big data graph clustering with laurence o'toole - digital marketing show, november 2015

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@analyticsseo #bigdatascience Big Data Science for Content Marketing Success [email protected] www.analyticsseo.com +44 208 977 4465 Keyword Clustering: How Big Data is taking the guesswork out of Digital Content Publishing Strategy

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Page 1: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

@analyticsseo #bigdatascience

Big Data Science for Content Marketing Success

[email protected] www.analyticsseo.com +44 208 977 4465

Keyword Clustering: How Big Data is taking the guesswork out of Digital Content Publishing Strategy

Page 2: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

The Web is not a random network

http://www.amazon.co.uk/Linked-Albert-laszlo-Barabasi/dp/0465085733

Page 3: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Mapping the Internet

http://internet-map.net/#9-158.57192434422797-63.9119835263755http://www.wired.com/2015/06/mapping-the-internet

Page 4: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Power Laws

• Pareto Principle – 80:20 - 80% of your market is dominated by 20% of websites• The ‘Long Tail’ of search• Characterised by a small number of large items, and a large number

of small items• Why is it like this? - Preferential Attachment – “Rich get Richer”

https://en.wikipedia.org/wiki/Power_lawhttp://www.thelongtail.com/about.html

Page 5: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

The Long Tail – Websites – Users & Visitors

http://www.hpl.hp.com/research/idl/papers/ranking/ranking.html

Page 6: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Pareto Effect – In Effect in Most Markets

Page 7: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

So What? How does this apply to Content Marketing

• The Long Tail theory argues that there is demand for more and more specific niches• Google and other search engines are trying to add structure to

unstructured data and make sense of your market• Analysis of the web for your market can show the long tail at work

• Large players dominating but also aggregating niches• Niche players building their businesses off the back of quality content satisfying the

peculiar needs of the many

• Understanding this can be the strategy to success in all markets• Helping you analyse niches of relative strength vs the competition• Supporting you to build a content marketing strategy based on Google’s view of the

world

Page 8: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Knowledge Graph Results

Direct Answers

• 200+ Ranking Signals/Factors (each having up to 50 variants)• 10,000+ Ranking Signals http://searchengineland.com/seotable/ • Rankbrain algorithm – Artificial Intelligence• It’s about becoming an Authority in an area – See Google’s

Natural Language Search Results for Intent Queries - they selected authoritative pages which:• Were frequently selected in search results• Consistently rank high in search results for related topics

• If you publish quality content giving good answers covering a natural cluster then you will do well• Think EAT, Users’ Needs and Mobile

Where’s Google Going?

What’s the difference between a Ranking Factor and a Ranking Signal?http://searchengineland.com/close-smx-west-growth-direct-answers-seos-react-216009

Page 10: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

What is Graph Clustering?

Page 11: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

6 Degrees of Separation

http://barokas.com/2014/11/10-reasons-give-thanks-pr/http://www.dailymail.co.uk/sciencetech/article-2064746/Facebook-shrinks-degrees-separation-just-FOUR.html

Page 12: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Graph AnalysisActor

Actor

Actor

Actor

Actor

Movie

Movie

Movie

Movie

Actor

Actor

Rod Steiger

Martin SheenCharlie Sheen

Julia Roberts

Clint Eastwood

Kevin Bacon

Louis Anderson

Truth or Consequences

JFK

Ferris Bueller’s Day Off

Movie

Quicksilver

Mystic River

Movie

Flatliners

Actor

Kiefer Sutherland

Movie

A Few Good Men

Actor

William BaldwinSo you calculate how connected an actor is – or their ‘Bacon number’.You can also calculate how ‘central’ an actor is. E.g. Eric Roberts.

http://oracleofbacon.org

Page 13: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Big Data - Graph Analysis for Content Marketing

It’s like Venn Diagrams on Steroids!

Page 14: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Graph Clustering shows Google’s algorithms at workGraph data example:

Natural clusters

Green = URLsBrown = KWsBlue = Domains

Imagine if you could get this view of your market?

