Better conversion with Intelligent Analytics

1. Better conversion with Intelligent Analytics @pjeedai

2. Turn user behaviour data into Value

3. Understand causes of bottlenecks on key areas

4. Think through factors that may impact results

5. Think about YOUR Data

6. What YOU can investigate

7. What YOU can investigateExamples and Ideas

8. Inspire

9. Tag Manager Makes all this MUCH easier to do

10. Tags Add Events, Scripts with Custom Triggers Add Extra Dimensions, Metrics, Data to Pageviews, Events, Hits

11. Variables Collect Data as Variables Use Variables for fields in Google Analytics

12. Page Values Collect Data from Page or from Back end Source

13. Send to dataLayer Collect Data to use in Tag Manager Variables Variables can also be captured without dataLayer

14. Event Variables Variables Can Be Variable

15. Lookup Variables If Bike Stock Type = New then use New Stock Variables If Bike Stock Type = Used then use Used Stock Variables

16. Variables for Custom Dimensions Custom Dimension Variables

17. Tools & Stacks

18. Google Analytics API Script or paid plugins like

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. 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. 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. 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. Bigger datasets, more powerful statistical analysis PowerBI cloud has real language querying “What is the best performing product for revenue this Quarter?”

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. 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. User & Session Recording

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. Hit level – Measurement Protocol

29. Gets a little spendy at the top end but Free Option is good to try out

30. Metrics and Measurement

31. Success Events If [Good Thing] happens Track [Good Thing] Ideally [Type of Thing] | [Which Specific Thing] | [What Details]

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. Fail Events

34. Fail Events Common Issue for eCommerce, Appointments, Holidays • Conversion Rate Affected by Stock Levels & Availability

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. Stock Level Events

37. Event Goals

38. Event Goals Goals are NOT just Sale Complete URL Clicks, Add, Viewed, Video Play Events are Ideal for segmenting Goals

39. Exeter Any Day Event Click To Book Individual, Create Team or Join Team Exeter – Any Day

40. Exeter Saturday Event Click To Book Individual, Create Team or Join Team Exeter – Saturday ONLY

41. Exeter Sunday Event Click To Book Individual, Create Team or Join Team Exeter – Sunday ONLY

42. Clicks By Race Day With 3 Event Goals – break down ways Total is reached Compare Click Volumes per Race Day AND Total

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. Custom Dimensions

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. 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. 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. 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. 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. 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. Custom Reports Extremely useful: within the limits of the User Interface For complex reports use API

52. Custom Reports Clicks to Book – By Ticket Type (from Event tracking)

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. Annotations Record Changes to Tracking Record Campaign and Promotion Periods Highlight ‘odd’ data to investigate

55. Lots of Data Pull data via API Match against Business Data Automate Crunch Segments, Dimensions, Metrics Take Action

56. PowerQuery > Data Model > PowerPivot Measures > Excel visualisation Same Data Model, Measures and Query could also be used in PowerBI

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. Calculated Metrics Custom Filters Visualisations Links to: • Google Analytics • AdWords • Sheets • BigQuery and more

59. • Automatic Refresh • Custom Filters • Date Range • Dimension Filters • Custom Segments Up to 20 Pages per Report Unlimited Reports Free!

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. Custom Segments

62. Custom Segments User has Clicked Enquiry on Used Motorcycle Label of Event Contains Year: Segment to 2015

63. Channels Report Segment can then be applied to other Reports: e.g. Channels

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. Shortcuts Create shortcuts to quickly re-apply Segments/Filters

66. Referrers Advertising Stock Costs Money Different Portals Different Audiences Different Stock

67. Referrers “Price To” from ?= Search string from Referral Path

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. Custom Channel Groups

70. Custom Channel Groups Default Channels are ok if you tag inbound links and adverts accurately But Custom Channel Groupings are better

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. 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. 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. Demographic Reports

75. Demographic Reports Make sure Privacy Policy is compliant Make sure this option is legal in your territories

76. Age Bands User Volumes By Age Band

77. Gender User Volumes by Gender

78. Affinity & Interests Interesting? Useful?

79. Affinity & Interests Combine with a Segment of Users Who Clicked but didn’t Enquire …can be Targeted with appropriate Stock

80. Content Grouping

81. Content Grouping Must have Edit Permission for the View • Template • Type • Categories

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. Content Grouping Pages Report – lots of detail Hard to see which Categories work best

84. Content Grouping Categories Content Group Applied: Apparel clearly more popular than Brands and Bags Can also be used to create Segments

85. Think about YOUR Data

86. What YOU can investigate

87. @pjeedai Data is one thing Intelligent use of data is everything

88. Thank you for listening

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