It’s common for marketers to target individual keywords when creating and optimizing content, but topic clusters are an even more powerful way to do keyword research. Not only will you get results from the individual terms you choose to target, but you’ll also build a powerful collection of relevant, associated keywords to drive more traffic to your website. Want to learn more about topic clusters and how you can implement them in your content marketing and SEO strategies? Watch our upcoming webinar with Dave Snyder, CEO and Founder at CopyPress, along with Liz Groth, Partner Development Manager, as they talk through how to build a better keyword universe for your marketing efforts. You’ll Learn: - Why you should consider topic clustering as a better approach in your content marketing. - How topic clustering reveals hidden keywords you may not have considered. - How to improve your current keyword research through topic clustering. Ready to try this unique approach and start improving your search results? Watch now and discover how topic clustering can help level up your strategy.

2. The Problem
Search focused content production is still largely focused on traditional keyword
research.
Why is this a problem?
Bad data
Limited scope
Focus on a single keyword
rather than intent
Image via Unsplash by austindistel

3. Linear Keyword Research
I call traditional keyword research “linear keyword” research.
As SEOs, we often approach keyword research based on lists of keywords,
sort them by volume, and work our way down the line.
This gives us a miopic view of the content’s real potential value.

4. Poll: Are you happy with your current keyword
strategy?
● Yes, it's been working well.
● It's okay, but there's room for improvement.
● Not really, the ROI isn't there.
● What keyword strategy?

5. Linear Keyword Research
Let’s dig into what I’m talking about using a term we all are familiar with:
“Search Engine Optimization” has a total search volume of 22,200 searchers per
month according to SEMRush.

6. Linear Keyword Research
However, “Search Engine Optimization” shares 7 of 10 first page SERP results with:
So now our TOPIC, also called a “bucket,” is actually worth 23,500 monthly searches
in volume.
22,200 searches + 1,300 searches = 23,500 total monthly searches

7. Linear Keyword Research
If we continue, we see that “SEO” has over 165,000 searches a month, and shares 5
of 10 results with the other keywords in our bucket.
This means our total BUCKET value is now up to 188,500 visitors.
23,500 searches + 165,000 searches = 188,500 monthly searches

8. Linear Keyword Research
This process could go on and on…

9. Linear Keyword
Research
Here’s an example of linear
data using the seed term
“employee contracts.”
All of the highlighted terms
seem unique enough, but
this is only one topical
bucket.

10. Linear Keyword Research
Here is the total volume of the “employee contracts” topical bucket.
This gives you a much better view of the potential to create content and the ROI
you can expect to see from your campaign.

11. Poll: What is the biggest challenge you're
facing with your keyword strategy?
● Lack of time or internal resources
● Unsubstantial website traffic/ROI
● Not enough experience
● Poor direction or execution

12. Issues With Linear Keyword Research
Results in waste and redundancies in your content strategy
Leads to inaccurate ROI modeling and forecasting
Requires significant labor to scale correctly
Increases the possibility of poor data accuracy from third party tools

13. The Solution: Clustering
Minimizes waste and gives you clear ideas on how to group
keywords into content pieces
Results in better forecast modeling that takes into account all
of the potential traffic each piece of content can generate
Decreases the cost of keyword research significantly from a
labor perspective
Dilutes the potential effects of poor third party volume
accuracy

14. Keyword vs. Cluster
Keywords Clusters
Linear focus
Hard to estimate traffic/ROI
Negates NLP and other
categorization/ranking factors
Holistic focus
Easy to estimate traffic/ROI
Based on the way current
algorithms work

15. Low-Tech Approach To Clustering
CopyPress approaches clustering using our proprietary tool Thematical,
which utilizes machine learning to cluster keywords based on SERP structure.
However, you can build low-tech clustering methods using tools like SEMrush.

16. Poll: What tools have you used to aid in your
keyword research?
● SEMrush
● Ahrefs
● SpyFu
● Other - Let us know in the comments!

17. The High-Tech Approach To Clustering
The issue with creating fully bucketed pictures of search volume possibilities is scale.
When we began using this method at CopyPress, we tried to employ straight-forward
Python scripting to compare keyword based SERP data sets.
However, the job was simply too massive to scale for enterprise projects. Millions to
billions of comparisons need to be made to identify similarities across keyword data
sets.
This is where machine learning comes in to play.

18. The High-Tech Approach To Clustering
We use a machine learning model similar
to what Netflix uses to help people find
new movies and shows that are
comparable to what they’ve previously
watched.
CopyPress collects keyword data based on
the seed terms and competitors the user
selects. Then, we gather the top SERP
results for every keyword. Image via Unsplash by freestocks

19. The High-Tech Approach To Clustering
Next, we run the top SERP results through
our models to determine how similar
Google believes these data sets/terms are.
Finally, we use a secondary machine
learning process to bucket related terms
based on a scale that the user controls.
Image via Unsplash by fatosi

20. Seed
Terms
Domain
Comp
KW
Data AWS
KW
Database
URL Data
For KW
Database
❖ Top 10
URLs
ranking for
each term
Level 1 - Logistical regression across 250 AWS servers to find high level similarity of
keywords per Google rankings
Level 2 - Logistical regression across 250 AWS servers to find deep level similarity of
keywords per Google rankings
Bucketing algorithm - Takes similarity scores and breaks them into relevant buckets
Sample Clustering Tech Stack

21. Poll: Who is in charge of your keyword
research and strategy?
● Dedicated internal team member(s)
● Third party or agency
● No one manages our keyword strategy
● I do it all myself

22. Building Your Own Model: Data

23. Building Your Own Model:
Machine Learning
https://www.kaggle.com/

24. Q&A