Page 15: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Market Analysis

• What does your market look like online? You might analyse..• Yourself• Your direct competitors• Consumer behaviour• Trends in consumer demand

• Or more importantly all of this and …… how Google’s algorithm works in your market?

Page 16: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

How companies typically do market analysis….

A B C D

A

B

C

D

Page 17: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

An old friend…The Venn Diagram

A BA

BC

A

B

C

D

A

B

C

D

E

F

G

H

I

J

K

L

MNO

P

RS

T

UV

W

Y

X

Z

You need a method for comparing A against everyone, then B against everyone and so on….

B

Page 18: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Even Venn diagrams have their limitations!

A

B

C

D A B

A

BC

A

B

C

D

E

F

G

H

I

J

K

L

M NO

P

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T

UV

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X

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BA B

A

BC

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B

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D

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M NO

P

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UV

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A B

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BC

A

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M N

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UV

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A B

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BC

A

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LM N

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UV

YZ

A B

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BC

A

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A B

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BC

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F

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L

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P

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UV

Y Z

Page 19: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

How companies should do market analysis….A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH AI AJ AK AL AMAN AO AP AQ AR AS AT AU AVAW AX AY

ABCDEFGHIJKL

MNOPQRSTUVWXYZ

AA

Page 20: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Gain unparalleled insights with Graph Analysis and Clustering

You simply need:• A large database of keywords• Your ranking position for each keyword• Demand for those keywords (Search Volume) and £value of each keyword• A way to measure your strength vs competition• Graph analysis database and tools• Method for clustering results (Latent Semantic Indexing & Graph Structure)• Method for visualising or distilling the results into Excel or PowerBI• Time, money and some technical skills

Page 21: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

RD

P

P

P

KW

KW

KW

KW

KW

CP

CP

KW

KW

KW

The competition!

KW

KW

CP

CDYou

Opportunity!

CD = Competing domainsCP = Competitors’ pages

RD = Ranking domainP = Your pageKW = Keyword

How to ‘graph’ your market

Page 22: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Graph Clustering - Structure & SemanticsWhat we do: Data comprising groups of keywords and associated ranking pages that we obtain sheds light on Google’s view of the relationship between the structure of the web and search intent.

Data & Segmentation: Based on this, we can model related keywords and ranking URLs; then segment the data into groups revealing natural clusters of search topics.

Unique insight: Determining maximum gain that could be derived; a recommended course of action for each these groups, provides actionable insight. Natural clusters

Graph data example: Green = URLsBrown = KWsBlue = Domains

Page 23: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

What is Keyword Clustering?• It’s about more than keywords• It’s about the structure of the web – or more exactly how Google interprets the

structure of the web to present its search results• It’s about how the SERPs fall into natural clusters• It’s about labelling those clusters semantically• It’s about finding relevant clusters with high demand where you are competing

against weaker competitors for short-term wins• It’s about defining a ‘real-time’ strategy that shows the algorithm at work in your

market so you can plan short-term and long-term growth• It’s about becoming an Authority in a cluster • If you publish quality content giving good answers covering a natural cluster then

you have a chance to become an Authority

https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2014197227

Page 24: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Everyone here is already clustering!

• Keyword research into keyword groups• Grouping URLs• Grouping competitors or other websites (blogs, affiliates, partners,

media, social networks, etc)• Everyone here sees their market through their lens – if you were

sitting in your closest competitor’s boardroom there would be lots of similarities but you would see things slightly differently• The common denominator is Google who aggregates all the content

and all the links and clusters the web into its different niches

Page 25: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – 1 Keyword, 1 Page of SERPs, Un-tuned Algorithm

http://search.carrotsearch.com/carrot2-webapp/search

Page 26: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – 1 Keyword, 1 Page of SERPs, Un-tuned Algorithm

Page 27: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – 1 Keyword, 1 Page of SERPs, Un-tuned Algorithm

Page 28: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – 1 Keyword, 1 Page of SERPs, Un-tuned Algorithm

Page 29: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – 1 Keyword, 1 Page of SERPs, Un-tuned Algorithm

Page 30: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – 1 Keyword, 1 Page of SERPs, Un-tuned Algorithm

