rand fishkin: two algorithm world

Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com

SEO in a Two Algorithm World

bit.ly/twoalgo

Get the presentation:

State of Search

November 16th, 2015 8:00am

Dallas, TX

Remember

When…

We Had One Job

Perfectly Optimized Pages

The Search Quality

Teams Determined

What to Include in

the Ranking System

They decided

links > content

By 2007, Link Spam Was Ubiquitous

This paper/presentationfrom Yahoo’s spam team in 2007 predicted a lot of what Google would launch in Penguin Oct, 2012 (including machine learning)

Even in 2012, It Felt Like Google Was Making Liars Out

of the White Hat SEO World

Via Wil Reynolds

Google’s Last 3 Years of

Advancements Erased a

Decade of Old School SEO

Practices

They Finally Launched Effective Algorithms to Fight

Manipulative Links & Content

Via Google

And They Leveraged Fear + Uncertainty of

Penalization to Keep Sites Inline

Via Moz Q+A

Google Figured Out Intent

Rand probably

doesn’t just want

webpages filled

with the word

“beef”

They Looked at Language, not Just Keywords

Oh… I totally

know this one!

They Predicted When We Want Diverse Results

He probably

doesn’t just

want a bunch of

lists.

They Figured Out When We Wanted Freshness

Old pages on this

topic probably

aren’t relevant

anymore

Their Segmentation of Navigational from Informational

Queries Closed Many Loopholes

Google Learned to ID Entities of Knowledge

And to Connect Entities to Topics & Keywords

Via Moz

Brands Became a Form of Entities

These Advancements Brought Google (mostly)

Back in Line w/ Its Public Statements

Via Google

During These Advances,

Google’s Search Quality

Team Underwent a

Revolution

Early On, Google Rejected Machine Learning in the

Organic Ranking Algo

Via Datawocky, 2008

Amit Singhal Shared Norvig’s Concerns About ML

Via Quora

In 2012, Google Published a Paper About How

they Use ML to Predict Ad CTRs:

Via Google

2012

“Our SmartASS system is a

machine learning system. It

learns whether our users

are interested in that ad,

and whether users are going

to click on them.”

By 2013, It Was

Something Google’s

Search Folks Talked

About Publicly

Via SELand

As ML Takes Over More of Google’s Algo, the

Underpinnings of the Rankings Change

Via Colossal

Google is Public About How They Use ML in Image

Recognition & Classification

Potential ID Factors(e.g. color, shapes,

gradients, perspective,

interlacing, alt tags,

surrounding text, etc)

Training Data(i.e. human-labeled images)

Learning

Process

Best

Match

Algo

Google is Public About How They Use ML in Image

Recognition & Classification

Via Jeff Dean’s Slides on Deep Learning; a Must Read for SEOs

Machine Learning in Search Could Work Like This:

Potential Ranking

Factors(e.g. PageRank, TF*IDF,

Topic Modeling, QDF, Clicks,

Entity Association, etc.)

Training Data(i.e. good & bad search

results)

Learning

Process

Best Fit

Algo

Training Data(e.g. good search results)

This is a good SERP –

searchers rarely bounce, rarely

short-click, and rarely need to

enter other queries or go to

page 2.

Training Data(e.g. bad search results!)

This is a bad SERP –

searchers bounce often,

click other results, rarely

long-click, and try other

queries. They’re definitely

not happy.

The Machines Learn to Emulate the Good Results & Try to Fix

or Tweak the Bad Results

Potential Ranking

Factors(e.g. PageRank, TF*IDF,

Topic Modeling, QDF, Clicks,

Entity Association, etc.)

Training Data(i.e. good & bad search

results)

Learning

Process

Best Fit

Algo

Deep Learning is Even More Advanced:

Dean says by using deep

learning, they don’t have to

tell the system what a cat is,

the machines learn,

unsupervised, for

themselves…

We’re Talking About

Algorithms that Build

Algorithms

(without human

intervention)

Googlers Don’t Feed in Ranking Factors… The Machines

Determine Those Themselves.

Potential Ranking

Factors(e.g. PageRank, TF*IDF,

Topic Modeling, QDF, Clicks,

Entity Association, etc.)

Training Data(i.e. good search results)

Learning

Process

Best Fit

Algo

No wonder these guys are stressed about Google

unleashing the Terminators Via CNET & Washington Post

What Does Deep Learning

Mean for SEO?

Googlers Won’t Know Why Something Ranks or

Whether a Variable’s in the Algo

He means other Googlers.

I’m Jeff Dean. I’ll know.

The Query Success Metrics Will Be All That

Matters to the Machines

Long to Short Click Ratio Relative CTR vs. Other Results

Rate of Searchers Conducting

Additional, Related Searches

Metrics of User Engagement

on the Page

Metrics of User Engagement

Across the Domain

Sharing/Amplifcation Rate

vs. Other Results

The Query Success Metrics Will Be All That

Matters to the Machines

Long to Short Click Ratio Relative CTR vs. Other Results

Rate of Searchers Conducting

Additional, Related Searches

Metrics of User Engagement

on the Page

Metrics of User Engagement

Across the Domain

Sharing/Amplifcation Rate

vs. Other Results

If lots of results on a SERP

do these well, and higher

results outperform lower

results, our deep learning

algo will consider it a

success.

