The document outlines a technical SEO workshop focused on utilizing SQL and BigQuery for efficient data analysis, particularly in environments with large datasets. It provides a roadmap for merging data sources, performing site audits, and integrating various analytics tools like Google Analytics and Search Console to derive insights. Additional tips on site consolidation and the importance of structured data management are also highlighted.

1. @haydenroche3
Technical SEO at
Scale
What to do when your Excel keeps
crashing
1
Senior Analyst, Red Ventures
@haydenroche3 #ReadingSEO

2. What we’ll walk through
● Brief intro to SQL and BigQuery
2@haydenroche3 #ReadingSEO

3. What we’ll walk through
● Brief intro to SQL and BigQuery
● Merging large data sets and the power you’ll gain
3@haydenroche3 #ReadingSEO

4. What we’ll walk through
● Brief intro to SQL and BigQuery
● Merging large data sets and the power you’ll gain
● Projects that YOU can do with “minimal-ish”
analytical effort
4@haydenroche3 #ReadingSEO

5. 5

6. Intro to BigQuery and
SQL 6#ReadingSEO

7. BigQuery 101
Online database where you
can quickly pull data using
SQL
@haydenroche3 #ReadingSEO 7

8. Joining Data Sources w/ SQL
http://www.sql-join.com/sql-join-types
@haydenroche3 #ReadingSEO 8

9. FREE SQL Resources
9
1. https://www.w3schools.com/sql/default.asp
2. https://www.udemy.com/topic/sql/free/
3. https://www.codecademy.com/learn/learn-sql
4. Your colleagues and technical SEO community
@haydenroche3 #ReadingSEO

10. BigQuery and SQL for SEOs
linkedin.com/in/areejabuali/
@areej_abuali
Find Areej’s slides here!
@haydenroche3 #ReadingSEO 10

11. Challenge 11#ReadingSEO

12. 12
“My website has 10K → 100K → 1M → 10M pages.
Doing analysis takes a decade without a data
expert.”
“Impossible” Technical SEO
@haydenroche3 #ReadingSEO

13. 13
“Two stars in the universe have the same chance of
colliding as 4 bumblebees randomly flying around
the United States.”
Seemingly Impossible Statistic
@haydenroche3 #ReadingSEO

14. 14
“My website has 10K → 100K → 1M → 10M pages.
Doing analysis takes a decade without a data
expert.”
Not So “Impossible” Technical SEO
@haydenroche3 #ReadingSEO

15. Case Study:
15
“StockX is the world’s
first stock market for
things – a live ‘bid/ask’
marketplace.”
@haydenroche3 #ReadingSEO

16. Starting with a Crawl
16
Find all the URLs!
Over 100K found
URLs in a starter
crawl
@haydenroche3 #ReadingSEO

17. Export All URLs
17@haydenroche3 #ReadingSEO

18. Get Everything Into
BigQuery
18#ReadingSEO

19. 1. Getting a CSV in BigQuery
19
Upload from local storage (your desktop)
→ File size limitations
@haydenroche3 #ReadingSEO

20. 1. Getting a CSV in BigQuery
20
Better strategy: upload csv to
Cloud Storage
@haydenroche3 #ReadingSEO

21. 2. Creating a Storage Bucket
21
https://cloud.google.com/storage/docs/
creating-buckets
@haydenroche3 #ReadingSEO

22. 3. Create a Data Table
22
Create Table within your
“techseo” project
@haydenroche3 #ReadingSEO

23. 3. Create a Data Table
23
Find your Screaming Frog CSV from
Cloud Storage
@haydenroche3 #ReadingSEO

24. Trust the Process
24#ReadingSEO

25. Starting with a Crawl
25
sf.URL sf.Status_Code
/ 200
/supreme 200
/supreme/jackets 200
/supreme-duffle-bag-ss18-black 200
/nike/airmax/95 200
@haydenroche3 #ReadingSEO

26. Starting with a Crawl
26
SELECT
sf.URL,
sf.Status_Code
FROM `techseo.site_audit.screaming_frog_crawl` sf
LIMIT 5
@haydenroche3 #ReadingSEO

