Follow up on the latest improvements and updates.


In Workbench > Keywords
All competitors' keyword ingredient data is pulled from SEMRush. Before, these data were sorted under one source like ' semrush'.
In the latest update, you can check which domains or competitors rank for those keywords. It is helpful as you could create a filter and identify the list of keywords dominated by a specific competitor.
Choose Add Rule. Chose source in the drop-down menu = 'competitor's URL'
Screenshot 2023-10-10 at 16
What's Changed?
Previously, you faced an extensive list of ingredients when customizing your Workbench view. Though comprehensive, this long list could be quite overwhelming.
To enhance user experience, we've reorganized these ingredients into distinct categories, each serving a unique purpose.
Where to find it?
This update can be accessed in two ways:
Navigate to
to determine the default ingredients that will appear in your Workbench.
Screenshot 2023-09-21 at 14
Alternatively, you can also directly modify the displayed ingredients from any Workbench view by clicking on the "Setup Column" icon located at the top-right corner of the datatable. For example, under the Keyword Workbench view, you can find 3 groups: Keyword, GSC, and Competitive.
Screenshot 2023-09-21 at 10
Key Features:
Toggle Groups
: Easily include or exclude a set of ingredients by toggling the group on or off.
Individual Control
: For more detailed control, you can easily click into each group to manage specific ingredients. For example, under the Keyword group, you have keywords, CPC, volume, and source. This makes the entire process more streamlined and intuitive.
Screenshot 2023-09-21 at 14
We hope this update makes your Workbench experience more efficient and enjoyable!
We just released a new internal linking relevance model that uses updated embeddings based on transformers (the T in GPT). We tested out a bunch of different transformer models to find the ones that did the best.
Our model assesses link relevance using two different approaches:
  • AI embeddings
  • SERP overlap
This new approach promises significant improvement over our traditional embedding-based relevance model. To activate it, navigate to:
Settings > System >
EMBEDDING_VIA_TRANSFORMER demand-based content helps you reduce duplicate content by increasing the uniqueness, freshness and depth of your category pages:
  • We create a universe of search intents: all the search engine keywords where the searcher has the intent to transact in a certain domain, such as automotive, clothing, electronics or homeware.
  • We cluster keywords into topic groups using our knowledge graph and we pair the universe of transactional unbranded intents with information about your pages and product listings.
  • At a page level the platform can see both the main keywords a page targets and all of the longer tail variations.
  • We use our knowledge graph to find the common user needs for which users search and turn these into language, or what we call content entities.
For instance, the
content entity finds the three colours with the greatest total search volume from amongst all the keywords with the page needs.
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These types of entities form the heart of our demand-based content. However, until recently, it was sometimes hard to know why the platform chose certain content. Not any more. Now you can quickly check which keywords and volume were used for each entity on any page. Just click on the inspect icon from the main content page to see zoom in to each entity.
For instance, how would the platform reach a decision to create the answer "The most popular colours for an Abarth are grey, yellow and black" for the
Just click on the "inspect" icon image
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Here we can see that the platform found a total demand of
  • 1,490 for grey across 39 keywords
  • 1,240 for yellow across 20 keywords
  • and 1,140 for black Abarths across 35 keywords
It used this data to create the answer "The most popular colours for an Abarth are grey, yellow and black".
This new inspection functionality lets you into how the content is created for any of your entities on any of your category pages.
Related Searches is a product feature which helps improve internal linking for enterprise SEO.
Links, whether they be internal or external, are one of the most important signals to Google and other search engines. Links indicate which page is the best answer for a search intent.
Many different keywords can express the same search intent, so we cluster keywords accordingly. Total demand is the search volume of an intent.
We cluster keywords into sub-intents and show the total demand for each. One way to think about search intent is all the keywords for which a page might rank.
We check for which sub-intents a page does not get much traffic and where the page does not target that sub-intent. These
opportunity intents
can add incremental unbranded organic traffic.
When we link through a page, we generally use the main intent as anchor text to link. Pages with more demand get more internal links. For a proportion of these, we also use opportunity intents.
We should take the intent of the main keyword of a page as the intent of the page. When we link, we link with anchor texts which are distributed across the main keyword and opportunity keywords.
The main intent is still the dominant way users think of a page, but this extra anchor text diversity increases the number of pages which rank for lots of higher demand intents.
Deduplication is a key feature for We list out the clusters of pages which answer the same user need and for each cluster of dupes, choose the best page. When we integrate with our clients, they typically add a 301 redirect between a duplicate and its new canonical. In this way we get more 'juice from the squeeze': more traffic from less pages. Our goal is to have one page for each search intent. A search intent can be expressed by 100s or 1,000s of keywords (check out our demo of categorising keywords into a search intent).
In this feature we made two updates:
  • We grouped pages with the same intent instead of listing which pages matched which keyword,
  • We used our new machine learning classifiers to match pages and keywords to intents, and expressed these intents as entities in our knowledge graph.
There are also two big advantages:
  • We can find pages with completely different names, pages which miss out superfluous words, pages which use synonyms and pages with misspellings
  • Since a page often targets many keywords, it could belong to many keywords, but you can only redirect to one canonical page. This can no longer happen.
For instance,
  • volkswagen mk1 golf
    for automotive in the UK:
  • chesterfield zetels
    for homeware in Belgium
  • bmw x5 7-seater
    for automotive in the UK:
  • dames t-shirts
    in clothing in the Netherlands:
  • gucci riemen
    for clothing in the Netherlands


Pages to be's SEO deduplication and pages to hide features clean up duplicate search pages and pages which don't answer a need search engines users have. This is all subtractive, like the first stage of SEO should be: we're helping you focus on getting more wood behind less arrows to concentrate your visibility. Our pages to create feature finds sought-after intents for which you don't have a page, but do have inventory and could rank. This expands your coverage so that your content can be found more users.
In our Related Searches product we link from one search, browse or other category page to another. The pages to which we link are not your
as is
pages, but your
pages to be
: a combination of the
  • Canonical pages from deduplication
  • Pages with sufficient demand
  • New pages which are going to drive incremental transactional unbranded traffic
We combine these to maximise demand across the minimal number of relevant pages without creating duplicate content.
By listing these out, we can tweak the criteria to have a page to be. For instance, you can set the minimum total demand for a page or the minimum number of listings a page can have, and can analyse the impact of these choices.
We used to dedupe only by intent. This would find the same intents expressed with different words or at different places on the site. However, it doesn't capture all duplication in search, browse or other category pages. You can also have different intents which have the same content.
To see why, imagine you have 10 red Volkswagen Golf 7 GTI 2018 listings and no other 2018 golfs. You could have pages about
golf 7 2018 for sale
buy vw gti 2018
used red golf 2018
golf gti 2018
and despite some small difference in H1, Title and URL all of the non-template content on the page would be identical. Google will have to put in some work to work out which page to rank.
Now, we identify all pages with the same listings, work out which page should be the canonical and pass that over to you for redirection just like we do pages with the same intent.
For instance:
  • king jumpsuits
    in clothing in the Netherlands
We now integrate Google Search Console to get you the most accurate ranking, impressions and click data for your site. Previously we got some of this data from SEMrush, but we are now able to show much fresher results.