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Better conversion with Intelligent Analytics

As presented at #BrightonSEO and #SearchElite Manchester September 2017
Uploaded version is the longer deck as used for #SASCon 2017 - examples and use cases for Google Analytics features and tools to make more from the data you can collect.

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Better conversion with Intelligent Analytics

  1. 1. Better conversion with Intelligent Analytics @pjeedai
  2. 2. Turn user behaviour data into Value
  3. 3. Understand causes of bottlenecks on key areas
  4. 4. Think through factors that may impact results
  5. 5. Think about YOUR Data
  6. 6. What YOU can investigate
  7. 7. What YOU can investigateExamples and Ideas
  8. 8. Inspire
  9. 9. Tag Manager Makes all this MUCH easier to do
  10. 10. Tags Add Events, Scripts with Custom Triggers Add Extra Dimensions, Metrics, Data to Pageviews, Events, Hits
  11. 11. Variables Collect Data as Variables Use Variables for fields in Google Analytics
  12. 12. Page Values Collect Data from Page or from Back end Source
  13. 13. Send to dataLayer Collect Data to use in Tag Manager Variables Variables can also be captured without dataLayer
  14. 14. Event Variables Variables Can Be Variable
  15. 15. Lookup Variables If Bike Stock Type = New then use New Stock Variables If Bike Stock Type = Used then use Used Stock Variables
  16. 16. Variables for Custom Dimensions Custom Dimension Variables
  17. 17. Tools & Stacks
  18. 18. Google Analytics API Script or paid plugins like
  19. 19. Google Analytics API to Excel, csv or database Also API for Facebook, Twitter, AdWords, Search Console & more Allows scheduled and batch data pulls
  20. 20. Google Analytics API to Excel, csv or database Also API for Facebook, Twitter, AdWords, Search Console & more Allows scheduled and batch data pulls Also has save to Drive or Cloud database options
  21. 21. Combine Business & Web Data Deeper Analysis and visualisation PowerBI also has native API link to Google Analytics API etc. PowerQuery and PowerBI have REST JSON API (make your own query from any source API) Also have csv folder source extract for batch processing
  22. 22. Requires a little more work than just using the Analytics UI, more powerful than VLOOKUP This is an example Data Model to split detail out This lets you combine, calculate and filter as needed
  23. 23. Bigger datasets, more powerful statistical analysis PowerBI cloud has real language querying “What is the best performing product for revenue this Quarter?”
  24. 24. Geo lookup or CRM for customer addresses (or combine both) Visualise on drill down map or overlay ArcGIS data e.g. Conversions > Avg from Areas with Household income > average
  25. 25. Average alone can mask opportunity & risks Tableau, R or PowerBI to analyse distribution patterns Best for your lowest value, least loyal customers May be Worst for your big spenders Often not a business win even if it reports as a Test or Campaign win
  26. 26. User & Session Recording
  27. 27. Also, you know. Maybe talk to them Ask Customer Services, Feedback form responses, BCC on Customer Service emails, Jump on a Live Chat
  28. 28. Hit level – Measurement Protocol
  29. 29. Gets a little spendy at the top end but Free Option is good to try out
  30. 30. Metrics and Measurement
  31. 31. Success Events If [Good Thing] happens Track [Good Thing] Ideally [Type of Thing] | [Which Specific Thing] | [What Details]
  32. 32. Success Events - Detail Why this much Detail? • Custom Segments • Remarketing • Event Goals • Filters and Offline Analysis (Dealer ID & stockID) (need to be a bit careful of high cardinality on unfiltered or unsegmented data)
  33. 33. Fail Events
  34. 34. Fail Events Common Issue for eCommerce, Appointments, Holidays • Conversion Rate Affected by Stock Levels & Availability
  35. 35. Fail Events - Detail Don’t just send Event on Success; Also send on Fail • IF it failed • WHAT type of Failure • WHERE it failed (WHICH use case)
  36. 36. Stock Level Events