Page 31: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – Stage 2

http://app.raw.densitydesign.org

Page 32: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – Stage 2

http://app.raw.densitydesign.org

Page 33: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – Stage 2

http://app.raw.densitydesign.org

Page 34: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

DIY Clustering – Stage 2

http://app.raw.densitydesign.org

Page 35: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Now imagine you could do this with…

• Millions of keywords• Millions of pages from the SERPs• Millions of websites• Using a Graph Database• With a tuned algorithm - Using the structure of the web and semantic

algorithms to refine the model

Page 36: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Using this tech and data you can really understand your market

We can filter the data to:1) Identify core niche competitors2) Market Dynamics – Size and Concentration & Growth3) Analyse the performance of existing keywords & pages4) Suggest new keywords for existing pages5) Suggest new keywords for new pages

Commercial & in-confidencewww.analyticsseo.com

Page 37: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

GreatBritishChefs.com case study

Taking the guesswork out of your content marketing efforts

Page 38: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

GreatBritishChefs.com’s Market

Mar

ket V

isibi

lity

Shar

e%

The initial ‘net casting’ shows that the top 19 biggest players account for 81% of the market.

Page 39: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Identifying Core Niche Competitors

CORE NICHE COMPETITORS

NICHE COMPETITORS

POWERFUL MAINSTREAM COMPETITORS

FRINGE COMPETITORS

HIGH

LOW

HIGH

LOW

ORG

ANIC

SEA

RCH

VISI

BILI

TY

STRENGTH OF DOMAIN

GreatBritishChefs.com

Top 100 market domains shown. Bubble size = number of unique keywordsX axis: Strength determined by Majestic® metrics Y axis: Sum of estimated organic search visibility (log scale)

Page 40: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Analysing Core Niche Competitors

CORE NICHE COMPETITORS

NICHE COMPETITORS

POWERFUL MAINSTREAM COMPETITORS

FRINGE COMPETITORS

HIGH

LOW

HIGH

LOW

ORG

ANIC

SEA

RCH

VISI

BILI

TY

STRENGTH OF DOMAIN

Halfords in Red

Page 41: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Analysing Core Niche Competitors

CORE NICHE COMPETITORS

NICHE COMPETITORS

POWERFUL MAINSTREAM COMPETITORS

FRINGE COMPETITORS

HIGH

LOW

HIGH

LOW

ORG

ANIC

SEA

RCH

VISI

BILI

TY

STRENGTH OF DOMAIN

CORE NICHE COMPETITORS

NICHE COMPETITORS

POWERFUL MAINSTREAM COMPETITORS

FRINGE COMPETITORS

HIGH

LOW

HIGH

LOW

ORG

ANIC

SEA

RCH

VISI

BILI

TY

STRENGTH OF DOMAIN

Webuyanycar.com

Page 42: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Identifying Core Niche Competitors

CORE NICHE COMPETITORS

NICHE COMPETITORS

POWERFUL MAINSTREAM COMPETITORS

FRINGE COMPETITORS

HIGH

LOW

HIGH

LOW

ORG

ANIC

SEA

RCH

VISI

BILI

TY

STRENGTH OF DOMAIN

Top 100 market domains shown. Bubble size = number of unique keywordsX axis: Strength determined by Majestic® metrics Y axis: Sum of estimated organic search visibility (log scale)

Commercial & in-confidencewww.analyticsseo.com

Visionexpress.com

Page 43: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Initial Market Share results for GreatBritishChefs.com

Market Keywords: Keywords you rank for:

Your Market Share

2.8%

113K 24K

Market Value

£6.1MYour Market Value

£115K

Market Searches

24.7MSearches for your keywords

4.8M

by visibility

Opportunity Keywords

89K

Commercial & in-confidencewww.analyticsseo.com

Page 44: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Which existing keywords for existing content to focus on?

