SearchLove London 2019 - Stacey MacNaught - Actioning Search Intent: What to Do with All That Data

Actioning Search
Intent
Stacey MacNaught
SearchLove London 2019

I run a (very) small SEO and content
marketing consultancy
@staceycav #searchlove

I am not good at coming up with
creative agency names:

Stacey
MacNaught
MacNaughtDigital

And I hired my Mum to do outreach
Stacey’s Mum

I’m also guilty of having loads of data that I
sometimes take literally no action with

Ever had data
paralysis?

I feel like that a lot with data

But with Search Intent, particularly
from mid 2018, it felt like this data was
the answer to a lot of problems.

Optimise for Search
Intent.

Structure for the user
journey and use
search intent to help
present the right
pages at the right
time

We felt that could be made more
actionable by applying it to specific
challenges we face.

We found 2 solid use cases where
search intent data has been used to
generate measurable improvements.

We analysed 10 websites and looked at
all search queries that had declined in
click terms (even if they were up
overall).

Order by number of positions lost
and focus on the ones that ranked
top 5 before losses

And then categorise by top level
search intent

Google’s take:
https://static.googleusercontent.com/media/guidelines.raterhub.com/en//searchqualityevaluat
orguidelines.pdf

We widely accept user intent
groupings like this….
Navigational Informational Transactional

DoGo Know
Not too dissimilar to Google’s take:

NavigationalUser wants to go to a
specific website.

Informational
User wants
information.

TransactionalUser wants to buy
something.

DoGo Know
But it’s not always clear:
Ambiguous

This is where we’ve been finding lots
of commercial sites dropping
rankings:

Seen rankings/traffic drops but no
drop in revenue??

Trigger words often implying
informational:

Trigger words often implying
transactional:

Cross reference with SERPs

End up with a list
like this.

Queries that
declined:

Go a step further and subcategorise
informational queries

“Know Simple”
Queries
Initial Solution
Queries
Solution
Comparison
Source-to-Cite
Queries
New Problem/
Opportunity

We had an issue on a site that ranked
top 3 for these:

Overnight: from 3rd to outside the top 100

”Ballet shoes,” and “girls ballet shoes”
(amongst others) unaffected.

Nothing on site had changed.

But that whole SERP had changed

It was an informational query for which
we were serving a transactional result.

Back to top 3 within 2 months without
page level link building

This hasn’t
worked for us
on all sites
Success equates to recovering a top 5 position

Other types of informational (shopping
led) queries this can work for

• Best {product} for {audience}
• {Product} Reviews
• {Product} vs {Product}
• Compare {products}

Sites on which we’ve had the most
success….

Retailers Stocking More than one Brand of
a Product Type

Service Comparison Type Sites

So we can recover any lost informational
ranking by creating a more informative
piece??

Did it previously rank
with a commercial
landing page? (E.g.
category page?)

Has the nature of
the SERP changed?

Better paid performance
=
happier client/boss

I’ve
experienced
this a lot.

The ballet shoe guide doesn’t
convert well on a first visit

It’s a similar story here with a
healthcare piece.

And 100+ other pieces of
informational led content we’ve
created

It’s naïve to assume there’s
enough brand recall after
someone reads an article that
they’ll come back of their own
accord and buy something

So we started working with the
paid search teams

We used content built out of
search intent analysis to create
remarketing lists

Data from campaigns run in UK, US and
Germany

The challenge: How many can
you get on that retargeting list?
(Minimum 100 required for display network
and 1,000 for search network)

Group content types for bigger
lists

Group by related product/service
rather than by content type

Audit existing
informational
assets on the
website.

Identify those
where user
intent fits into
one of those
informational
categories

Create new assets on relevant
topic/product/ service areas

Start with a simple (tool-free)
brainstorm/shout out session

Ask client customer service or
sales team for the common
questions/objections from
prospective customers.

Perform keyword gap analysis with
competitors or publishers who are
educating your potential
customers

Identify other relevant queries

Survey previous customers about
what drove their purchase.

Gather/Create all Assets

Group by product/service area

Work alongside PPC team to
create relevant ads

Run. Analyse. Refine.
Run. Analyse. Refine.
Run. Analyse Refine….

Work more closely with PPC team

https://www.searchenginejournal.com/scrape-google-serp-custom-extractions/267211/#close

https://www.aira.net/blog/how-to-automatically-categorise-keywords-for-the-buyers-journey/

https://moz.com/blog/revisiting-navigational-informational-transactional-search-post-pagerank

SearchLove London 2019 - Stacey MacNaught - Actioning Search Intent: What to Do with All That Data

SearchLove London 2019 - Stacey MacNaught - Actioning Search Intent: What to Do with All That Data

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SearchLove London 2019 - Stacey MacNaught - Actioning Search Intent: What to Do with All That Data