seo analytics: how to report & improve performance

SEO Analytics: How to report &

improve performance

SEO Reporting

with (not provided)

Background

1. (not provided) keyword traffic affects all analytics

platforms.

2. Google SEO keywords used to hold a referral with q

parameter holding keyword e.g. q=keyword

3. Now redirects referral to take out q parameter, but retains

other information

4. Distinct from (not set) or (direct) traffic, which has no

referral information at all e.g. iOS browsers, https secure

search

Contents

1. What Data Is Left?

2. User/Page Based Reporting

3. Current Tools

What Data Is Left?

What data is left?

● Google Webmaster Tools

● SEO Rank Checkers

● Google Referral Information

● Historic Analytics Data

● AdWords Keyword Reports

● SEO User Behaviour

Google Webmaster Tools

Top Queries

○ Keyword - Sometimes misses important

keywords

○ Impressions - rarely displays all SEO

impressions

○ Clicks - rarely displays all

○ Avg. Position - looks reliable compared with

other ranking methods

Top Pages - Same as above but on URL level

Can download via Python tool, but keywords per page only

available manually

Impression/Click metrics only useable for trend splits

SEO Rank Checkers

SEOmoz, AdvancedWebRanking, AuthorityLabs etc.

● Can provide what keywords rank for which URL

● Prone to personalisation, location inaccuracies

● Against Google ToS

Can be used to find which URLs are ranking for which

keyword, but rankings may be inaccurate.

Google Referral Information

Take the Google referral URL, find what other information is

available aside from query.

● Keyword Ranking still available for now (cd=)

● What type of search result click (sitelink, video,

knowledge graph etc.)

● Landing Page URL

Can be used to narrow down what type of result hit a page,

to compare with Rank Checkers

Data from 2012 verifies GWT Average rankings well (85%

correlation with median position)

Historic Analytics Data

Take metrics from

keywords from

when you had

data.

Use Forecasting,

Seasonal cycles to

provide what

trends keywords

take

AdWords Data

Paid Search still has all metrics available.

Run exploratory AdWords campaigns to get benchmark

data, or use existing campaigns.

● Get reliable Search Impressions

● Per Keyword Conversion Rates

● Use GWT link with AdWords to find Paid/SEO

relationships

SEO User and Page Based Reporting

SEO User Based Reporting

Keywords are powerful as they indicate the intent of a

user hitting a website.

But can this information be found elsewhere?

Cohort Analysis - get demographic data from a user,

infer what lifetime value of that user is and where SEO

falls in that journey

SEO User Behaviour

How users behave onsite differs depending on which

keyword they arrive upon.

Examining branded multi-touch behaviour in study for a

client, we found:

● Brand Searchers touched website ~10% more often

than non-brand

● Brand Searchers average number of touches before

conversion were ~150% less than Non-brand

Can be used to make judgements on which visitor type

hits a page from (not provided)

Click Through Rates

As keywords change position, amount of traffic

fluctuates. (i.e if ranking goes down, traffic goes down)

If observed keyword changes rank, check landing URL

for projected change in traffic.

CTR from GWT and industry benchmarks.

Requires historic record of keywords and positions

Page Based Reporting

With loss of SEO metrics by keyword, SEO metrics by

URL page helpful.

Good SEO fundamentals provide inference of SEO

keyword behaviour:

● Client Study showed average of 70% of keywords

hitting URL were also in URL's <title> tag

● Requires unique title tags to be effective

● Internal links, H1, tags etc. also influence SEO

rankings

Narrow down what keywords could be making each

page's (not provided) split.

Current Tools

(not provided) % split

● Take all keywords that are not (not

provided), find % split distribution

● Apply to (not provided) traffic, estimate

what % of traffic are attributed to other

keywords

Will only work with big samples of keywords,

which are dwindling to 0.

Use GWT and other data to find split in

future.

URL Split of (not provided)

● Take landing page URL for SEO keywords

● Download title tag, metrics per URL

● Find % split of (not provided) for each URL

Benchmark against pre (not provided) era, to find

% of keywords that are in title tag

● Useful metric for focusing title tags for SEO

● Narrows down list of possible keywords to

landing URL

Google Webmaster Tool API

● Only way to download Search Queries is currently Python tool.

● Allows download of Top Queries, Top Pages

● Lacks Keywords per Top Page.

● Runs every week for historic archive

SEO keyword Forecast

Apply to

historic data to

find seasonal

trends.

