RANKING FACTORS
RELOADED –WHY
CONTENT ISYOUR
KEYTO SUCCESS
MARCUS
TOBER
BRIGHTON, ENGLAND
APRIL 22TH, 2016

Searchmetrics
Made with love in Berlin
More than 220 passionate people
Innovator in SEO Software since
2005

Marcus Tober
Founder and CTO of
Searchmetrics
In love with SEO and SEARCH
since 2001
Study of computer science
In Berlin, so I´m the Techie!

Our Secret Sauce is our Software
The Searchmetrics Suite
Search Experience Optimization
Makes Digital Marketing Better,
Faster and More Profitable.

25.000.000.000.000
SOCIAL SIGNALS
120 MILLION
DOMAINS
120 BILLION
LINKS600 MILLION
KEYWORDS
SoA
Hadoop, Hbase.
Elastic Search
API
25 PB
Parstream

Download slides:
searchmetrics.com/BrightonSEO

Why do
traditional SEO
Ranking Factors
fail?

#1
Google‘s intelligence

1 Keyword = 1 Landing Page
Bad SEO
+ SEO-Checklist
KW in title
KW in description
KW in heading
KW density
Linkbuilding
Content in footer
Etc.
✓
✓
✓
✓
✓
✓
✓

1 Topic = 1 Landing Page
Good SEO
+ Content-Checklist
Content Quality
User Intention
Content Experience
Etc.
✓
✓
✓
✓

Movie where they make fun of Star Wars
Keyword in Title?
Example: “Movie where they make fun of StarWars”

Keyword in Title?
Example: “Movie with 4 guys in LasVegas”

Movie where they make fun of Star Wars
Keyword in Title?
Example: “As a cafe owner how can seo help”

The future of Google

Machine Learning
(ML) ≠ AI

Machine Learning
(ML)
Deep Learning Artificial Intelligence
An algorithm that
improves over time
Aims to bridge the gap
between ML and AI –
solves more complex
problems
Human like
intelligence
Spectrum of Intelligence

• More than 2,500 years old
• Played by 40m worldwide
• Deemed uncrackable by Machine
Learning/AI (until AlphaGo)
Go – The limits of machine learning

Is Go really that hard?
*Hint: The little number above the 10 is how many 0s you add on the end
(well actually, yes…)
1050 4x1079 – 4x1081
10171
Number of...
in a chess game in the observable universe in game of Go
…possible moves …possible moves…atoms

Not so simple
That’s…
1,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,000,000,00
0,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,000,000,00
0,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000
…possible positions!

Deep learning – AlphaGo vs Human
1HumanAlpha Go
4 -
End result:
5 sets between March 9th and March 15th 2016

• ML and deep learning solve problems that
cannot be solved by numerical means alone.
MACHINE LEARNING
- FAST FACTS -
• Deep learning operates on multiple levels to
solve more complex problems.
• Google´s recent success in cracking Go is a
major step for ML/AI research

Why do
traditional SEO
Ranking Factors
fail?

#2
Different industries,
different Ranking Factors

Analysing
industry by industry.
With data-driven insights.

Backlinks RANKING FACTORS
2016

0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce
Referring Domains
T10 Ø: 272736
Correlation: 0.20
RANKING FACTORS
2016

0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health
T10 Ø: 272736 99526
Correlation: 0.20 0.21
Referring Domains RANKING FACTORS
2016

0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health Finance
T10 Ø: 272736 99526 38728
Correlation: 0.20 0.21 -0.02
Referring Domains RANKING FACTORS
2016

0
20,000,000
40,000,000
60,000,000
80,000,000
100,000,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health Finance
Total Number of Backlinks
T10 Ø: 69.6 M 24.8 M 9.8 M
Correlation: 0.14 0.15 -0.03
RANKING FACTORS
2016

0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health Finance
Referring Domains RANKING FACTORS
2016
T10 Ø: 30.963 12.394 4823
Correlation: 0.28 0.23 0.01

0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health Finance
Number of Backlinks
T10 Ø: 2.086.520 802.963 277.228
Correlation: 0.16 0.19 -0.01
RANKING FACTORS
2016

Will Google destroy the
relevance of backlinks for
rankings in the future?

