SEO Master Class Webit 2010

SEO Masterclass

Rand Fishkin, CEO & Co-founder, SEOmoz

Webit, Sofia October 2010

Topics for the Masterclass

• Correlation analysis of search results• Changes from Google Instant• Information architecture & navigation structure• Overcoming Twitter’s cannibalization of the link graph• Making Analytics Actionable• New Research: Topic Modeling in the Search Results

Use Statistical Analysis to Answer

Important SEO Questions

Correlation ≠ Causation

The more I wear suits, the more I speak on panels.

Therefore: wearing suits causes me to speak on panels.

Understanding Correlation Significance

No Correlation

Exact Match Domain

Perfect Correlation

Most of our data for search rankings falls in this region(which we’d expect given algorithms w/ 200+ ranking factors)

Question #1:

How to Best “Optimize” a Site for

Search Engine Rankings

Methodology

• 11,351 SERPs via Google AdWords Suggest

• 1st Page Only (usually ~10 results per page)

• Correlations are w/ Higher Position on Page 1

• Controlled for SERPs Where All (or None) of the

Results Matched the Metric

Methodology

Looking for elements that higher ranking

pages have that lower ones do not

NOT looking at raw counts of how many

pages featured a given element

Contains All Query Terms in Domain Name

Exact Match Hyphenated

Domain

Exact Match Domain

Highest Stderr = 0.0241804

Our Interpretation

• Exact match domains remain powerful in both

engines (anchor text could be a factor, too)

• Hyphenated versions are less powerful, though

more frequent in Bing (G: 271 vs. B: 890)

• Just having keywords in the domain name has

substantial positive correlation

Highest Stderr = 0.00350211

KWs in Body

KWs in Alt Attribute

KWs in H1 Tag

KWs in URL

KWs in Title

Our Interpretation

• The Alt attribute of images is interesting

• Putting KWs in URLs is likely a best practice

• Everyone optimizes titles (G: 11,115 vs. B: 11,143).

Differentiating here is hard.

• (Simplistic) on-page optimization isn’t a huge factor

Highest Stderr = 0.0269818

.gov

.edu

.info

.net

.org

.com

Our Interpretation

• More reasons to believe Google when they

say .gov, .info and .edu are not special cased

• The .org TLD extension is surprising – do they earn

more links? Less spam? More non-commercial?

• Don’t forget about branding/user behavior - .com is

still probably a very good thing (at least own it)

Highest Stderr = 0.0033353

Content Length (tokes in body)

URL Length(chars.)

Domain Name Length (chars.)

Our Interpretation

• Shorter URLs are likely a good best practice

(especially on Bing)

• Long domains may not be ideal, but aren’t awful

• Raw content length seems marginal in correlation

Question #2:

What Kind of Links Matter & How

Should We Evaluate Links?

Highest Stderr = 0.00335677

# of Linking Root Domains to URL

# of Links to URL

Our Interpretation

• Links are likely still a major part of the algorithms

• Bing may be slightly more naïve in their usage of

link data than Google, but better than before

• Diversity of link sources remains more important

than raw link quantity

Highest Stderr = 0.00415058

# of Links w/ exact match anchor text

# of linking root domains w/

exact match anchor text

Our Interpretation

• Many anchor text links from the same domain likely

don’t add much value

• Anchor text links from diverse domains, however,

appears highly correlated

• Bing and Google are relatively similar in evaluating

these metrics

Correlation of Page-Level Link Valuation Metrics

Our Interpretation

• PageRank (and similar algorithms) are not particularly

representative of rankings (but are somewhat correlated)

• Linking domains are likely a better metric than raw links

• Page Authority is reasonably good, but has a way to go

Correlation of Domain-Level Link Valuation Metrics

Our Interpretation

• No single domain valuation metric is especially well

correlated with rankings

• Rankings of individual pages may be more

disparate we typical think re: “domain authority”

• Overall, we’re still very naïve when it comes to

understanding how links influence search rankings

Question #3:

How Does Google Instant Change

Keyword Demand / SEO?

http://www.readwriteweb.com/archives/report_google_search_box_in_firefox_accounts_for_9.php

Are Most Users Seeing/Using Google Instant?