Apply to all

SEO traffic or

individual

keywords

SEO keyword CTR Prediction

Apply to Impressions

and ranking

keywords to find

projected traffic

Unreliable under

position 10

May have different

CTR distribution

depending on query

Monitoring SEO Rank changes

Predict traffic changes according to CTR and rank change

1. Monitored keyword increases

from position 5 to position 1

2. CTR expected to raise by

factor of 5.5

3. Repeat for every SEO

keyword pointed at URL in

rank checker

4. Calculate overall traffic

change

5. Compare with actual traffic

change to that URL

SEO keyword Clustering

Apply Machine

Learning to find

user behaviour

e.g. Split brand vs

non-brand

behaviour

Summary

Putting all above together

1. Shortlist what keywords are likely to hit URL.

2. Compare with keywords that URL is ranking for

3. Infer traffic split of keywords

4. Monitor changes to rankings, project changes

to (not provided) traffic for displayed URL

5. Account for seasonal and forecasted traffic

volumes

Further Reading

http://www.slideshare.net/MarketingFestival/5-michaelking-jak-se-vyporadat-s-not-provided

Custom Channel Grouping

& utm tagging best practices

Traffic types

Earned

Media

Owned

Media

Paid

Media

Traffic types

Direct

(owned)

Display

(Paid)

Organic

(Earned)

Assisted conversion report

for Social Paid is broken

Social

Because Default Channel

Grouping is wrong

“Social Paid” incorrectly grouped under “(Other)”

Here is a Private Channel

Grouping fixed example...

Note: assumes that utm_campaign=*_paid_* is Social Paid in Private Channel Group:

https://analytics.google.com/analytics/web/template?uid=WsCugGmAReeaI4JEOfta4A

Correct tagging... Expected GA utm_medium for social are

1. sm (like cpc)

2. social (like organic)

3. social-network

4. social network

5. social-media

6. social media

Current... utm_medium=FacebookIrelandLtd

utm_source=Facebook.com_N5851.270751FACEBOOK_131474803_70473783_304144507_5441400

utm_campaign=Essence_paid_FacebookIrelandLtd

utm_content=20160601 20160601073000

utm_id=

utm_term=n/a

dclid=COCYws21hs0CFVMg0wod3oUDnw

.

.

.

Social Paid should be... utm_medium=sm

utm_source=facebook.com

utm_campaign=2016_01_01_Essence_paid_AirportCampaign

utm_content=N5851.270751FACEBOOK_131474803_70473783_304144507_5441400

utm_id=N5851.270751FACEBOOK_131474803_70473783_304144507_5441400

utm_term=n/a

dclid=COCYws21hs0CFVMg0wod3oUDnw

.

.

.

Social Organic should be... utm_medium=social

utm_source=facebook.com

utm_campaign=2016_01_01_Essence_organic_AirportCamp

utm_content=N5851.270751FACEBOOK_131474803_70473783_304144507_5441400

utm_id=N5851.270751FACEBOOK_131474803_70473783_304144507_5441400

utm_term=n/a

dclid=n/a

.

.

.

utm_medium=referral

utm_source=facebook.com

utm_campaign=n/a

utm_content=n/a

utm_id=n/a

utm_term=n/a

Social Referral should be...

Excel workbook

(future data only)

bit.ly/utm-tagging-examples

Note: Using 5 GA profile filter fix on a

new GA profile view (rather than re-tag)

1.Source=^Facebook\.com_(.*)

Campaign=.*_paid_.*

CampaignCode=$A1

2.Source=^Facebook\.com_(.*)

Campaign=.*_paid_.*

AdContent=$A1

3.Source=^Facebook\.com_(.*)

Campaign=.*_paid_.*

Output Source=facebook.com

4.Medium=^facebook(.*)

Campaign=.*_paid_.*

Output Medium=sm

5.Medium=^facebook(.*)

Output Medium=social

(future data

only)

Report Automation Examples

Example Dashboard:

https://analytics.google.com/analytics/web/template?uid=NyGD2Rg0RaufLDoIan18tg

GA dashboard can be auto-emailed

GoogleSheets - GA plugin

GA setting: Goals

Goals

Time spent + Pages/session

goals are not good macro KPI`s

Instead...

Enable Smart Engagement Goals

Enable Smart engagement Goals

Also add newsletter tracking

Example KPIs

bit.ly/better-kpis

3. Check GA country filter

Check URL based country filter working as

confliction data in GEO-ip report...

4. Provide instructions on how to

setup GTM to report on

contentGroupings

PostCategories & PostType

(e.g. blog post, video content, white paper, infographic, etc)

Example of content groups...

Use GTM

CSS

selectors

Use GTM

customVariable

script

to tidy data collected

How to test...

Remember to add 5 contentGroups

in GA settings 1. contentType

2. pageID

3. author

4. readTime

5. publishDate

Alternative method, if GTM changes take too long for IT dept to approve...

Manual ContentGrouping for

contentType is also possible

PageGroup Field Match value

homepage url ^/t5/BusinessNow-(.*)/ct-p/..($|\?)

site search url ^/t5/forums/searchpage/

blog post title .*/ba-p/[0-9]+

category page url .*/label-name/.*

404 page title .*Page Not Found.*

Manual ContentGrouping for

pageID is also possible