Cash advance fresno ca
Backlinks & Internal Links
Rank: 5 Rank: 12
17 Referring Domains
21 internal links
52,000 Referring Domains
443 internal links
Example: “Cash advance fresno ca”

fast cash credit card
Rank: 6 Rank: 17
5,685 Referring Domains
No KW inTitle
59,780 Referring Domains
KW inTitle
Backlinks & Keyword in Title
Example: “Fast cash credit card”

Keyword in Title
RANKING FACTORS
2016

0%
10%
20%
30%
40%
50%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health
Keyword in Title
T10 Ø: 33% 35%
Correlation: -0.04 -0.05
RANKING FACTORS
2016

0%
10%
20%
30%
40%
50%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health Finance
Keyword in Title
T10 Ø: 33% 35% 10%
Correlation: -0.04 -0.05 0.04
RANKING FACTORS
2016

Word Count
RANKING FACTORS
2016

1,000
1,500
2,000
2,500
3,000
3,500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Finance
T10 Ø: 1575
Correlation: 0.09
Word Count
RANKING FACTORS
2016

1,000
1,500
2,000
2,500
3,000
3,500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
eCommerce Health Finance
T10 Ø: 3069 2826 1575
Correlation: 0.13 0.15 0.09
Word Count
RANKING FACTORS
2016

natural detox
Rank: 5 Rank 26
Word count = 3180
Internal links count = 375
Interactive elements = 398
Word count = 6087
Internal links count = 395
Interactive elements = 625
Word count and Interactive lements
Example: “Natural detox”

Being Relevant
Relevant content:
• matches user intention
• is logically structured & comprehensive
• offers a good user experience
• deals with topics holistically

Why do
traditional SEO
Ranking Factors
fail?

#3
Mobile Customer Journey

Paid
Search
DirectOrganic
Search
SocialEmail
ShoppingFinance
More often the
Last Interaction
More often an
Assist Interaction
Beauty & Fitness
Search in the Customer Journey
Source: https://www.thinkwithgoogle.com/tools/customer-journey-to-online-purchase.html

Mobile Search in the Customer Journey
54%
use their smartphone to
research or purchase products
68%
…use mobile
apps
Usage of Smartphones when researching for a retail product
53%
use a mobile
browser, but…
Source: https://www.thinkwithgoogle.com/tools/mobile-purchase-journey.html

Mobile Search Query?
A: „Calendar“
B: „Nfl Schedules“

Maps
Cool Math
UnblockedGames
Solitaire
Calendar
Mail
Office 365
Nfl Scores
Nfl Schedules
Restaurants near me
Nba Scores
Pill identifier
Mlb Scores
School Closings
Keyword Device Spread
Ratio > 70 %

April 2015Today
71%80%
77.1% 78.5% 84.6%
Mobile Friendliness

Dating
10 organic results
5 Ads
4 Images
3 News
Translation Widget
Desktop vs Mobile
Example: “Dating”

4 Ads
6 Apps
3 News
4 Images
2 Organic results!
Example: “Dating”
Desktop vs Mobile

Mp3 Download
Desktop vs Mobile: App Integrations

10%
15%
20%
25%
30%
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Responsive Framework RANKING FACTORS
2016
Ø Top10 Desktop: 20.2%
Ø Top10 Mobile : 16.8%
𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = −0.04
𝜌 𝑀𝑜𝑏𝑖𝑙𝑒 = −0.04

60
70
80
90
100
110
120
130
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Ø Top10 Desktop: 117
Ø Top10 Mobile : 83
Internal link count RANKING FACTORS
2016
𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = 0.09
𝜌 𝑀𝑜𝑏𝑖𝑙𝑒 = 0.05

