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SearchLove London | Annie Cushing, 'Are Your Google Analytic's Reports Pretty Little Liars?'

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SearchLove London | Annie Cushing, 'Are Your Google Analytic's Reports Pretty Little Liars?'

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It can be easy to be fooled by complicated data. But don’t fret, Google Analytics expert Annie is here to show us how to ensure we’re making wise data-driven decisions. Hear about some of the worst mistakes companies can make, and how we can avoid these in our own analytics.

It can be easy to be fooled by complicated data. But don’t fret, Google Analytics expert Annie is here to show us how to ensure we’re making wise data-driven decisions. Hear about some of the worst mistakes companies can make, and how we can avoid these in our own analytics.

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SearchLove London | Annie Cushing, 'Are Your Google Analytic's Reports Pretty Little Liars?'

  1. Are Your Reports Pretty Little Liars? Annie Cushing @AnnieCushing
  2. just call me DR DATA
  3. relying on nebulous “USER” data
  4. USERS (FKA UNIQUE VISITORS) NEW USERS DAYS SINCE LAST SESSION % NEW SESSIONS COUNT OF SESSIONS USER TYPE (NEW/RET VISITOR) USER DEFINED VALUES metric dimension
  5. =
  6. google knows these are not visitors.
  7. there are a few
  8. site must have a login
  9. visitors need to actually log in
  10. site must upgrade to universal
  11. site must capture customer id
  12. you must set up a new view
  13. WARNING: your user data will shrink
  14. quite the BOON for sites like these ….
  15. ignoring LOGGED IN users
  16. TWO WAYS to accomplish this
  17. custom variables
  18. custom variables
  19. custom dimensions
  20. custom dimensions
  21. custom dimension setup
  22. good candidates • customer id
  23. good candidates • customer id • logged in
  24. good candidates • customer id • logged in • author
  25. good candidates • customer id • logged in • author • page category (not tags)
  26. good candidates • customer id • logged in • author • page category (not tags) • publication date
  27. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender
  28. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age
  29. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level
  30. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level
  31. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level • number of help articles viewed
  32. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level • number of help articles viewed • complaint
  33. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level • number of help articles viewed • complaint
  34. missing metrics that MATTER
  35. good candidates • cost of goods sold
  36. good candidates • cost of goods sold • profit
  37. good candidates • cost of goods sold • profit • margin
  38. good candidates • cost of goods sold • profit • margin • number of members
  39. good candidates • cost of goods sold • profit • margin • number of members • population
  40. good candidates • cost of goods sold • profit • margin • number of members • population • game score
  41. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards
  42. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points
  43. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points • email opens
  44. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points • email opens • email sends
  45. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points • email opens • email sends
  46. whatMARKETERScare about
  47. whatBUSINESS OWNERScare about
  48. when these COLLIDING realities intersect
  49. examples from the WILD
  50. email metrics in campaign reports? ALL DAY
  51. see medium history for a registered USER
  52. hosing your CAMPAIGN tagging
  53. labels matter
  54. reports impacted if you get MEDIUM wrong
  55. what happens when you tag INTERNAL links
  56. 1.4 MILLION sessions overwritten
  57. 1.4 MILLION sessions overwritten
  58. using THIRD-PARTY services
  59. booking engines
  60. event services
  61. payment gateways
  62. application sites
  63. partner sites
  64. PLAN A keep them on your site
  65. PLAN B cross-domain tracking
  66. how to CHECK your tracking
  67. STEP 1: install google analytics debugger
  68. STEP 2: open the console ctrl-shift-j command-opt-j
  69. STEP 3: check the visitor/client id
  70. STEP 4: make sure domain is different
  71. STEP 5: check for a match
  72. PRO TIP: set up auto-linking
  73. GET ALL THE LINKS

Editor's Notes

  • I DO A LOT OF ANALYTICS AUDITS, AND THERE ARE SOME ISSUES THAT I SEE ON A REGULAR BASIS.

    BUT NOT ALL ISSUES ARE THE RESULT OF IMPLEMENTATION ERRORS. SOMETIMES THEY’RE THE RESULT OF NOT UNDERSTANDING THE LIMITATIONS OF GA.
  • National Insurance Number
  • THESE ARE VISITORS
  • THESE ARE VISITORS
  • THESE ARE NOT VISITORS
  • SOME OF THESE BOGUS KPIS INCLUDE …
  • DON’T ALLOW YOURSELF TO BE HELD ACCOUNTABLE TO CRAP METRICS.
  • PRETTIEST LIAR
  • I’LL SHOW YOU
  • CLASSIC
  • UNIVERSAL

    GOOGLE NEEDED TO MAKE THIS CHANGE TO MAKE WAY FOR …
  • TO TRY TO ADDRESS THIS ISSUE, GOOGLE ANNOUNCED USER-ID FEATURE
  • THIS VIEW ONLY CAPTURES LOGGED IN USERS
  • THIS VIEW ONLY CAPTURES LOGGED IN USERS
  • VISITORS ARE THE ROYALTY OF YOUR SITE
  • AND WHAT DO WE DO TO ROYALTY? WE STALK THEM.
  • 1.4 MIL SESSIONS OVERWRITTEN
  • IF YOU DON’T SET THIS UP THIS IS WHAT WILL HAPPEN TO YOUR DATA …

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