SEO hast changes over the past decade. Understand how classical ranking factors become less important, while user experience dominates the top rankings.
As seen live on stage at @ProjectAcom #PakCon2018 in Berlin.
12. The Future of Search Engine Optimisation
SEO
C P T User Experience
13. Caffeine
Update
June 2010
February 2011
Google Panda
Update
Hummingbird
August 2013
March 2015
RankBrain
Amit Singhal
leaves
February 2016
May 2017
“AI first”
Company
Timeline: How AI entered Google Search
Artificial Intelligence gradually changes how search works
16. Training Machine Learning on User Signals
Refined
Search
Fast & Slow
Clicks
Task
Completion
Quality
Raters &
Guidelines
Browsing &
Supplemental
Data
More Background: https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html
18. The Future of Search Engine Optimisation
SEO
C P T User Experience
19. The Future of Search Engine Optimisation: Implications
SEO
C P T User Experience
Hygiene
Factors
Defining the Top Rankings
Ranking Factors:
Good Proxy
Signals for
Website Quality
• Direct Measurement of User Experience and Satisfaction
• Extensive Testing and Incremental Learning
• Training AI based on Immediate User Feedback
• Data Aggregation, „Reverse Aging“, Even Stronger Network Effects and Moats
20. Implications of an AI-centric Search Environment
User Focus
More than ever: Relentless
Focus on the User
• No compromise on user
experience
• Understanding Intent,
Needs and Objections
• Strong Focus on Mobile
and Speed
• Embrace Voice and
Conversational Search
KPIs & Metrics
How to Measure a Great
Product and User Experience?
• Conversion? (sparse data)
• Bounce Rate? (unreliable)
• Session Time?
• Pages/Session?
• Retention?
• Secondary Metrics:
• Supply, Inventory
• Liquidity
• PageSpeed
Organisation
Challenging the classical “SEO
Team” Approach
• SEO has interfaces with
Dev, Product, PR, Design,
Content/Editorial, etc
• Ideal World: SEO would be
a frontend PM skill
• Install tech. SEO as a PM
• Create independent x-
functional SEO dev team
Tools & Insights
From Ranking-Tracking to
Pattern Recognition
• „Why have I lost rankigns?“
- „Who won and why?“
• Typical SEO Analysis: Data
Aggregation & Pattern
Recognition
• New Tools should identify
patterns and less obvious
correlations automatically
21. The Only SEO Metric I Measure Daily…
What matters most
• Conversion (indexed)
• Impressions/Session
• Time On Site
• Bounce Rate
• Returning Visitors
• Ultimately: Monthly Activity
22. Implications of an AI-centric Search Environment
User Focus
More than ever: Relentless Focus on
the User
• No compromise on user
experience
• Understanding Intent, Needs
and Objections
• No more “SEO text”, useful
content or no text at all
• Strong Focus on Mobile and
Speed
• Paywalls/Signup Layer might
incur high indirect costs
• Embrace Voice and
Conversational Search
• “Panda Diet” – Trimming the
weak sports of your website
KPIs & Metrics
How to Measure a Great
Product and User Experience?
• Conversion? (sparse data)
• Bounce Rate? (unreliable)
• Session Time?
• Pages/Session?
• Retention?
• Secondary Metrics:
• Supply, Inventory
• Liquidity
• PageSpeed
Organisation
Challenging the classical “SEO
Team” Approach
• SEO has interfaces with
Dev, Product, PR, Design,
Content/Editorial, etc
• Ideal World: SEO would be
a frontend PM skill
• Install tech. SEO as a PM
• Create independent x-
functional SEO dev team
Tools & Insights
From Ranking-Tracking to
Pattern Recognition
• „Why have I lost rankigns?“
- „Who won and why?“
• Typical SEO Analysis: Data
Aggregation & Pattern
Recognition
• New Tools should identify
patterns and less obvious
correlations automatically
23. Takeaways: For Your Kind Consideration….
1. Google Search heavily relies on AI already
2. Previous ranking factors are gradually declining in relevance
3. Classical SEO remains to be a hygiene factor instead
4. The best user experience will win position #1 in Google
5. Currently, Time on Site might be the best metric to measure UX
6. PMs have to learn SEO or SEOs have to merge into PMs
7. Equality of arms: Machines are also better at analysing ranking changes
26. Theory: Typical Page Quality (Qp) over Number of Pages (np)
np
Qp
Homepage
Category
Category+Brand
Facetted Search
Thin Catalogue (low inventory)
Dupe Content page „no results“ page
highestlowestmediorceuseful
400.000200.000 300.000100.000
Page Quality (Qp) can
be defined as content
richness, engagement,
ultimateley how useful
the page is to the user.
But also its revenue
potential.
PROBLEM:
Since Panda (2011) this
structure has become toxic.
28. Theory: Typical Page Quality (Qp) over Number of Pages (np)
np
Qp
highestlowestmediorceuseful
400.000200.000 300.000100.000
Average Quality
😞
Quality Threshold 80% (mediocre and better)
NOINDEX
(320.000)
INDEX
(80.000)
New Average Quality
QTYINCREASE
Target = 80%
Quality Threshold
RANKINGS
Page Quality (Qp) can
be defined as content
richness, engagement,
ultimateley how useful
the page is to the user.
But also its revenue
potential.