The document discusses how ranking factors may be less important determinants of search engine results page (SERP) position for highly competitive keywords or "head terms". Evidence from analyses of query data and case studies of flower delivery companies around Valentine's Day and Mother's Day suggest that links and other traditional ranking factors correlate less with top SERP positions, and that SERP composition changes more in response to query volume and user intent/behavior factors than technical SEO. The author develops a theory that head terms may be determined less by explicit ranking factors and more by meeting user needs and intent.
35. Google in 2017:
31,584 experiments
2,453 search changes
https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html
56. @THCapper
Moz Study My Study
17,600 queries from KWP 4,900 queries from STAT
Top 50 results Top 10 results
57. @THCapper
Moz Study My Study
17,600 queries from KWP 4,900 queries from STAT
Top 50 results Top 10 results
Desktop only (?) Desktop & Smartphone
58. @THCapper
Moz Study My Study
17,600 queries from KWP 4,900 queries from STAT
Top 50 results Top 10 results
Desktop only (?) Desktop & Smartphone
May 2015 Feb 2017, Oct 2018, May 2019
59. @THCapper
Moz Study My Study
17,600 queries from KWP 4,900 queries from STAT
Top 50 results Top 10 results
Desktop only (?) Desktop & Smartphone
May 2015 Feb 2017, Oct 2018, May 2019
Mean Spearman correlations Mean Spearman correlations
60. @THCapper
Moz Study My Study
17,600 queries from KWP 4,900 queries from STAT
Top 50 results Top 10 results
Desktop only (?) Desktop & Smartphone
May 2015 Feb 2017, Oct 2018, May 2019
Mean Spearman correlations Mean Spearman correlations
70. @THCapper@THCapper
Top 50 vs. Top 10
0.33
0.29
DA of
linking
URLS
Links
0.32
PA of
linking
URLS
https://www.stonetemple.co
m/link-as-a-ranking-factor/
141. @THCapper@THCapper
Evidence:
1. Links less relevant in the top 5
2. SERPs change when they become
high volume
3. Pricing affects rankings
4. Google is re-assessing intents for
established keywords
145. “what percent of users clicked
through a picture-link and then
quickly clicked back”
https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html
148. “whether there was a significant
increase in the time until they made
their first interaction”
https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html
176. @THCapper
How can I measure this?
● Branded search
● Social following
● Survey data
https://www.distilled.net/resources/measuring-brand-awareness/
186. @THCapper
When do links matter least?
● Your page has different intent to
those above it
● Your page is already fluctuating
among top spots
@THCapper
193. GA: “The average amount of time users spent viewing a
specified page or screen, or set of pages or screens.”
Define Average Time on Page
@THCapper
194. GA: “The average amount of time users spent viewing a
specified page or screen, or set of pages or screens.”
Define Average Time on Page
@THCapper
195. “Average Time on Page” =
(“Time on Page”) / (Pageviews - Exits)
Define Average Time on Page
@THCapper
196. Before we calculate an average, we need the individual times:
Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s
@THCapper
197. Before we calculate an average, we need the individual times:
Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
@THCapper
198. Before we calculate an average, we need the individual times:
Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s
@THCapper
199. Before we calculate an average, we need the individual times:
Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s 10s
@THCapper
200. Before we calculate an average, we need the individual times:
Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s 10s
0s: Pageview
20s: Leave site
20s
@THCapper
201. Before we calculate an average, we need the individual times:
Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s 10s
0s: Pageview
20s: Leave site
20s 0s
@THCapper
202. Average Time on Page?
Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s 10s
0s: Pageview
20s: Leave site
20s 0s
203. Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s 10s
0s: Pageview
20s: Leave site
20s 0s
“Average Time on Page”= (“Time on Page”) / (Pageviews - Exits)
204. Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s 10s
0s: Pageview
20s: Leave site
20s 0s
“Average Time on Page”= (Total of 30s) / (3 Pageviews - 2 exits)
205. Scenario Intuitive Time on
Page
Google Analytics
Time on Page
0s: Pageview
10s: Social plugin
20s: Click through to next page
20s 20s
0s: Pageview
10s: Social plugin
20s: Leave site
20s 10s
0s: Pageview
20s: Leave site
20s 0s
“Average Time on Page”= 30 /1 = 30 seconds
206. ● Confusing prospective consultants in interviews
● Confusing conference attendees
● Generating random numbers
Usefulness of Average Time on Page
@THCapper