modeling capabilities • Robust ETL support • Flexible data repositories 37
    38. 38. The Competitor’s Toolbox • Simple presentation tools • Powerful data manipulation environment • Rich analytical modeling capabilities • Robust ETL support • Flexible data repositories 38
    39. 39. The Competitor’s Toolbox • Simple presentation tools • Powerful data manipulation environment • Rich analytical modeling capabilities • Robust ETL support • Flexible data repositories 39
    40. 40. The Competitor’s Toolbox • Simple presentation tools • Powerful data manipulation environment • Rich analytical modeling capabilities • Robust ETL support • Flexible data repositories 40
    41. 41. The Competitor’s Toolbox • Simple presentation tools • Powerful data manipulation environment • Rich analytical modeling capabilities • Robust ETL support • Flexible data repositories 41
    42. 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. 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. 44. Architect Your Technology Visitor Scope of Insight Session Quantitative Type of Data Qualitative 44
    45. 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. 46. Manage Your Talent • People are critical to analytics • But analysts alone cannot get the job done • Analytics is a team effort 46
    47. 47. Analytical Executives 47
    48. 48. Analytics Amateurs 48
    49. 49. Analytical Professionals 49
    50. 50. The Description Has Not Changed 50
    51. 51. How Much Should You Spend? 51
    52. 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. 53. The Analytical Organization 53
    54. 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. 55. Manage Your Analytical Talent 55
    56. 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. 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. 58. The Hierarchy of Analytical Needs Recommendations Insights Information Data 58
    59. 59. What Most Companies Get Today … Recommendations Insights Information Data 59
    60. 60. What Companies Really Need Recommendations Insights Information Data 60
    61. 61. What Management Really Wants! Recommendations Insights Information Data 61
    62. 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. 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. 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. 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. 66. Focus Your Analytical Efforts Recommendations Insights Information Data 66
    67. 67. 67
    68. 68. At the End of the Day it Takes … 68
    69. 69. People … 69
    70. 70. Process … 70
    71. 71. … and Technology 71
    72. 72. … in Roughly Equal Measures 72
    73. 73. … to Become an “Analytics Superhero” 73
    74. 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. 75
    76. 76. “If it’s worth doing, it’s worth doing analytically.” 76
    77. 77. “If it’s worth doing, it’s worth doing web analytically.” 77
    78. 78. Want to Know More? 78
    79. 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 79

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