We’ll Be Optimizing Less

for Ranking Inputs

Unique Linking Domains

Keywords in Title

Anchor Text

Content Uniqueness

Page Load Speed

And Optimizing More for Searcher Outputs

High CTR for this position?

Good engagement?

High amplification rate?

Low bounce rate?

Strong pages/visit afterlanding on this URL?These are likely to be the

criteria of on-site SEO’s future… People return to the siteafter an initial search visit

OK… Maybe in the future. But,

do those kinds of metrics really

affect SEO today?

Remember Our Queries & Clicks Test from 2014?

Via Rand’s Blog

Since then, it’s been much harder to move the

needle with raw queries and clicks…

Case closed! Google says they don’t use clicks in the rankings.

Via Linkarati’s Coverage of SMX Advanced

But, what if we tried long

clicks vs.

short clicks?

Note SeriousEats,

ranking #4 here

11:39am on June 21st,

I sent this tweet:

40 Minutes & ~400

Interactions Later

Moved up 2 positions after 2+

weeks of the top 5 staying

static.

70 Minutes & ~500

Interactions Total

Moved up to #1.

Stayed ~12 hours, when it

fell to #13+ for ~8 hours, then

back to #4.

Google? You

messing with us?

Via Google Trends, we can see the relative impact

of the test on query volume

~5-10X normal volume

over 3-4 hours

BTW – This is hard to replicate.

600+ real searchers using a

variety of devices, browsers,

accounts, geos, etc. will not look

the same to Google as a Fiverr

buy, a clickfarm, or a bot. And

note how G penalized the page

after the test… They might not put

it back if they thought the site

itself was to blame for the click

manipulation.

OK… Maybe in the future. But,

do those kinds of metrics really

affect SEO today?

Via Bloomberg Business

The Future:

Optimizing for Two

Algorithms

The Best SEOs Have Always

Optimized to Where Google’s Going

Today, I Think We Know,

Better Than Ever, Where That Is

Welcome to your new home, the User/Usage Signals + ML Model Cabin

We Must Choose How to Balance Our Work…

Hammering on the Fading Signals of Old…

Or Embracing Those We

Can See On the Rise

Classic SEO(ranking inputs)

New SEO(searcher outputs)

Keyword Targeting Relative CTR

Short vs. Long-Click

Content Gap Fulfillment

Task Completion

Success

Amplification & Loyalty

Quality & Uniqueness

Crawl/Bot Friendly

Snippet Optimization

UX / Multi-Device

Branded Search & TrafficLinks & Anchor Text

5 New(ish) Elements of

Modern SEO

Punching Above Your

Ranking’s Average CTR#1

Optimizing the Title, Meta Description, & URL

a Little for KWs, but a Lot for Clicks

If you rank #3, but have a higher-

than-average CTR for that

position, you might get moved up.

Via Philip Petrescu on Moz

Every Element Counts

Does the title match

what searchers want?

Does the URL seem

compelling?

Do searchers

recognize & want to

click your domain?

Is your result fresh?

Do searchers want a

newer result?

Does the description

create curiosity &

entice a click?

Do you get the

brand dropdown?

Given Google Often Tests New Results Briefly on Page One…

It May Be Worth Repeated Publication on a Topic to Earn that High CTR

Shoot! My post only made it to #15…

Perhaps I’ll try again in a few

months.

Driving Up CTR Through Branding Or Branded

Searches May Give An Extra Boost

#1 Ad Spender

#2 Ad Spender

#4 Ad Spender

#3 Ad Spender

#5 Ad Spender

With Google

Trends’ new, more

accurate, more

customizable

ranges, you can

actually watch the

effects of events

and ads on search

query volume

Fitbit has been running ads on

Sunday NFL games that clearly

show in the search trends data.

Beating Out Your Fellow SERP

Residents on Engagement#2

Together, Pogo-Sticking & Long Clicks Might

Determine a Lot of Where You Rank (and for how

long)

Via Bill Slawski on Moz

What Influences Them?

Speed, Speed, and More Speed

Delivers the Best UX on Every Browser

Compels Visitors to Go Deeper Into Your Site

Avoids Features that Annoy or Dissuade Visitors

Content that Fulfills the Searcher’s Conscious &

Unconscious Needs

An SEO’s Checklist for Better Engagement:

Via NY Times

e.g. this interactive

graph that asks visitors

to draw their best

guess likely gets

remarkable

engagement

e.g. Poor Norbert

does a terrible job

at SEO, but the

simplicity compels

visitors to go

deeper and to

return time and

again

Via VoilaNorbert

e.g. Nomadlist’s

superb, filterable

database of cities and

community for remote

workers.