30%
35%
40%
45%
50%
55%
60%
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Ø Top10 Desktop: 46%
Ø Top10 Mobile : 36%
𝜌_( 𝑀𝑜𝑏𝑖𝑙𝑒)=0.01
Embedded Videos RANKING FACTORS
2016
𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = 0.04

4
5
6
7
8
9
10
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Ø Top10 Desktop: 7.6
Ø Top10 Mobile : 6.2
Bullets in lists RANKING FACTORS
2016
𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = 0.09
𝜌 𝑀𝑜𝑏𝑖𝑙𝑒 = 0.06

1,500
2,000
2,500
3,000
3,500
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Word count RANKING FACTORS
2016
Ø Top10 Desktop: 2926
Ø Top10 Mobile : 2005
𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = 0.11
𝜌 𝑀𝑜𝑏𝑖𝑙𝑒 = 0.05

“The User Intention you must
think about…”

Search Query & Expectations
???

#1 #14
Example: “costume ideas”
Search Query & Expectations

http://www.popsugar.com/smart-living/Cheap-
Halloween-Group-Costumes-25300043#photo-
25300043
Rank #1
60 part image gallery
Short description
Links to other relevant pages
High Social Engagement
User Intention = Inspiration!
✓
✓
✓
✓
Example: “costume ideas”
Search Query & Expectations

https://www.savers.com/halloween/costumes/all
Rank #14
14 costume suggestions
No product texts
General footer text
User Intention ≠ Shopping!
✗
✗
✗
Example: “costume ideas”
Search Query & Expectations

13
14
14
15
15
16
16
17
17
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Font Size above the fold RANKING FACTORS
2016
Ø Top10 Desktop: 14.6
Ø Top10 Mobile : 16.0
𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = −0.07
𝜌 𝑀𝑜𝑏𝑖𝑙𝑒 = 0.01

0
1
2
3
4
5
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Number of Images RANKING FACTORS
2016
Ø Top10 Desktop: 2.6
Ø Top10 Mobile : 0.8𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = −0.01
𝜌 𝑀𝑜𝑏𝑖𝑙𝑒 = −0.04

0
2
4
6
8
10
1 3 5 7 9 11 13 15 17 19
Desktop Mobile
Number of total lists RANKING FACTORS
2016
Ø Top10 Desktop: 6.8
Ø Top10 Mobile : 2.7
𝜌 𝐷𝑒𝑠𝑘𝑡𝑜𝑝 = 0.15
𝜌 𝑀𝑜𝑏𝑖𝑙𝑒 = 0.05

10.9
%
3.0% 10.3
%
App Integrations
Share of Keywords with App Integration

App Integrations
Searchmetrics Suite / Mobile App Rankings

New Ranking Factors 2016

New Ranking Factors 2016
• Relevance Features with a calculated
relevance score.
• Specific Ranking Factors for different
industries.
• Available for multiple countries.

But…
how to stay relevant?

“The content relevance
you must think about…”

mortgage
#2
#10
#2
Holistic data-driven Approach
Searchmetrics Suite: Content Optimization

Calculator
Mortgage comparison
Relevant information
Help section
✓
✓
✓
✓
Holistic data-driven Approach

Calculator
Mortgage comparison
Relevant information
Help section
✗
✗
✗
✓
Holistic data-driven Approach

2009 2012 TODAY
Content Updates – Example bankrate.com

Holistic data-driven Approach
Searchmetrics Suite: Content Performance

Data-driven content analysis
Relevant content to fulfil the user‘s intention
Success in search & all other channels

Industry Standard for Understanding Content Performance
Longest running record of Google™ data
Largest
Data Set
Interpret Customer
Intent
Appl
e
Watc
h
Historical Data
Most inputs.
More that just
SEO
Only platform with
transactional search data

MAY THE CONTENT
BE WITH YOU!
Download slides:
searchmetrics.com/BrightonSEO