We love seo 2019 - Tom Capper
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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.
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We love seo 2019 - Tom Capper
- 1. Ranking for Head Terms & the limits of ranking factors @THCapper / @Distilled
- 3. What are your views on Brexit? Douvres, Brexit, par Banksy (2017) by Paul Bissegger is licensed under CC BY-SA 4.0
- 4. Why aren’t we first for {head term} ?!
- 7. @THCapper I was missing something @THCapper
- 11. PageRank & links were a proxy for user behaviour @THCapper
- 12. Google doesn’t need a proxy anymore @THCapper
- 13. Google is a browser @THCapper
- 14. Google is an ISP @THCapper
- 15. Google is, of course, a dominant search engine @THCapper
- 16. & links have become a dirty signal @THCapper
- 17. (Rand said) Build links that might genuinely drive high quality traffic @THCapper
- 18. @THCapper My conclusion in 2017 @THCapper
- 19. @THCapper
- 20. @THCapper (Some of you will have noticed those were small R-squareds…) @THCapper
- 21. @THCapper Content piece gains 191 referring domains
- 23. @THCapper I had part of the answer @THCapper
- 24. At the competitive, data-rich top end, links mean increasingly little Me, February 2017
- 25. @THCapper@THCapper Strength of correlation: Domain Authority & Rankings February 2017 October 2018 0.07 0.02
- 26. SEO in 2018 is winning at user testing, brand awareness, and perception Me, February 2017
- 27. @THCapper I was still wrong @THCapper
- 28. @THCapper What if: Head terms are no longer about ranking factors ? @THCapper
- 33. Back to basics: What do I mean by ranking factors?
- 34. @THCapper Google is changing (it’s a cliché) @THCapper
- 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
- 38. @THCapper Ranking factors: Why is that site outranking me? Basic technical
- 39. @THCapper Ranking factors: Why is that site outranking me? Basic technical Targeting
- 40. @THCapper Ranking factors: Why is that site outranking me? Basic technical Links Targeting
- 41. @THCapper Ranking factors: Why is that site outranking me? Basic technical Links Targeting Hygiene factors
- 42. @THCapper 2004 Reasonable surfer 2011 Panda 2012 Penguin 2014 Pigeon 2015 Mobilegeddon @THCapper
- 43. @THCapper Building on the old model @THCapper
- 49. I’m a frog & Google is a saucepan
- 50. @THCapper We are asking the wrong questions Basic technical Links Targeting Hygiene factors
- 51. Evidence
- 54. @THCapper Strength of correlation: Domain Authority & Rankings May 2015 (Moz) February 2017 (Me) 0.07 0.26
- 55. @THCapper Moz Study My Study 17,600 queries from KWP 4,900 queries from STAT
- 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
- 61. @THCapper Links are less important in the top 10? @THCapper
- 62. @THCapper Hypothesis: The higher you rank, the less “ranking factors” matter @THCapper
- 63. @THCapper I need a fairer comparison @THCapper
- 64. @THCapper Prediction: We’ll see a weaker LRD & ranking correlation in positions 1-5 @THCapper
- 68. @THCapper@THCapper PSA: 1. Position 5 is an arbitrary cut-off 2. You still need traditional factors to get there
- 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/
- 71. @THCapper@THCapperhttps://www.stonetemple.co m/link-as-a-ranking-factor/ Top 50 vs. Top 10 0.33 0.29 DA of linking URLS Links 0.32 PA of linking URLS 0.06 0.11 DABranded Search Volume 0.05 LRDs
- 72. @THCapper Hypothesis: The higher you rank, the less “ranking factors” matter @THCapper
- 73. @THCapper Hypothesis: The higher you rank, the less “ranking factors” matter @THCapper
- 74. @THCapper Caveat: We only looked at one factor @THCapper
- 75. @THCapper Caveat: We only looked at one factor @THCapper (& one potential factor)
- 76. @THCapper@THCapper Caveat: We lack a control; There could be joint-correlations
- 78. @THCapper Theory: Head terms are no longer about ranking factors @THCapper
- 79. @THCapper@THCapper Evidence: 1. Links less relevant in the top 5