LONG-TERM ROI

LOW/NO ROI

QUICK ROI

MAINTAIN ROI

HIGH

LOW

HIGH

LOW

ORG

ANIC

GRO

WTH

POT

ENTI

AL

AVERAGE RELATIVE STRENGTH

Bubble size = number of unique keywordsX axis: Average relative strength of cluster determined by Majestic® metrics Y axis: Sum of estimated organic traffic growth per cluster(log scale)

9,215 of your keywords have growth potential, clustered into >400 categories below:

Page 45: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Weekly estimated traffic growth possible in this quadrant alone: 556,837Estimated current weekly traffic from Market Share: 204,703So with this quadrant one could (in theory) increase traffic by: 172 %

HARDER torank for

EASIER torank for

Which existing keywords for existing content to focus on?

Page 46: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

cluster label Your keyword count

average keyword frequency across

100 domainssum of potential traffic increase

average relative strength

beef 214 16.29 30,940 -25.12

dinner 268 17.27 30,818 -14.5

Jelly 82 12.96 28,117 -20.57

Example of 3 Clusters:

Example Keywords from 1st Cluster:

Your keyword Top competing URL Your top ranking page for this

keywordKeyword

frequency across domains

Potential traffic

increase (max)

Potential traffic

increase (5 ranks up)

Your current

rank

roast beef www.jamieoliver.com/recipes/beef-recipes/perfect-roast-beef/

www.greatbritishchefs.com/recipes/roast-beef-recipe-mushrooms-brandy-potatoes

15 1,150 400 29

roast beef recipe

www.jamieoliver.com/recipes/beef-recipes/perfect-roast-beef/

www.greatbritishchefs.com/recipes/roast-beef-recipe-mushrooms-brandy-potatoes

20 669 250 17

how to cook steak

www.bbcgoodfood.com/technique/how-cook-steak

www.greatbritishchefs.com/how-to-cook/how-to-cook-steak

17 635 300 22

Which existing keywords for existing content to focus on?

Page 47: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Example ‘Beef’ Cluster

Page 48: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Which NEW keywords for existing content to focus on?

LONG-TERM HIGH ROI

SHORT-TERM LOW ROI

SHORT-TERM HIGH ROI

SHORT-TERM LOW ROI

HIGH

LOW

HIGH

LOW

SUM

OF

ORG

ANIC

GRO

WTH

POT

ENTI

AL

AVERAGE RELATIVE STRENGTH – DOMAIN LEVEL

Bubble size = number of unique keywordsX axis: Average relative strength of cluster determined by Majestic® metrics Y axis: Sum of estimated organic traffic growth per cluster(log scale)

43,449 keywords GreatBritishChefs.com could rank for that relate closely to existing content:

Commercial & in-confidencewww.analyticsseo.com

Page 49: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Which NEW keywords for existing content to focus on?

Weekly estimated traffic growth possible in this quadrant alone: 164,592Estimated current weekly traffic from Market Share: 204,703So with this quadrant one could (in theory) increase traffic by: 80 %

HARDER torank for

EASIER torank for

Page 50: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Which NEW keywords for existing content to focus on?

cluster labelcount of unique

opportunity keywords

Sum of potential traffic increase

average relative strength

Average keyword frequency across

100 domains

turkey 859 29,222 -26.50 3.62

beef 1,462 16,810 -23.58 4.49

dinner 1,261 12,194 -27.07 2.89

Example of 3 clusters:

Example Keywords from 1st Cluster:

Relevant opportunity

keywordTop competing URL

Your most suitable* ranking page for this

keyword

Keyword frequency

across domains

Potential increase in

search volume (position 1)

Potential increase in

search volume (position 5)

Potential increase in

search volume(position 10)

christmas dinner

www.bbcgoodfood.com/recipes/category/christmas-dinner

www.greatbritishchefs.com/collections/christmas-recipes

14 59,786 20,000 500

christmas food

en.wikipedia.org/wiki/List_of_Christmas_dishes

www.greatbritishchefs.com/collections/christmas-recipes

10 100,000 24,383 750

christmas food ideas

en.wikipedia.org/wiki/List_of_Christmas_dishes

www.greatbritishchefs.com/collections/christmas-recipes

15 500,000 100,000 22,371

* Suitability is determined by semantic similarity of each ranking page’s keywords to the proposed new keyword phrase.

Commercial & in-confidencewww.analyticsseo.com

Page 51: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Which NEW keywords for NEW content to focus on?

