Uploaded byKirk Williams
The Beginner's Guide to Google Analytics
The document serves as a beginner’s guide to Google Analytics, focusing on understanding audience acquisition, user behavior, and conversion rates. It emphasizes common mistakes made in data analysis, such as over-focusing on bounce rates and ignoring attribution and context. The guide encourages a human-centered approach to data analysis, advocating for understanding user goals rather than just numbers.
More Related Content
Similar to The Beginner's Guide to Google Analytics
![[DSC Europe 25] Nikolay Burlutskiy - Best Practices for Building Enterprise M...](https://cdn.slidesharecdn.com/ss_thumbnails/uirvaiuvq8y1w8hzd9tx-7-251212103249-2619edb4-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Debmalya Biswas - Agentification: the art of transforming man...](https://cdn.slidesharecdn.com/ss_thumbnails/r5azlggvtqiaiiusrqdr-4-251212103249-5a12c89b-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Branko Dzakula - From Defense to Attack: How AI Redefines Cyb...](https://cdn.slidesharecdn.com/ss_thumbnails/80bdzdxpr3ky2g0qvyk9-8-251211083048-ce5fc1ee-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Marija Vlajkovic & Andrea Radonjanin - Integration of AI tool...](https://cdn.slidesharecdn.com/ss_thumbnails/qf1jrglttoc3bm8s3aop-final-integration-of-ai-tools-251208151905-394f3a6a-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Dobrica Cosic - Savings by the Second: How Dynamic Pricing an...](https://cdn.slidesharecdn.com/ss_thumbnails/znp09f3smtqz3w2sq6wn-1-dobrica-cosic-savings-by-the-second-how-dynamic-pricing-and-smart-data-are-bu-251208151905-26e6f41e-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Branko Urosevic -Rethinking Financial Talent: Integrating Cod...](https://cdn.slidesharecdn.com/ss_thumbnails/8jjrus8ttko6qj64f58f-3-251212103250-642c6374-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Hans Kleinsman - The Compliance Gearbox: How Tax Tech Mediate...](https://cdn.slidesharecdn.com/ss_thumbnails/dxdytie1toel0hr90bjs-2-251212103250-174fdbe7-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Milan Sekuloski - Data, Defence, and Development: Cybersecuri...](https://cdn.slidesharecdn.com/ss_thumbnails/dfrkwwx4qly6atqpbl4z-4-251209104645-c3d4b0ca-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Sara Polak - The Archaeology of Innovation: AI as the Next Cr...](https://cdn.slidesharecdn.com/ss_thumbnails/7ecbscdnt8mlcuqbd2ln-2-sara-polak-ai-creative-industries-251208152533-aa1fcf54-thumbnail.jpg?width=640&height=640&fit=bounds)
The Beginner's Guide to Google Analytics
- 1. The Beginner’s Guideto Google Analytics Billings Advertising & Marketing Club, February 2017
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8. @PPCKirk zatomarketing.com Google Analytics:The Walkthrough
- 9. @PPCKirk zatomarketing.com Google Analytics:The Walkthrough
- 10. @PPCKirk zatomarketing.com Who canuse Google Analytics? CEOs SMB Owners MarketingNerds
- 11. @PPCKirk zatomarketing.com NOTE! Thispresentation is NOT:
- 12. @PPCKirk zatomarketing.com This presentationIS: Audience Acquisition Behavior Conversions Mistakes to Avoid
- 13.
- 14. Section 1: Audience (whois visiting)
- 15.
- 16.
- 17.
- 18. Section 2: Acquisition (Wheredid they come from?)
- 19.
- 20.
- 21. Section 3: Behavior (Howdid they act when they visited?)
- 22.
- 23.
- 24.
- 25. Section 4: Conversions (Whatdid they accomplish on their visit?)
- 26.
- 27.
- 28.
- 29.
- 30. @PPCKirk zatomarketing.com 5 MistakesAnalysis Posers Make http://25.media.tumblr.com/b86f4540af360fa55ca031968db495b3/tumblr_mm2ailAxvk1sn8ji0o1_400.gif https://media.giphy.com/media/22ANyGx7WK4CI/giphy.gif
- 31. @PPCKirk zatomarketing.com #1 –Analysis Posers focus on Bounce Rate
- 32.
- 33. @PPCKirk zatomarketing.com When aclient asks about Bounce Rate.
- 34. @PPCKirk zatomarketing.com Instead ask: Didyour visitor accomplish what they came for? Did you engage your visitor?
- 35. @PPCKirk zatomarketing.com #2 –Analysis Posers ignore Attribution “I’m Gertrude.”
- 36.
- 37.
- 38.
- 39.
- 40.
- 41.
- 42.
- 43. @PPCKirk zatomarketing.com Attribution asks: Whoshould get the credit for Gertrude’s sale?
- 44. @PPCKirk zatomarketing.com Begin with: Analyzingdifferent attribution models.
- 45.
- 46. @PPCKirk zatomarketing.com #3 –Analysis Posers don’t go Beyond the Data
- 47. @PPCKirk zatomarketing.com Don’t BeScrooge McData! Data Needs (Good) Analysis & Action
- 48. @PPCKirk zatomarketing.com #4 –Analysis Posers ignore History & Context
- 49. @PPCKirk zatomarketing.com (the machineshaven’t won yet!) The Good News:
- 50.
- 51.
- 52. @PPCKirk zatomarketing.com #5 –Analysis Posers forget about people
- 53.
- 54. @PPCKirk zatomarketing.com Psychometrics vsDemographics Electiontargeting:
- 55. @PPCKirk zatomarketing.com “before long,he was able to evaluate a person better than the average work colleague, merely on the basis of ten Facebook “likes.” Seventy “likes” were enough to outdo what a person’s friends knew, 150 what their parents knew, and 300 “likes” what their partner knew. More “likes” could even surpass what a person thought they knew about themselves.”
- 56. @PPCKirk zatomarketing.com Analysis posersfocus on data. Analysis ninjas focus on people.
- 57. Thank You! Questions, Ideas,Insults? Slides + Links: zatomarketing.com/analytics