Methodology: Keyword Referral Search Data

• Look at keyword sending traffic via analytics

• Distribute into groups by word-length

• Analyze shifts in demand by keywords that

brought visits to the site

• Compare from period prior to Google Instant

and directly after

http://www.mecmanchester.co.uk/blog/google-instant-data-after-12-days.html

Via MEC Manchester (UK)5 Sites, 4 Verticals, 10K+ Keywords

Via Distilled Consulting (UK)11 Sites, Various Sizes (3.5K – 75K weekly visits), 75K+ Keywords

http://www.distilled.co.uk/blog/seo/impact-of-google-instant/

Via ConductorMultiple sites, 880K visits, 10Ks of keywords

http://blog.conductor.com/2010/09/what%E2%80%99s-been-the-impact-of-google-instant-on-searcher-behavior-so-far-not-much/

Interesting Takeaways

• Google Instant seems not to have shifted keyword

demand by much (if at all)

• Google “suggest” has been out for a long time

already; users are likely accustomed to this feature

• The “long tail” may get longer/shorter over time, but

Instant seems less responsible than other factors

Goals of Successful Information

Architecture

Semantically Logical Structure

Minimize Click-Depth

Maximize Usability of Navigation

Tips for Semantically Useful

Navigation

Initially Design without Keyword Research

Add in Keyword Research Based Modifications

Validate Architecture/Path with Non-SEOs

Tips for Minimal Click-Depth

Imitate the Ideal Nav Pyramid

Broad Linking at Top Levels

Editorial Categorization > User-Defined

Editorial Categorization > User-Defined

HACK: Multi-Level HTML Sitemap

Tips for Usable Navigation

Obvious Navigation Elements

Naming Conventions that Match Intent

User & Usability Testing

Avoiding Common “Big Site”

Problems

Duplicate Content Issues

Rel Canonical Tags

Google Webmaster Tools

SEOmoz Web App

Scraping & Re-Publishing

Employ Absolute URLs

Absolute: <a href=“http://www.seomoz.org/blog”> anchor </a>

Relative: <a href=“../blog”> anchor </a>

C&D vs. Large, Credible Orgs that Scrape

Don’t Go Overboard w/ Bot Blocking

Incomplete Indexation

Track Referrals, not Site: Commands

Check Page “Types” that Don’t Receive Traffic

XML Sitemaps

Content Syndication

RSS Feeds

Twitter for Indexation

“Search Results” in the SERPs

Create Category “Landing” Pages

Remove Obvious Traces of “Search” on Landing Pages

Thin Content Issues

Bolster w/ UGC

Employ Scalable Content Production

Keep “Thin” Pages Out of the SERPs

Faceted Navigation

Rel Canonical Can Help

Use AJAX to Reload Pages

Watch Out for Google Crawling Javascript

Offer Facets Only to Logged-In / Cookied Users

Logged-In = 345 / Googlebot = 141

Overcoming Twitter’s

Cannibalization of the Link Graph

Way Back in 2007

Interesting content, blog posts & linkbait earned LOTS of links

Fast Forward to 2010

Not so many links (in comparison)

Fast Forward to 2010

But tons of social sharing (and tweets)

Are Pages Linking Out Less?

Via Linkscape’s web index

How Do We Earn Traditional Web

Links (the kind search engines love)?

Tactic #1:

Embeddable Content

Infographics

http://royal.pingdom.com/2010/02/24/google-facts-and-figures-massive-infographic/

+1,085 links from

356 root domains

Badges

http://www.zillow.com/webtools/badges/

Value-Add Widgets

Tactic #2:

Reference Material

Research & Data

http://www.time.com/time/health/article/0,8599,2014332,00.html

Awards + Rankings

http://www.moneycrashers.com/top-personal-finance-blogs/

Citation-Worthy Explanations

http://www.seomoz.org/blog/googles-algorithm-pretty-charts-math-stuff

Tactic #3:

Syndicated Content

Niches where content is low-supply/high-demand

http://perfectmarket.com/vault_index_summer_2010_infographic

ID sites that already syndicate

from someone else

Tactic #4:

Stick to Niches w/o Twitter Adoption

Find sectors where traditional

blogs/forums dominate conversation

http://www.eng-tips.com/

Many of these “old-school” sites have

followed external links (but don’t abuse these)

Note the nofollow

highlighting

Some “Web 2.0” Ones, Too

Impressive domain and

page level metrics

Tactic #5:

Friends, Partners, Customers &

Vendors

Friends + Family

Partners

Customers

http://www.seomoz.org/blog/headsmacking-tip-1-link-requests-in-order-confirmation-emails

Vendors

http://burton.kontain.com/evogear/

Tactic #6:

Twitter May Take Your Tweets, But

They’ll Never Take Your Content!

Turn Your Tweets Into Content

http://twournal.com/home

Turn Tweets from industry leaders into

content, too (this entices them to share)

http://www.seomoz.org/blog/bing-vs-google-prominence-of-ranking-elements

Tactic #7:

Can’t Beat ‘em? Join ‘em!