27. Starting with a Crawl
27
sf.URL sf.Status_Code
/ 200
/supreme 200
/supreme/jackets 200
/supreme-duffle-bag-ss18-black 200
/nike/airmax/95 200
SELECT
sf.URL,
sf.Status_Code
FROM
`techseo.site_audit.screaming_frog_
crawl` sf
LIMIT 5
@haydenroche3 #ReadingSEO

28. Now Let’s Add
More Data
28#ReadingSEO

29. Adding in Google Analytics
29
Option 1:
Export Data
From UI
Pros: Easy, Quick
Cons: Manual
@haydenroche3 #ReadingSEO

30. Adding in Google Analytics
30
Option 1:
Export Data
From UI
Pros: Easy, Quick
Cons: Manual
Option 2:
BigQuery + GA Data
Integration
Pros: Automated & granular
Cons: API setup knowledge*
@haydenroche3 #ReadingSEO

31. Not real data!
31@haydenroche3 #ReadingSEO

32. Adding in Google Analytics
32
ga.Landing_Page ga.Sessions ga.Sales ga.Revenue
/ 800,000 12,800 £576,000
/supreme 29,000 145 £11,165
/supreme/jackets 6,700 52 £11,759
/supreme-duffle-bag-ss18-black 4,400 48 £19,360
/nike/airmax/95 18,800 47 £3,055
@haydenroche3 #ReadingSEO

33. Adding in Google Analytics
33
ga.Landing_Page ga.Sessions ga.Sales ga.Revenue
/ 800,000 12,800 £576,000
/supreme 29,000 145 £11,165
/supreme/jackets 6,700 52 £11,759
/supreme-duffle-bag-ss18-black 4,400 48 £19,360
/nike/airmax/95 18,800 47 £3,055
SELECT
ga.Landing_Page,
ga.Sessions,
ga.Sales,
ga.Revenue
FROM
`techseo.site_audit.google_analyti
cs` ga
@haydenroche3 #ReadingSEO

34. Join SF and GA
34#ReadingSEO

35. Joining SF & GA
35
SELECT
sf.URL,
sum(ga.Sessions) AS Sessions,
sum(ga.Revenue) AS Revenue,
round(ga.Revenue/ga.Sessions, 2) AS RPS
FROM
`techseo.site_audit.screaming_frog_crawl` sf
LEFT JOIN `techseo.site_audit.google_analytics` ga ON (sf.URL = ga.Landing_Page)
GROUP BY
sf.URL
@haydenroche3 #ReadingSEO

36. Joining SF & GA
36
SELECT
sf.URL,
sum(ga.Sessions) AS Sessions,
sum(ga.Revenue) AS Revenue,
round(ga.Revenue/ga.Sessions, 2) AS RPS
FROM
`techseo.site_audit.screaming_frog_crawl` sf
LEFT JOIN
`techseo.site_audit.google_analytics` ga ON
(sf.URL = ga.Landing_Page)
GROUP BY
sf.URL
sf.URL ga.Sessions ga.Revenue ga.RPS
/ 800,000 £576,000 £0.72
/supreme 29,000 £11,165 £0.39
/supreme/jackets 6,700 £11,759 £1.76
/supreme-duffle-bag-
ss18-black
4,400 £19,360 £4.40
/nike/airmax/95 18,800 £3,055 £0.16
@haydenroche3 #ReadingSEO

37. Adding in Search
Console
37#ReadingSEO

38. Once again…
Not real data!
38@haydenroche3 #ReadingSEO

39. Adding in Search Console
39
gsc.Landing_Page gsc.Query gsc.Clicks gsc.Impressions gsc.Position
/ stockx 658,400 823,000 1.00
/supreme supreme 79,300 1,220,000 4.00
/supreme/jackets supreme windbreaker 1,260 3,600 1.00
/supreme-duffle-bag-ss18-black supreme duffle bag 2,268 8,100 1.20
/nike/airmax/95 nike airmax 95 925 74,000 6.50
@haydenroche3 #ReadingSEO

40. Joining SF, GA & GSC
40
SELECT
sf.URL,
sum(ga.Sessions) AS Sessions,
sum(ga.Revenue) AS Revenue,
count(gsc.Query) AS Ranking_KWs,
avg(gsc.Position) AS Avg_Pos
FROM `techseo.site_audit.screaming_frog_crawl` sf
LEFT JOIN `techseo.site_audit.google_analytics` ga ON (sf.URL = ga.Landing_Page)
LEFT JOIN `techseo.site_audit.google_search_console` gsc ON (sf.URL = gsc.Landing_Page)
GROUP BY
sf.URL
@haydenroche3 #ReadingSEO

