SearchLove London | Annie Cushing, 'Are Your Google Analytic's Reports Pretty Little Liars?'
•Transferir como PPTX, PDF•
23 gostaram•24,943 visualizações
The document discusses issues with using "user" metrics in analytics reports for websites that require users to log in. It recommends setting up custom dimensions in Google Analytics to properly track logged-in users versus visitors. Some key custom dimensions to track include customer ID, logged-in status, author, and membership level to gain more accurate insights about your real users.
Recomendados
Recomendados
Mais conteúdo relacionado
Destaque
Destaque (15)
Semelhante a SearchLove London | Annie Cushing, 'Are Your Google Analytic's Reports Pretty Little Liars?'
Semelhante a SearchLove London | Annie Cushing, 'Are Your Google Analytic's Reports Pretty Little Liars?' (20)
Mais de Distilled
Mais de Distilled (20)
Último
Último (20)
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
- 7. 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
- 8. =
- 10. google knows these are not visitors.
- 15. there are a few
- 16. site must have a login
- 17. visitors need to actually log in
- 18. site must upgrade to universal
- 19. site must capture customer id
- 20. you must set up a new view
- 21. WARNING: your user data will shrink
- 22. quite the BOON for sites like these ….
- 25. ignoring LOGGED IN users
- 28. TWO WAYS to accomplish this
- 29. custom variables
- 30. custom variables
- 34. good candidates • customer id
- 35. good candidates • customer id • logged in
- 36. good candidates • customer id • logged in • author
- 37. good candidates • customer id • logged in • author • page category (not tags)
- 38. good candidates • customer id • logged in • author • page category (not tags) • publication date
- 39. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender
- 40. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age
- 41. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level
- 42. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level
- 43. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level • number of help articles viewed
- 44. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level • number of help articles viewed • complaint
- 45. good candidates • customer id • logged in • author • page category (not tags) • publication date • gender • age • membership level • number of help articles viewed • complaint
- 46. missing metrics that MATTER
- 47. good candidates • cost of goods sold
- 48. good candidates • cost of goods sold • profit
- 49. good candidates • cost of goods sold • profit • margin
- 50. good candidates • cost of goods sold • profit • margin • number of members
- 51. good candidates • cost of goods sold • profit • margin • number of members • population
- 52. good candidates • cost of goods sold • profit • margin • number of members • population • game score
- 53. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards
- 54. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points
- 55. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points • email opens
- 56. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points • email opens • email sends
- 57. good candidates • cost of goods sold • profit • margin • number of members • population • game score • awards • points • email opens • email sends
- 61. when these COLLIDING realities intersect
- 62. examples from the WILD
- 63. email metrics in campaign reports? ALL DAY
- 64. see medium history for a registered USER
- 65. hosing your CAMPAIGN tagging
- 66. labels matter
- 70. reports impacted if you get MEDIUM wrong
- 78. what happens when you tag INTERNAL links
- 79. 1.4 MILLION sessions overwritten
- 80. 1.4 MILLION sessions overwritten
- 83. booking engines
- 84. event services
- 85. payment gateways
- 87. partner sites
- 89. PLAN A keep them on your site
- 90. PLAN B cross-domain tracking
- 91. how to CHECK your tracking
- 92. STEP 1: install google analytics debugger
- 93. STEP 2: open the console ctrl-shift-j command-opt-j
- 94. STEP 3: check the visitor/client id
- 95. STEP 4: make sure domain is different
- 96. STEP 5: check for a match
- 97. PRO TIP: set up auto-linking
- 98. GET ALL THE LINKS
Notas do Editor
- 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 …