Twitter is (almost certainly) influencing (at least some) rankings

Lots of tweets, virtually no links,

but remarkable rankings

Twitter can send lots of direct traffic

You need to target the right Tweeters

http://twitaholic.com/top100/followers/

The Ones Who Send Real Traffic

http://mashable.com/2009/07/07/twitter-clickthrough-rate/

Use the “Rank”Not the “Grade”

http://twittergrader.com

Takeaways

• # of Followers DEFINITELY isn’t everything

• Tweeting heavily may not necessarily hurt the attention

people pay to your tweets

• Klout score probably isn’t great for predicting the reach of

an individual’s tweets/messages

• TwitterGrader Rank may be useful for predicting the

traffic you’d get from a tweeter

• Avg. CTR across 254 shared links = 1.17% (don’t feel

bad if only 1/100 followers clicks a link)

Making Analytics Actionable

To Make Analytics Actionable Always Ask:#1 - “Why am I measuring this?”

#2 – “What would I do if results were different?”

# of Visits Per Search Engine Over Time

Action: Measure against search engine market shares & volume to determine whether you’re making positive strides

# Pages Getting Search Referrals Over Time

Measure this number on a weekly/monthly basis

Action: Discover if indexation is an issue worth effort

This number sucks. Learn more about why at:www.seomoz.org/blog/indexation-for-seo-real-numbers-in-5-easy-steps

# of Keywords Sending Traffic from aSearch Engine over Time

Action: Determine if content additions are accretive and what drives growth/shrinkage in search traffic

Did rankings fall? Or is demand down?

Search Referral Analytics

# of Visits per Keyword

Action: Analyze top traffic drivers from a value perspective, check rankings for potential easy wins & get answers if traffic dips

“SEO Tools” is a big win and we could rank higher

First-Time vs. Returning Visits per Keyword

The keyword “SEO” leans toward first-time visits

Action: Determine value of reaching new visitors vs. converting branded users (focus efforts on the more valuable one)

This metric speaks to business strategy about converting existing fans vs. reaching new customer segments

Distribution of Keyword Referrals

Action: Discover strengths vs. opportunities (60-70% of traffic is typically in the long tail and it converts better)

Keyword Rankings

www.seomoz.org/rank-tracker

Action: Know if traffic spikes/dropoffs are from rankings, indexation or search demand shifts

Rankings and Traffic both Dropped

Page Two Rankings

Referrals from Page 2

Action: Identify low hanging fruit that can be optimized quickly

Could totally 301 this to www.opensiteexplorer.org

Engagement Analytics

Time on Site

Action: Compare to ROI metrics; if they correlate, improve on keywords/landing pages with low time on site

Average “upgrade to PRO” visitor spent a whopping 44 minutes on SEOmoz!

# of Page Views

Action: Depending on your metrics, a “sweet spot” of pages browsed often dictates a conversion event – optimize towards it

Average “upgrade to PRO” visitor visits 12X the pages of an average visitor

Repeat Visit Ratio

Action: Find what content/activities/referrers send engaged traffic and copy those while improving subpar pages

Sharing/Linking Activity

“Sharing Activity” Conversions

Action: Find patterns/sources that predict sharing activities (both content and CTAs) and make them testable conversion events

GA allows you to set custom actions as “goals” then filter, monitor and improve on these metrics

Latent Conversion Tracking

Removing Last-Click Attribution

Full Path Analysis

Initial Referrer

www.seomoz.org/blog/how-to-get-past-last-touch-attribution-with-google-analytics

ROI Analytics

Lifetime Customer Value

Cost of Acquisition

Return on Investment

ROI = CLTV - CAC

No. No. No.

Yes. Yes. Yes.

Always Be Asking “What’s the ROI?”

Get the ROI for every category (and subset)

New Research: Topic Modeling in the

Search Results

Methodology: LDA (Latent Dirichlet Allocation)

• Build an LDA model based on the English

language Wikipedia dataset (8mil+ pages)

• Generate scores for top 10 rankings across

several thousand search results

• Look at correlation of search rankings with

scores (in process)

Chance of word is because of a topic=

(Number of times the document already uses that topic a lot)X

(Number of times that word has been in that topic)

Simplified LDA Formula

Tool to Test it Out

http://www.seomoz.org/labs/lda

Tool to Test it Out

http://www.seomoz.org/labs/lda

Tool to Test it Out

We might need to work the “relevance”

of our content

http://www.seomoz.org/labs/lda

Interesting Takeaways

• There may be more to “on-page” optimization then

just using target keywords in the right places / ways

• Search engines keep saying “make relevant content”

– perhaps we can get more scientific and precise about

what “relevant” means

• Our LDA topic modeling work is still in its infancy.

Expect more data, correlations, etc. in weeks to come.

Q+A

Rand Fishkin, CEO & Co-Founder, SEOmoz

• Twitter: @randfish

• Blog: www.seomoz.org/blog

• Email: rand@seomoz.org