  37. 37. Event Goals
  38. 38. Event Goals Goals are NOT just Sale Complete URL Clicks, Add, Viewed, Video Play Events are Ideal for segmenting Goals
  39. 39. Exeter Any Day Event Click To Book Individual, Create Team or Join Team Exeter – Any Day
  40. 40. Exeter Saturday Event Click To Book Individual, Create Team or Join Team Exeter – Saturday ONLY
  41. 41. Exeter Sunday Event Click To Book Individual, Create Team or Join Team Exeter – Sunday ONLY
  42. 42. Clicks By Race Day With 3 Event Goals – break down ways Total is reached Compare Click Volumes per Race Day AND Total
  43. 43. Buy to Click Ratios Calculate Buy to Click Ratios by Day AND Totals Sunday has higher drop-off between Click and Buy 1560 ÷ 3164 = 49.30% 466 ÷ 1041 = 44.76% 2015 ÷ 4067 = 49.54%
  44. 44. Custom Dimensions
  45. 45. Custom Dimensions • Product SKU • Site ID • Visitor ID • User Lifetime Value • Product Availability • Previous Submit • Checkout Account Type • Guest • New Account • Logged In • Experiment ID and Experiment Variant Practically almost any Dimension you might want to track (non PII) Limited to 20 on a standard account. 200 on GA Premium 
  46. 46. Custom Dimensions Dealer ID to link back to Network CRM Dealer Name varies (multi-franchise) Report whole network performance but link back to Dealer Sales
  47. 47. Custom Metrics Dimensions Describe Data Metrics Measure Data • User/Session of Described Type – Count • Lead Value • Original Price • Displayed Price • Product Level Discount Value • Volume Level Discount Applied • Finance Deposit
  48. 48. Custom Metrics Frequently eCommerce will pass either • Original Price (even if discounted Price is applicable) • Discount Price (but no idea how much discount vs. Original Price) Custom Metrics allows you to record & calculate the differences Reports can then show Value Lost in discount/promotions This allows you to report real Revenue performance
  49. 49. Calculated Metrics Calculate Business Metrics not Web Metrics Original Price – Discount Price Applied = Value Lost Buy Exeter Tickets ÷ Click Exeter = Buy to Click Rate
  50. 50. Calculated Metrics Buy Exeter Tickets ÷ Click Exeter = Clicks to Buy Rate But we can now use this in Custom Reports By Channel | By Campaign | By Device | By Gender | By Age etc.
  51. 51. Custom Reports Extremely useful: within the limits of the User Interface For complex reports use API
  52. 52. Custom Reports Clicks to Book – By Ticket Type (from Event tracking)
  53. 53. Custom Reports Drill down to Individual Tickets – Exeter Saturday has most Clicks Compare performance of Exeter Saturday 2016 vs Exeter Saturday 2017 Are sales behind schedule? Traffic growth but lower Clicks to Buy Ratio?
  54. 54. Annotations Record Changes to Tracking Record Campaign and Promotion Periods Highlight ‘odd’ data to investigate
  55. 55. Lots of Data Pull data via API Match against Business Data Automate Crunch Segments, Dimensions, Metrics Take Action
  56. 56. PowerQuery > Data Model > PowerPivot Measures > Excel visualisation Same Data Model, Measures and Query could also be used in PowerBI
  57. 57. Manual: Pull data via API to Sheets and Download. Download csv from GA UI Automate: Next Analytics, Analytics Edge, Supermetrics, Power Query, PowerBI
  58. 58. Calculated Metrics Custom Filters Visualisations Links to: • Google Analytics • AdWords • Sheets • BigQuery and more
  59. 59. • Automatic Refresh • Custom Filters • Date Range • Dimension Filters • Custom Segments Up to 20 Pages per Report Unlimited Reports Free!
  60. 60. Distribution matters • 2 users off the scale of this chart • 130+ items per purchase “Average” is misleading if you don’t know how it is distributed
  61. 61. Custom Segments
  62. 62. Custom Segments User has Clicked Enquiry on Used Motorcycle Label of Event Contains Year: Segment to 2015
  63. 63. Channels Report Segment can then be applied to other Reports: e.g. Channels
  64. 64. Segment Share of Contribution Share of Sessions or Event Clicks easily compared Or Targeted. Or Reported against # Stock of each Year being advertised etc.