- 81. @THCapper Rankings 2 weeks before Mother’s Day Site 1 Interflora 2 Flying Flowers 3 M&S 4 Serenata 5 NextFlowers 6 eFlorist 7 & 8 WaitroseFlorist 9 Appleyard
- 83. @THCapper Lesser known, online-only brands
- 84. @THCapper Fairly well recognized, primarily online
- 85. @THCapper Rankings 2 weeks before Mother’s Day Site Specialist? 1 Interflora Yes 2 Flying Flowers Yes 3 M&S No 4 Serenata Yes 5 NextFlowers Ish 6 eFlorist Yes 7 & 8 WaitroseFlorist Ish 9 Appleyard Yes
- 86. @THCapper Rankings 2 weeks before Mother’s Day Site Specialist? DA 1 Interflora Yes 53 2 Flying Flowers Yes 46 3 M&S No 82 4 Serenata Yes 56 5 NextFlowers Ish 40 6 eFlorist Yes 44 7 & 8 WaitroseFlorist Ish 42 9 Appleyard Yes 44
- 87. @THCapper Site Specialist? DA BSV 1 Interflora Yes 53 212,000 2 Flying Flowers Yes 46 24,000 3 M&S No 82 3,060,000 4 Serenata Yes 56 5,000 5 NextFlowers Ish 40 4,850,000 6 eFlorist Yes 44 16,000 7 & 8 WaitroseFlorist Ish 42 924,000 9 Appleyard Yes 44 1,700 Rankings 2 weeks before Mother’s Day
- 88. @THCapper Site Specialist? DA BSV 1 Interflora Yes 53 212,000 2 Flying Flowers Yes 46 24,000 3 M&S No 82 3,060,000 4 Serenata Yes 56 5,000 5 NextFlowers Ish 40 4,850,000 6 eFlorist Yes 44 16,000 7 & 8 WaitroseFlorist Ish 42 924,000 9 Appleyard Yes 44 1,700 Rankings 2 weeks before Mother’s Day
- 89. @THCapper Nice rankings... shame if something happened to them. @THCapper
- 90. @THCapper Keyword: Mother’s day flowers
- 91. @THCapper Keyword: Mother’s day flowers
- 92. @THCapper Keyword: Mother’s day flowers
- 93. @THCapper Keyword: Mother’s day flowers
- 94. @THCapper Keyword: Mother’s day flowers
- 95. @THCapper Keyword: Mother’s day flowers
- 96. @THCapper Keyword: Mother’s day flowers
- 97. @THCapper Keyword: Mother’s day flowers
- 98. @THCapper Keyword: Mother’s day flowers
- 99. @THCapper Keyword: Mother’s day flowers
- 100. @THCapper Keyword: Mother’s day flowers
- 101. @THCapper Keyword: Mother’s day flowers
- 102. @THCapper Keyword: Mother’s day flowers
- 103. @THCapper Keyword: Mother’s day flowers
- 104. @THCapper Keyword: Mother’s day flowers
- 105. @THCapper Keyword: Mother’s day flowers
- 106. @THCapper Keyword: Mother’s day flowers Offline brands: Doing pretty well
- 107. @THCapper Keyword: Mother’s day flowers Offline brands: Doing pretty well
- 108. @THCapper Eflorist & Serenata: Strong domains you never heard of, the “SEO” winners Keyword: Mother’s day flowers
- 109. @THCapper Why is that site outranking me? Basic technical Links Targeting Hygiene factors
- 110. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on Keyword: Mother’s day flowers
- 111. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on
- 112. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on
- 113. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on
- 114. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on
- 115. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on
- 116. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on
- 117. @THCapper Flying Flowers & Appleyard Small brands, small domains, clung on
- 118. @THCapper Flying Flowers Simple, price-orientated, emphasis on free delivery
- 119. @THCapper Site DA BSV Specialist? Change WaitroseFlorist 42 924,000 Ish +7 M&S 82 3,060,000 No +4 Winners
- 120. @THCapper Losers Site DA BSV Specialist? Change eFlorist 44 16,000 Yes -5 Serenata 56 5,000 Yes -16
- 121. @THCapper Hangers on Site DA BSV Specialist? Change Interflora 53 212,000 Yes -2 Flying Flowers 46 24,000 Yes -2 NextFlowers 40 4,850,000 Ish 0 Appleyard 44 1,700 Yes 0
- 122. @THCapper@THCapper Evidence: 1. Links less relevant in the top 5 2. SERPs change when they become high volume
- 123. @THCapper Money where my mouth is @THCapper
- 124. @THCapper Valentine’s Day 2019 strategy 1. Free delivery 2. Same day / free delivery focus in meta data 3. Push cheaper, postable products
- 125. @THCapper Valentine’s Day results: Top keywords Keyword Change in 14-day run-up Flower delivery +2 Flowers +2 Spring flowers - Valentine’s card messages +2 Flower delivery UK +2 Send flowers UK - Roses +1