41. Joining SF, GA & GSC
41
sf.URL ga.Sessions ga.Revenue gsc.Ranking_KWs gsc.Avg_Pos
/ 800,000 £576,000 30 1.00
/supreme 29,000 £11,165 7,900 4.00
/supreme/jackets 6,700 £11,759 610 1.00
/supreme-duffle-bag-ss18-black 4,400 £19,360 10 1.20
/nike/airmax/95 18,800 £3,055 4 6.50
@haydenroche3 #ReadingSEO

42. 42@haydenroche3 #ReadingSEO

43. Additional Data Sources
43
● Off-page factors → backlinks and referring domains
@haydenroche3 #ReadingSEO

44. Additional Data Sources
44
● Off-page factors → backlinks and referring domains
● Log files → Googlebot requests and frequency
@haydenroche3 #ReadingSEO

45. Additional Data Sources
45
● Off-page factors → backlinks and referring domains
● Log files → Googlebot requests and frequency
● Pagespeed insights → TTI, FCP, FMP
@haydenroche3 #ReadingSEO

46. Additional Data Sources
46
● Off-page factors → backlinks and referring domains
● Log files → Googlebot requests and frequency
● Pagespeed insights → TTI, FCP, FMP
● GSC Coverage Report → indexed pages
@haydenroche3 #ReadingSEO

47. Additional Data Sources
47
● Off-page factors → backlinks and referring domains
● Log files → Googlebot requests and frequency
● Keywords → 3rd party ranking data
● Pagespeed insights → TTI, FCP, FMP
● GSC Coverage Report → indexed pages
@haydenroche3 #ReadingSEO

48. 48
“Let’s Take A Quick Breather”
@haydenroche3 #ReadingSEO

49. Time to
Harness the
Data Power
49#ReadingSEO

50. 50
“If you flip a coin 100 times, there’s less than an 8%
chance that it ends 50/50.”
Seemingly Impossible Statistic
@haydenroche3 #ReadingSEO

51. Site Consolidation 51#ReadingSEO

52. Site Consolidation
52
Goal:
Identify low-value pages/subfolders
→ redirect or remove
@haydenroche3 #ReadingSEO

53. Site Consolidation
53
URL
/adidas-yeezy-boost-350-v2-black
/adidas-yeezy-boost-350-v2-core-black-green
/palace-pro-tool-t-shirt-black
/palace-pro-tool-t-shirt-white
/supreme-waist-bag-ss19-black
/supreme-waist-bag-ss19-light-blue
/jordan-4-retro-black-cement-2012
/jordan-4-retro-white-cement-2012
Start with a
sample → expand
to everything
@haydenroche3 #ReadingSEO

54. Site Consolidation
54
Use a CASE Statement to
group URLs at scale
@haydenroche3 #ReadingSEO

55. Site Consolidation
55
CASE
WHEN URL like ‘/adidas%’ THEN ‘Adidas’
WHEN URL like ‘/palace%’ THEN ‘Palace’
WHEN URL like ‘/supreme%’ THEN ‘Supreme’
WHEN URL like ‘/jordan%’ THEN ‘Air Jordan’
ELSE ‘’
END as Brand
Wildcard (%)
allows any
character/num
ber to follow
‘/adidas’
@haydenroche3 #ReadingSEO

56. Site Consolidation
56
Time to let go of
the manual Excel
filtering +
labelling
@haydenroche3 #ReadingSEO

57. Site Consolidation
57
Landing_Page Brand
/adidas-yeezy-boost-350-v2-black Adidas
/adidas-yeezy-boost-350-v2-core-black-green Adidas
/palace-pro-tool-t-shirt-black Palace
/palace-pro-tool-t-shirt-white Palace
/supreme-waist-bag-ss19-black Supreme
/supreme-waist-bag-ss19-light-blue Supreme
/jordan-4-retro-black-cement-2012 Air Jordan
/jordan-4-retro-white-cement-2012 Air Jordan
@haydenroche3 #ReadingSEO