  65. 65. Shortcuts Create shortcuts to quickly re-apply Segments/Filters
  66. 66. Referrers Advertising Stock Costs Money Different Portals Different Audiences Different Stock
  67. 67. Referrers “Price To” from ?= Search string from Referral Path
  68. 68. Regular Expressions “Price To” from ?= Search string from Referral Path BUT Different format for “Price To” Search on EVERY Referrer site  Expresso http://www.ultrapico.com/expresso.htm (Windows Only) http://regexr.com/ (Browser)
  69. 69. Custom Channel Groups
  70. 70. Custom Channel Groups Default Channels are ok if you tag inbound links and adverts accurately But Custom Channel Groupings are better
  71. 71. Custom Channel Groups Start with Default as Template Divide Channels more precisely Rules apply top to bottom Different Groups Different Levels of Detail Paid Search is a good example By Type: • Shopping • Display • Text • Video By Spend: • High Cost • Medium Cost • Low Cost Buying Cycle, Competition, Volume etc.
  72. 72. This is a Top line Channel splitting – slightly more detail than Default Channels: • Paid: Main Ad types, Top Funnel, Middle Funnel, Bottom Funnel • Organic Search: Google or Other – Keyword Known/Not Provided • Email: Which Mailing list (Mailchimp or EventBrite) Custom Channel Groups
  73. 73. Custom Channel Groups More detail on Paid Campaigns • Paid Search • Paid Social 2nd Dimension of Keyword Exeter Race promotion • Keywords mostly Exeter • Some Top Funnel generic
  74. 74. Demographic Reports
  75. 75. Demographic Reports Make sure Privacy Policy is compliant Make sure this option is legal in your territories
  76. 76. Age Bands User Volumes By Age Band
  77. 77. Gender User Volumes by Gender
  78. 78. Affinity & Interests Interesting? Useful?
  79. 79. Affinity & Interests Combine with a Segment of Users Who Clicked but didn’t Enquire …can be Targeted with appropriate Stock
  80. 80. Content Grouping
  81. 81. Content Grouping Must have Edit Permission for the View • Template • Type • Categories
  82. 82. Content Grouping Tag Manager makes it easier dataLayer makes it simple Page Template: Used Bikes > All Used Stock Page Titles Makes: Ducati > Ducati Models Models: Multistrada > Multistrada Variants (S, Enduro etc.) Model Year: 2009, 2010, 2011 etc. Price Bands: £5,000 - £7,499 | £7,500 - £8,999 | £9,000 - £10,499 etc Up to 5 Groupings
  83. 83. Content Grouping Pages Report – lots of detail Hard to see which Categories work best
  84. 84. Content Grouping Categories Content Group Applied: Apparel clearly more popular than Brands and Bags Can also be used to create Segments
  85. 85. Think about YOUR Data
  86. 86. What YOU can investigate
  87. 87. @pjeedai Data is one thing Intelligent use of data is everything
  88. 88. Thank you for listening
  89. 89. Resources Get Started With Tag Manager & Google Analytics https://analytics.google.com/analytics/academy/ Learning Resources https://www.simoahava.com/ http://www.lunametrics.com/blog/ http://www.analytics-ninja.com/blog.html Demographics and Advertising Tools Policy Requirements https://support.google.com/analytics/answer/2700409?hl=en-GB&utm_id=ad Regular Expression Tools http://regexr.com/ (Online) http://www.ultrapico.com/Expresso.htm (Windows) Data Studio https://www.google.com/analytics/data-studio/ API Tools http://www.analyticsedge.com/ https://supermetrics.com/ http://www.nextanalytics.com/ https://powerbi.microsoft.com/en-us/ https://www.scitylana.com/ Connect on Linked In https://www.linkedin.com/in/timstewart/ Or ask me a question on Twitter @pjeedai