LONG-TERM HIGH ROI

LONG-TERM AVERAGE ROI

SHORT-TERM HIGH ROI

SHORT-TERM AVERAGE ROI

HIGH

LOW

HIGH

LOW

SEAR

CH V

OLU

MES

– K

EYW

ORD

CLU

STER

LEV

EL

RELATIVE STRENGTH - DOMAIN LEVEL

Showing clusters of new potential opportunity keywords that are less related to existing content

20,863 new keywords for content creation strategies:

Commercial & in-confidencewww.analyticsseo.com

Page 52: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Which NEW keywords for NEW content to focus on?

Weekly estimated traffic growth possible in this quadrant alone: 302,272Estimated current weekly traffic from Market Share: 204,703So with this quadrant one could (in theory) increase traffic by: 148 %

HARDER torank for

EASIER torank for

Page 53: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Which NEW keywords for NEW content to focus on?

cluster labelcount of unique

opportunity keywords

Sum of potential traffic increase

average relative strength

Average keyword frequency across

100 domains

recipes 2,638 32,626 -22.00 4.22

pasta 655 20,687 -29.55 3.50

bread 822 20,079 -22.49 2.64

Example of 3 clusters:

Example Keywords from 1st Cluster:

Relevant opportunity

keywordTop competing URL

Keyword frequency

across domains

Potential search volume

(position 1)

Potential search volume

(position 5)

Potential search volume

(position 10)

quick dinner ideas

www.bbcgoodfood.com/recipes/category/quick-easy

10 10,969 731 50

simple recipes www.bbcgoodfood.com/recipes/collection/easy

10 4,969 950 70

how to cook www.theguardian.com/lifeandstyle/series/how-to-cook-the-perfect

10 2,999 647 11

Page 54: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Short-term, High ROI Recommendations for GreatBritishChefs.com

Content Type of optimisation Max % increase in weekly traffic (est.)

Most rewarding clusters

Existing Optimise existing content and keywords 172% Top 3 (of 70):beefdinnerjelly

Existing Optimise existing content with new keyword suggestions 80% Top 3 (of 34):

turkeybeefdinner

New Create new content with new keyword suggestions 148% Top 3 (of 41):

recipespastabread

Page 55: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Content Marketing Strategy

Sustainable investment in new

quality contentEngaged users

Increased shares, links

and click-thrus

Increased relevance in your cluster

Increased share of voice

in SERPs

Increase in Traffic and

Sales

Re-invest profits

Page 56: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Take-aways• Don’t try and second-guess Google’s algorithm – Let the data speak for itself• Analyse your whole market to get a different perspective and to see as many

opportunities as possible• You need ‘Artificial Intelligence for Natural Search’ - If your competitors are using

Big Data and Data Science and you’re not - then you’ll face an uphill battle• Produce quality content - http

://www.thesempost.com/google-quality-raters-guide-mobile/ • Gary Illyes, Google, “How many visitors have I helped today?” and not just “how

many visitors did I get.”• If you want ‘Direct Answers’ then you need to know the questions!• Look at your site’s E-A-T… That is, analyzing the page’s “expertise,

authoritativeness and trustworthiness”

Page 57: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

Useful Linkscarrotsearch.com/ - Ling3G Clustering Engine – Circles and Foamtree

www.visualisingdata.com/resources/ - great set of resources for visualising data

www.visualcomplexity.com/vc/ - a great resource about visualising Big Data

www.linkurious.com – visualising and clustering graph data

www.keylines.com – visualising and clustering graph data

www.zoomcharts.com – online HTML 5 charting tool

www.neo4j.com – graph database and tutorials

www.thesempost.com/google-quality-raters-guide-mobile/ - recent article on the latest manual (not a link to the manual)

Google Quality Raters PDF – essential reading for Content Marketers

Page 58: Big Data graph Clustering with Laurence O'Toole - Digital Marketing Show, November 2015

About Analytics SEO

@analyticsseo #bigdatascience

Big Data Science for Content Marketing Success

[email protected] www.analyticsseo.com +44 208 977 4465