The Power of Ecommerce Filters - A Dunelm Case Study

The Power of
Ecommerce Filters:
A Dunelm Case
Study
Sam Gipson
Dunelm
@samgipson

Dunelm (Soft Furnishings) Ltd
Case Study:
Optimising
ecommerce
filters on
Dunelm.com

Ecommerce
filters:
Aid customers in
narrowing the
products
appearing on
product lister
pages (PLPs)

PLP (260 results)
.
Filtered PLP (86 results)

Organically they can rank really well for medium-
long tail searches

Drive traffic via
lower volume,
high converting
keywords
= HUGE
opportunity
SEO’s favourite chart.

2020!
National lockdowns
Regional lockdowns
Huge demand shift from
offline to online

Just launched
‘click and collect’
in time for
national
lockdown

Dunelm’s online visibility was going
from strength to strength
Source: AHREFs

Our filtering
system was
completely
blocked from
being crawled
With no way to
optimise filter
pages

TOP TIP:
Install “Robots
Exclusion Checker”
Chrome Extension
It visually show
whether your filter
system is allowing
Googlebot or not
Free Google Chrome Extension

“black coffee table”
12K searches/pm
Position 1
Despite this, our generic PLPs were ranking well

“grey tv unit”
7K searches/pm
Position 1
Despite this, our generic PLPs were ranking well

Compared to our competitors
we were over-achieving in the
rankings

Our generic PLPs were
ranking higher than
competitor “filter” pages
that provided a better
customer experience

TOP TIPS
 Become obsessed with
Google’s results
 Simulate the customer journey
 Who has the best landing page
experience? How can you
improve yours?

Our objective:
Build an SEO optimised
filter solution

45% of SEOs let Google
index all filters or no filters
at all
Not many sites have
granular control over their
filter indexation

Opening all filters for indexation
Links

Links
Negatives:
• Potentially thousands or millions of
URLs indexed
• Huge page cannibalisation issues

Links
Negatives:
• Google wasting time crawling low-
quality pages
• Negative overall impact on organic
visibility

Blocking all filters
Links

Blocking all filters
Links
Negatives:
• Inability to target medium-long tail
terms at scale
• Competition will outrank you
• Losing out on market share

Other sites do something in between
Links
With some filters
being indexable..

..and others being
excluded
Other sites do something in between

But popular exclusion methods
for low-value filters are far from efficient
Robots.txt
Meta Robots
“Noindex”
Canonical tags
Rel =
“nofollow”

Because URLs are presented to Google
that must be “managed”
 Relying on Google to pick a Canonical
 Index bloat
 Wasted crawl budget

So, we set about trying to
implement a more efficient
filtering system

Filters we DO want Google to discover & crawl we
server-side render crawlable links

 Crawlable <a href> links
 Clear anchor text
 Short, descriptive URL
What this looks like in practice

For filters we DON’T want Google to discover we use
client-side JavaScript for functionality

What this looks like in practice
 Filters are JS buttons
 No crawlable links
 Hash URLs used

Back-end: SEO Tool with new filter functionality
We wanted to
control exactly
which values
Google could
‘see’ and index

- H1s
- Copy snippet
- Page Title
- Meta Description
Customisable on-page elements

All Meta Data
managed in our
internal SEO tool

Meanwhile…
Remember
this?

The effectiveness of Dunelm’s old CLP & PLP
strategy was starting to fail
Source: AHREFs

Technical
Solution

Nearly there, right?

3 main challenges we had to overcome

1. Structural
issues that
would impede
the scaling of
categories

• SEO team advised trading team
on suggested category structures
• Factored in range expansion
• Needed to be scalable and avoid
future URL changes
Architecture Changes

2. Internal product
attribute data not
aligning with how
customers search

Educating internal
teams to use
correct values, not
“internal” styles or
types
Keyword Research & Attribute Review
Mapping out
how customers
search for our
products
Breaking that
down to attribute
and value
granularity

3. Missing product
data

Working with suppliers
We had gaps
in our
product data
Getting suppliers to
classify products based
on styles or types from
keyword research
Working with
suppliers to fill
missing
information

Reattribution Project
• Dedicated project to clean up
and optimise our data
• Process involves SEO and 3
other internal teams
(+suppliers)
• This is an ongoing project

15 kickoff meetings
with commercial
directors
25 traders trained
in SEO
Internal PR and Training was critical
Development teams
trained in SEO
Regular reporting
to leadership team

Results so far

Launched on 1st
February
900 pages live
~30% through
project

“white wardrobes”
Before filters After filters
PLP: position 14
Filter: position 2
Ranking Results

“black coffee table”
Before filters After filters
PLP: position 8 Filter: position 2
Ranking Results

We’ve stopped traffic decline
as we offer customers a
better experience.
We now organically rank for
over 1.1m keywords.
Still a lot more work to be
done!

Thank You.
@samgipson
linkedin.com/in/samgipson
samgipson.com

The Power of Ecommerce Filters - A Dunelm Case Study

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