- 126. @THCapper Mother’s Day 2019 pushback 1. No more free delivery 2. No more letterbox flowers 3. Otherwise identical
- 127. @THCapper Mother’s Day 2019 results: Top keywords Keyword Change in 14-day run-up Flower delivery -2 Send flowers UK +1 Flower delivery UK -4 Flowers -5 Send flowers -3 Order flowers online -2 Flowers UK -1
- 128. @THCapper@THCapper Theory: Head terms are no longer about ranking factors
- 129. @THCapper@THCapper Evidence: 1. Links less relevant in the top 5 2. SERPs change when they become high volume 3. Pricing affects rankings
- 131. @THCapper Medic update: Winners Site Visibility NHS +4.6% WebMB +6.5% Healthline +3.5% Bupa +4.9% Cosmopolitan +4.6% Elle +3.6%
- 132. @THCapper Medic update: Winners Site Visibility NHS +4.6% WebMB +6.5% Healthline +3.5% Bupa +4.9% Cosmopolitan +4.6% Elle +3.6%
- 133. @THCapper Medic update: Winners Site Visibility NHS +4.6% WebMB +6.5% Healthline +3.5% Bupa +4.9% Cosmopolitan +4.6% Elle +3.6%
- 134. @THCapper Medic update: Winners Site Visibility NHS +4.6% WebMB +6.5% Healthline +3.5% Bupa +4.9% Cosmopolitan +4.6% Elle +3.6%
- 136. Revenue impact?
- 137. @THCapper
- 138. @THCapper
- 140. @THCapper@THCapper Theory: Head terms are no longer about ranking factors
- 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
- 142. Explanation
- 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
- 146. @THCapper@THCapper KPI 1: Pogo-stick rate
- 147. @THCapper PSA: Not the same as bounce rate @THCapper
- 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
- 149. @THCapper@THCapper KPI 1: KPI 2: Pogo-stick rate Time to SERP interaction
- 150. @THCapper@THCapper Do links get you this? KPI 1: KPI 2: Pogo-stick rate Time to SERP interaction
- 152. @THCapper@THCapper
- 153. @THCapper Reminder: Links are a proxy for popularity & trust @THCapper
- 154. @THCapper Not traditional ranking factors: - Price - Aesthetics - Lack of pop-ups @THCapper
- 155. What does this mean for your content?
- 156. The fundamentals still work
- 161. @THCapper@THCapper KPI 1: KPI 2: Pogo-stick rate Time to SERP interaction
- 168. @THCapper@THCapper KPI 1: KPI 2: Pogo-stick rate Time to SERP interaction
- 170. @THCapper MetaData
- 172. @THCapper Brand
- 173. @THCapper “But I can’t do anything about brand” @THCapper
- 174. @THCapper@THCapper
- 175. @THCapper
- 176. @THCapper How can I measure this? ● Branded search ● Social following ● Survey data https://www.distilled.net/resources/measuring-brand-awareness/
- 179. @THCapper@THCapper
- 180. What does this mean for PR?
- 181. @THCapper Strategic approach: ● Focus on links where you’re not in the running ● Focus on awareness where you are @THCapper
- 182. @THCapper When do links matter least? @THCapper
- 183. @THCapper When do links matter least? ● Your page has different intent to those above it @THCapper
- 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
- 187. @THCapper
- 188. @THCapper What else matters? ● Favourable brand impressions ● Traffic driven through placements ● Topical (e.g. country-specific) relevance @THCapper
- 189. What does this mean for measurement?
- 191. Engagement Metrics ● Avg. Session Duration ● Avg. Time on Page ● Bounce Rate ● Pages/Session ● Exit Rate ● Social Interactions ● Scroll Depth ● Micro-Conversions (e.g. signup) @THCapper
- 192. Define Average Time on Page @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
- 207. Engagement Metrics ● Avg. Session Duration ● Avg. Time on Page ● Bounce Rate ● Pages/Session ● Exit Rate ● Social Interactions ● Scroll Depth ● Micro-Conversions (e.g. signup) @THCapper
- 208. Engagement Metrics ● Avg. Session Duration ● Avg. Time on Page ● Bounce Rate ● Pages/Session ● Exit Rate ● Social Interactions ● Scroll Depth ● Micro-Conversions (e.g. signup) @THCapper
- 209. Final Takeaways
- 210. @THCapper@THCapper Takeaways: 1. Traditional ranking factors are not enough 2. Inherit Google’s KPIs 3. But don’t abandon the foundations just yet
- 211. Thank You