58. Site Consolidation
58
Landing_Page Brand Type
/adidas-yeezy-boost-350-v2-black Adidas Shoes
/adidas-yeezy-boost-350-v2-core-black-green Adidas Shoes
/palace-pro-tool-t-shirt-black Palace T-Shirt
/palace-pro-tool-t-shirt-white Palace T-Shirt
/supreme-waist-bag-ss19-black Supreme Bag
/supreme-waist-bag-ss19-light-blue Supreme Bag
/jordan-4-retro-black-cement-2012 Air Jordan Shoes
/jordan-4-retro-white-cement-2012 Air Jordan Shoes
@haydenroche3 #ReadingSEO

59. Site Consolidation
59
Landing_Page Brand Type Colour
/adidas-yeezy-boost-350-v2-black Adidas Shoes Black
/adidas-yeezy-boost-350-v2-core-black-green Adidas Shoes Black-Green
/palace-pro-tool-t-shirt-black Palace T-Shirt Black
/palace-pro-tool-t-shirt-white Palace T-Shirt White
/supreme-waist-bag-ss19-black Supreme Bag Black
/supreme-waist-bag-ss19-light-blue Supreme Bag Light-Blue
/jordan-4-retro-black-cement-2012 Air Jordan Shoes Black
/jordan-4-retro-white-cement-2012 Air Jordan Shoes White
@haydenroche3 #ReadingSEO

60. Site Consolidation
60
Combining CASE statements
with what we learned before
@haydenroche3 #ReadingSEO

61. Site Consolidation
61
Brand Type Indexed Pages
Adidas Shoes 150
Palace T-Shirt 15,000
Supreme Bag 10,000
Air Jordan Shoes 50
@haydenroche3 #ReadingSEO

62. Site Consolidation
62
Brand Type Indexed Pages Revenue
Adidas Shoes 150 £750,000
Palace T-Shirt 15,000 £1,000
Supreme Bag 10,000 £300,000
Air Jordan Shoes 50 £125,000
@haydenroche3 #ReadingSEO

63. Site Consolidation
63
Brand Type Indexed Pages Revenue Backlinks
Adidas Shoes 150 £750,000 1,400
Palace T-Shirt 15,000 £1,000 5
Supreme Bag 10,000 £300,000 100
Air Jordan Shoes 50 £125,000 350
@haydenroche3 #ReadingSEO

64. Site Consolidation
64
Brand Type Indexed Pages Revenue Backlinks
Adidas Shoes 150 £750,000 1,400
Palace T-Shirt 15,000 £1,000 5
Supreme Bag 10,000 £300,000 100
Air Jordan Shoes 50 £125,000 350
@haydenroche3 #ReadingSEO

65. Site Consolidation
65
Save and reuse all CASE
statements for the future!
@haydenroche3 #ReadingSEO

66. 66
Technical SEO and BQ insights

67. Let’s Recap 67#ReadingSEO

68. What Have We Learned?
68
● Large CSVs can live in BigQuery, not your desktop
@haydenroche3 #ReadingSEO

69. What Have We Learned?
69
● Large CSVs can live in BigQuery, not your desktop
● The power of the LEFT JOIN
@haydenroche3 #ReadingSEO

70. What Have We Learned?
70
● Large CSVs can live in BigQuery, not your desktop
● The power of the LEFT JOIN
● CASE statements and data groupings
@haydenroche3 #ReadingSEO

71. What Have We Learned?
71
● Large CSVs can live in BigQuery, not your desktop
● The power of the LEFT JOIN
● CASE statements and data groupings
● How to find quick insights from combining data sources
@haydenroche3 #ReadingSEO

72. Additional Resources
72
1. Uploading CSVs to BigQuery →
https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv
2. BigQuery Pricing Scheme →
https://cloud.google.com/bigquery/pricing
3. Set up BigQuery export →
https://support.google.com/analytics/answer/3416092
4. Creating a storage bucket in GCS →
https://cloud.google.com/storage/docs/creating-buckets
@haydenroche3 #ReadingSEO

73. Thank You!
73
Questions/Comments?
➔ @haydenroche3
➔ linkedin.com/in/haydenroche
@haydenroche3 #ReadingSEO