google ads hiding search terms data

The Impact of Google Ads Hiding Search Terms Data (Case Study)

Rory Young

Rory Young

September 17th, 2020

Rory is the Managing Director of Clean Digital, a PPC agency in Edinburgh that develops strategies to maximise value from all paid media spend via human-based automation.

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Google recently announced that they will be updating how advertisers can access their search terms, with the reports now showing only “terms that were searched by a significant number of users”. This is an important development for search and shopping advertisers on Google Ads, as search terms give a great insight into the relevancy of the traffic matching to an advertiser’s ad.

The change in search term reporting took place in “September 2020”, with advertisers across the world receiving an alert in their accounts on September 2nd 2020. Google has previously given more notice on major changes to it’s Google Ads platform, so advertisers and agencies around the world have been scrambling to understand the changes and how to optimise within the new report restrictions moving forward.

Why Is This Change Significant?

Put simply, if advertisers have less information on the search terms they are paying for, then they have fewer options available to improve their account.

Stefan Neefischer outlined the impact succinctly in his LinkedIn post. Advertisers will still be able to research & add in new keywords “normally” (as this only really relates to queries with significant volume), the impact will be most felt on a negative keyword strategy & matching control perspective.

For negative keywords, advertisers will now have less data to stop paying for searches that are irrelevant to their campaigns. It’s never been viable for advertisers to review every significant search query.

However, more advanced PPC strategies include deriving negatives based on consistently appearing “themes” (see nGrams below!), and it will simply take longer for these themes to appear if Google is withholding some of the data. This is very significant, as this extra time can translate to thousands of ad spend in the bigger accounts & more expensive verticals.

Regarding matching control, now that more queries are hidden, advertisers simply don’t know what they are matching to. This is particularly important right now given Google has already taken steps to broaden the reach of “Exact” in recent years (per the excellent post from Greg Finn), so if this continues and we aren’t aware of the queries an advertiser is matching to has serious considerations.

Are advertisers now appearing on competitor terms that we weren’t previously? Has the intent of the original keyword now morphed into a product/service not offered by the business?

What Is The Impact of Less Search Terms Data?

Over a week after the announcement, we can already get an excellent idea for the extent of the change across search and shopping campaigns. The below stats & graphs are from the 60+ advertiser accounts under my agency’s (Clean Digital) MCC account.

For search keywords, our accounts have had “hidden” queries increase from 1.2% to 20.9% of total clicks received, an increase of 1,741%! Costs have risen from 1.6% to 29.90% (a 1,868% increase), highlighting that hidden queries have higher CPC’s than those present within the search terms report.

google search hidden search queries

When drilling further into specific campaign themes, we found that brand term campaigns (e.g. Nike bidding on keywords containing “nike”) across our MCC have had an increase in hidden query clicks from 0.31% to 5.84% pre/post the Google Change.

Whilst this equates to a similar % increase in hidden queries (1800%), there is a much larger % increase in CPC’s on the hidden queries for brand.

The hidden brand queries are coming in 456% more expensive than the tracked queries since the change. This is obviously a massive concern for advertisers, as it will make brand bidding more expensive longer-term whilst it also suggests that the query matching may be even less relevant (i.e. non-brand) than previously.

On shopping ads, there was a higher observed base of hidden queries prior to the 1st of September, although we’ve still observed an increase in hidden clicks from 10% of the total to 36%.

google shopping hidden search terms

How To Check The Impact Of Your Account

If you are a Google Ads advertiser, the easiest way to check the impact of the change is to navigate to your Search Terms report and compare the “Total: Search terms” line item with the “Total: Account” line item.

It’s worth noting that this method can’t be used if you are running any display/Youtube activity, as those campaigns will be contributing clicks but not search terms.

google search terms report

In this example, the Search Term report tracked 9,476 from 10,678 Account clicks for the 24th – 31st August, meaning the Search Terms tracked 89% of total clicks (i.e. 11% were hidden). In the same period after the change, the Search Term report tracked 6,421 of the 11,181 total clicks, meaning the Search Term track rate dropped to just 57% (i.e. 43% of total clicks have been hidden).

If you’d like to see more data from your analysis (e.g. by campaign type or by day) or if you’d like to map over multiple accounts, I’d recommend setting up the two reports definitions below.

 Search Term Report Columns

search terms report column

Campaign Report Columns

campaign report columns

Using some pivot/vlookup magic in Excel will allow you to tell the impact across a number of segments, of course, you can add in other metrics here (e.g. Top of Page Impression Share) as needed.

How To Optimise Ads With Less Search Terms Data

The fundamentals of search term optimisation should be the same as they always have; review the search term data regularly and make significant decisions based on theme data.

My colleague Aitor has written a piece on general search term optimisation, whilst there are several scripts online that can help with turning search term data into meaningful nGrams (e.g. this brainlabs one!).

nGrams break down search terms into “themes”, meaning advertisers can distill significant search term datasets into actionable insights.

In the example below, the advertiser can easily see how search term themes are contributing toward their overall ROI target.

ngram diagram

Actionable optimisations from this example could be:

Optimisation 1

Break out “online” into its own ad group (e.g. “+nike +trainers +online”) given it has stronger ROI than average. Closer control on both bidding & matching can be assigned here.

Optimisation 2

Analyse why “pink” is performing poorly, and consider a restructure or negative addition based on findings.

If we establish it’s because the website never stocks pink trainers, then add “pink” as a negative keyword to save on wasted media spend.

It could be that “pink” trainers are available on-site but just not obvious to the users based on the general adcopy/landing page used. If this is the case, restructuring this theme into its own ad group will give a “fairer” view on the specific nGram performance

We’re taking the analysis one step further, and with the help of some python code, we’re pulling a Fast Moving Query analysis across our nGram activity. This report is essentially an nGram report pulled by day, with formulae in place to identify which themes have had the biggest drop-off.

This is helping us to establish what “themes” of queries have had a drop in activity, and whether this is significant enough to alter our known query optimisation decisions.

In the example below, we can establish the themes that have had a greater variance than the 30% observed across the entire account. Using this, we can assume query themes included Reviews/Official make up a proportion of the “hidden” queries given we’re missing 55% of clicks on these themes, whereas the “nke” misspell has had a 75% drop-off.

search term queries before after

Using this analysis, I’d recommend further breaking the nGrams into “themes” (e.g. all misspells like “nke”) and decide on the best course of action based on the previous & new performance.

If you find a lot of the suspected hidden queries have had low performance, then you may want to add these as negative keywords to prevent wasted spend.

Conclusion

Although there has been a decrease in data from the search terms report, you can still make significant optimisations to campaigns with the limited data available.

nGrams are a great way to define and track the keyword themes of a campaign and can reveal which groups are both under and over performing.

Although you might not be able to see every long-tail keyword that is triggering your ads, you can easily set up negative keyword rules to stop underperforming themes from triggering them.

By doing this, you’ll also capture the long-tail keywords which Google isn’t publishing in their search terms report anymore.

Although it might not be as perfect as before, unless Google decides to backtrack on its search terms data removal, there are very limited strategies available!

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