Via Nomadlist

Filling Gaps in Your

Visitors’ Knowledge#3

Google’s looking for

content signals that a

page will fulfill ALL of

a searcher’s needs.

I think I know a

few ways to

figure that out.

ML models may note

that the presence of

certain words,

phrases, & topics

predict more

successful searches

e.g. a page about New York that doesn’t

mention Brooklyn or Long Island may

not be very comprehensive

If Your Content Doesn’t Fill the Gaps in Searcher’s Needs…

e.g. for this query, Google

might seek content that

includes topics like “text

classification,”

“tokenization,” “parsing,”

and “question answering”

Those Rankings Go to Pages/Sites That Do.

Moz’s Data Science Team

is Working on Something to

Help With This

The (alpha) tool extracts

likely focal topics from a

given page, which can

then be compared vs. an

engines top 10 results

In the meantime, check

out

Alchemy API

Or MonkeyLearn

Fulfilling the Searcher’s Task

(not just their query)#4

Broad search Narrower search

Even narrower

search

Website visit

Website

visit

Brand

search

Social validation Highly-specific search

Type-in/direct visit Completion of Task

Google Wants to Get Searchers Accomplishing

Their Tasks Faster

Broad search

All the sites (or answers) you probably

would have visited/sought along that path

Completion of Task

This is Their Ultimate Goal:

If Google sees

that many

people who

perform these

types of

queries:

Eventually end

their queries on

the topic after

visiting Ramen

Rater…

The Ramen Rater

They might use the

clickstream data to

help rank that site

higher, even if it

doesn’t have

traditional ranking

signals

They’re definitely getting and storing it.

A Page That Answers the Searcher’s Initial Query

May Not Be Enough

Searchers performing this

query are likely to have the

goal of completing a

transaction

Google Wants to Send Searchers

to Websites that Resolve their

Mission

This is the only site

where you can reliably

find the back issues

and collector covers

Earning More Shares, Links,

& Loyalty per Visit#5

Pages that get lots of

social activity &

engagement, but few

links, seem to

overperform…

Google says they

don’t use social

signals directly, but

examples like these

make SEOs

suspicious

Even for insanely competitive

keywords, we see this type of

behavior when a URL gets

authentically “hot” in the

social world.

Data from Buzzsumo & Moz

show that very few articles

earn shares AND that links &

shares have almost no

correlation.

Via Buzzsumo & Moz

I suspect Google doesn’t

use raw social shares as

a ranking input, because

we share a lot of content

with which we don’t

engage:

Via Chartbeat

Google Could Be Using a Lot of Other Metrics/Sources to Get

Data That Mimics Social Shares:

Clickstream (from Chrome/Android)

Engagement (from Chrome/Android)

Branded Queries (from Search)

Navigational Queries (from Search)

Rate of Link Growth (from Crawl)

But I Don’t Care if It’s Correlation or Causation;

I Want to Rank Like These Guys!

BTW – Google Almost Certainly Classifies SERPs

Differently & Optimizes to Different Goals

These URLs have loads of shares & may have high

loyalty, but for medical queries, Google has different

priorities

Knowing What Makes Our Audience (and their

influencers) Share is Essential

From an analysis of the 10,000 pieces of content receiving the most social shares on the web by Buzzsumo.

Knowing What Makes them Return (or prevents

them from doing so) Is, Too.

We Don’t Need “Better” Content… We Need “10X” Content.

Via Whiteboard Friday

Wrong Question:

“How do we make something as

good as this?”

Right Question:

“How do we make something 10X

better than any of these?”

10X Content is the Future, Because It’s the Only Way to Stand

Out from the Increasingly-Noisy Crowd

http://www.simplereach.com/blog/facebook-continues-to-be-the-

biggest-driver-of-social-traffic/

The top 10% of content

gets all the social shares

and traffic.

Old School On-Site Old School Off-Site

Keyword Targeting Link Diversity

Anchor Text

Brand Mentions

3rd Party Reviews

Reputation Management

Quality & Uniqueness

Crawl/Bot Friendly

Snippet Optimization

UX / Multi-Device

None of our old school tactics will get this

done.

We Have to Go From This:

Wikipedia on Vince Carter (currently ranking #10 for “Vince Carter Dunks”)

To This:

Via ESPN

I’ve Been Curating a List of “10X” Content Over the Last

8 months… It’s All Yours:

bit.ly/10Xcontent

FYI that’s a capital “X”

Welcome to the

Two-Algorithm World of

2015

Algo 1: Google

Algo 2: Subset of Humanity

that Interacts With Your

Content

“Make Pages for People, Not

Engines.”

Terrible Advice.

Keyword Targeting Relative CTR

Short vs. Long-Click

Content Gap Fulfillment

Amplify & Return Rates

Task Completion

Success

Quality & Uniqueness

Crawl/Bot Friendly

Snippet Optimization

UX / Multi-Device

Engines People

Optimize for Both:

Algo Input & Human Output

Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com

bit.ly/twoalgo