New Search Term Insights in Google Ads

Last week, a new segment option showed up in the keywords view: ‘Search terms match type’.

This doesn’t relate directly to the match type of each keyword, but to the relationship between keyword and search term, and it segments performance – from the impression level upwards – by each stratum of that relationship.
This new breakdown is more useful and more significant than perhaps it first appears…

Keywords and Search Terms (Hammers and Nails)

The keyword-search term relationship has changed over the years – as has the importance attached to the relationship – but while keywords aren’t what they once were, they’re still both the most useful and most precise unit of target segment we have at our disposal in Google Ads

And while keywords are the tools we work with, it’s search terms that really perform the function we’re interested in. They are the ‘nails’ to the ‘hammer’ of our keywords.

We used to have far more control over the selection of search terms – with exact match being Exact; Phrase match – in its early days – also using stringent rules about the presence and order of search terms, and a very cavalier Broad match… (Though at least we know where we stood with it.)

Now all match types have widened; the distinction between a Phrase and Broad is less clear; the relevance of Broad match seems to have overtaken that of Phrase, and the relationship between search term and the keyword has become tenuous.

‘Don’t worry about that relationship’ – Google would urge us – ‘worry about the actual value of the auction’ – value that Google can determine – so they say – based on countless other signals that they feel no obligation to share with us as targeting tools or – in some cases – even as information.

But even if we accept the downweighting of the search term as the essential marker of how valuable a given auction is to us, it’s still important to know what the relationship actually is between a keyword and the search terms it triggers.

Without that knowledge, we simply don’t know what ground a keyword is covering… whether that ground is unique to it… how many (if any) of its clicks are from users searching on the phrase we have in mind, and that we are therefore focussing on in the associated ad text, landing page text and so on…

Four Existing Methods of Analysis

We have always had various methods of assessing that relationship.


Most commonly, we do so through the Search Terms report.

NB it’s worth adding ‘keyword’ as a column to this report, to see which keyword triggered each search term… This allows us to identify keyword crossover, and how each specific keyword is casting its search term net.

An underrated improvement came our way in early 2021 when Google determined that an exactly matching keyword would take precedence for a given search term, and the Exact match instance of that keyword would always have highest priority, obviating the need for internal negatives to make sure that the most relevant keyword attracted its matching search term (rather than potentially losing out on Ad Rank to a less relevant keyword).

See Google’s full order of keyword matching priority here:

This tidied things up a lot for us.


In the reporting section, we’ve been able to break down performance by whether a search term was an exact match for one of our keywords (regardless of keyword match type) or not.

With a little help from pivot table we could aggregate this data across an account:

(full instructions on how to run and leverage this analysis in part three of this blog post).

The reporting section now features the Exact/Phrase/Broad search term breakdown that has recently appeared as a segmentation, giving a simpler, more granular view (with the slight blurring that ‘Exact’ in our new dimension includes both exactly matching search terms, and close variants.)


A related type of analysis was the Lin Rodnitzky ratio, which specifically sought to establish the proportion of spend going towards converting search terms.

This served as a useful tool for establishing how tightly or conservatively set up an account was, based on the proportion of its spend that was going on ‘proven’ search terms versus spend that was going on ‘unproven’ search terms don’t for either lax or experimental reasons.

The usefulness of LR ratio analysis has been somewhat compromised by the decrease in search term visibility, but if you’re interested, see my blog post and video on how to use it.


More recently, PPC Predict has produced a tool that uses a (huge) list of never-converting search terms from across its bank of data, to apply as negatives. In an exceptionally clever model, each new account linked to the tool both benefits from that overall bank of data and adds to it…

The early signs of conversion rate uplift from BETA testers (including me) look like a resounding argument for the search term still being the most essential variable in determining success and failure – counting against the ongoing implication from Google that the words in the search aren’t where we should be putting our attention these days…

So while search term analysis is alive and well in various forms, with this new option, we have a simpler, clearer and more accessible breakdown than ever before, of keyword expansion behaviour and its effects in our accounts.

How Does the new Segmentation Work?

The ‘Exact’ row in the breakdown includes both the ‘Exact’ and ‘Exact (close variant)’ matches that we see in the Search Term report… and should be the only row populated under an Exact-match keyword, as above.

The ‘Phrase’ row shows those matches that were made under Phrase match rules but not under Exact match rules…. While the ‘Broad’ row shows those matches that were made under Broad match rules but would not have been made by Phrase or Exact.

Note how a Broad match instance of the same keyword as the one shown above has a mix between Phrase-matched and Broad-matched search terms with – in this case – almost all coming under Phrase match behaviour…

On the other hand this Broad-match keyword (which does not have an Exact-match counterpart) shows a much more even split between the search term match types, and a very text-book difference between them in terms of CTR, CPC, conversion rate and CPA…

…but this pattern is by no means universal. In this example, Phrase matches are seeing by far the highest conversion rate, while broad matches are outperforming phrase on CTR.

The breakdown of search term match types varies considerably, and will depend on: other keywords present; negative keywords in place; bid strategy (which influences how much leeway and what signals are available to Broad match keywords) and so on.

And What Is It Good For?

This new segmentation should prove useful for:

• Analysing how your keywords are working – how far are they expanding and with what success

• Assessing how tight a control you have over search term matches with your current setup

It’s increasingly debatable how important and valuable it is to have that right control… But there are certainly advantages to keeping a good handle on which specific search terms are actually triggering your ads, and what ads they’re triggering.

These reports can also inform our view of just how beneficial that control is… Are the exact matches performing considerably better than expansions, or not?

• Deciding what match types to employ or to change, on the basis of how much and how successfully they are expanding.

Search terms matter. Their relationship to keywords is all-important for our ability to use them.

So if we banged our desks at the reduction of search term visibility, we should raise a glass at this new-and improved window onto keyword-search term interaction.

It streamlines our analysis, and gives us a better understanding of keyword expansion and its impact on our campaigns.

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