Google Ads “Other Search Terms”: Is 57-67% Hidden Spend Normal?

A keyword is what an advertiser targets. A search term is the search that triggered the ad. The Search terms report is where those two worlds are supposed to meet.

They do not meet completely. Google's current Search terms report documentation says the report contains terms used by a “significant number of people”. Queries without enough activity are omitted to meet Google's privacy standards. Their cost and outcomes can instead appear in the aggregate line labelled “Other search terms”.

That distinction matters. The report still totals the hidden group's cost and available outcomes. What you lose is the query string, the evidence needed to decide whether a particular search was relevant, should become a keyword, or should be excluded.

This is why the issue is more serious than a messy report. A business can be billed for a click, count its conversion and still be unable to inspect the search that caused either. Query-level optimisation becomes sample-based: advertisers work from the visible portion and infer what may be happening in the rest.

There is no official industry average. Google does not publish one, and the available studies use different accounts, campaign types, dates and definitions. The honest way to read the evidence is as a series of reference points, not a clean statistical distribution.

The earliest measured impact came from Seer Interactive. Before the September 2020 change, its accounts could attribute 98.7% of cost to visible query rows. Afterwards, visibility fell to 71%, roughly 28% hidden. Seer also found 20.4% of clicks disappeared from query-level reporting.

A contemporary Tinuiti analysis covered by Search Engine Land found desktop text-ad spend visibility dropping 24 percentage points, from 98% to 74%. No device-and-format combination in that analysis had more than 76% of September spend attributed to visible queries.

Later evidence is less standardised but shows how wide the account-level spread can be. A 2024 Search Engine Land contributor analysis put hidden clicks and spend near 40% in the data examined. The author's colleagues reported accounts ranging from 20% to 80%, but that was an informal peer check. In the same year, search-intelligence vendor Adthena reported a 51% average across accounts it reviewed, including one with £5 million assigned to “Other” over 12 months.

Our verdict is therefore deliberately narrower than “this is normal”. A figure of 57% is six points above Adthena's vendor-reported average; 67% is 16 points above it. Both are high. Both also sit inside repeatedly documented account examples. Treat them as a reason to decompose the account, not as evidence that something impossible has happened.

Google's stated reason is privacy. Ads Liaison Ginny Marvin reiterated in 2025 that the reporting threshold is based on sufficient volume across Google searches and applies to all queries. Google says Smart Bidding does not know or care whether a query will later qualify for advertiser reporting; it bids according to predicted performance.

That position is covered in the public exchange summarised by Search Engine Roundtable. The numerical threshold itself is not published, so an advertiser cannot predict when a particular query will move from hidden to visible.

The industry objection is commercial rather than semantic. Whether the privacy rationale is accepted or not, advertisers pay for traffic they cannot fully audit. They cannot add a negative against a query they never receive. Agencies cannot show a client the complete query ledger behind an invoice. And account managers have less evidence when deciding whether broadening, automation or a niche market is creating poor matches.

The defensible criticism is therefore not “privacy is an excuse”. It is that Google has chosen a privacy design in which the platform makes query-level ad decisions while the paying advertiser may receive only an aggregate. That creates a real imbalance of observability, whatever the original motive.

This is where the strongest headline and the strongest caveat arrive together.

In 2025, Taikun Digital analysed almost 14 million clicks across 933 campaigns and roughly $20 million in spend. As Search Engine Land reported, hidden queries had 52% higher CPCs and 44% lower CTRs in the combined data. Visible terms returned 5.60 times spend, compared with 2.59 times for hidden terms.

Read only that far and the conclusion seems obvious: hidden equals worse. But the segmented findings change the story. The same analysis found minimal visible-versus-hidden difference across non-brand traffic: 1.90x ROAS versus 1.88x, according to the detailed data reported by MediaPost. The much larger gap was concentrated in brand activity. Hidden brand terms had markedly higher CPCs and lower ROAS, leading the analyst to suspect that non-brand-like queries were entering brand campaigns. That is a plausible interpretation, not query-level proof because the underlying terms were hidden.

Tinuiti had raised a similar mix warning in 2020. Its report found higher cost per conversion on unattributed text-ad clicks, while noting that the difference could reflect which brand and non-brand queries remained visible.

The correct conclusion is conditional: hidden aggregate performance may be worse, sometimes dramatically, but it is not universally worse and the reporting threshold has not been shown to cause the gap. Judge your own aggregate against your real target and customer quality. Do not apply another agency's ROAS ratio to your account.

Four factors deserve investigation. None should be treated as guilty without account data.

1. Niche and long-tail demand

The threshold depends on query activity across Google, not on how often the query appears in one advertiser's account. A specialist B2B service, uncommon medical treatment or precise local job may produce many unique, low-volume searches. That creates a structural visibility problem even when the traffic is relevant.

2. Broader matching and query diversity

Broad and phrase matching can expose an account to a wider variety of searches, making it more likely that individual variants remain below the reporting threshold. A 2024 practitioner analysis found hidden share below 20% on the exact-match examples it examined and much more on phrase. That is an observed association from one analysis, not a law. Google's modern exact match includes close variants, so changing the label on a keyword does not force every query to become visible.

One public case makes the limitation clear: an advertiser using all exact-match keywords reported 80% of spend and 90% of conversions under “Other”. Google's response, covered by Search Engine Journal, was to use Search Terms Insights for aggregated categories.

3. Campaign and network mix

Brand, non-brand, Shopping, Google Search and Search Partners can produce distinct query distributions and unit economics. Segment them before making a recommendation. Search Partners may be a problem in one account and valuable in another; blanket advice to disable it is not evidence-led.

4. Performance Max is a separate reporting problem

PMax can serve beyond Search, so total campaign cost cannot be compared with visible Search queries as if all spend came from one query report. Google now exposes more PMax Search term information, including a campaign-level API resource, but the correct analysis still keeps PMax separate and uses its available Search reporting and category insights. For the broader control problem, see our Performance Max guide.

Community discussion is remarkably consistent on the pain, but much less reliable on the cure.

In a January 2026 r/PPC discussion about accounts above 50%, advertisers described similar visibility levels in real estate and other accounts. A separate 2026 exact-match thread described the same structural problem in a niche account. Older threads contain the same frustration: people can reconcile the money but cannot see what they bought.

On X, the 2025 Taikun analysis triggered a direct exchange between its author and Google's Ads Liaison. The analyst argued that the performance gap looked like monetisation of low-quality inventory; Google replied that the threshold was privacy-driven and pointed to the near-equal non-brand result as evidence that more segmentation was needed.

These conversations matter because they show the operational burden and the trust problem. They do not establish a reliable benchmark. Reddit contributors do not use a standard report scope, and an X argument cannot reveal a hidden query. We use community examples as evidence of practitioner experience, not as proof of cause.

• An API or script “recovery” method. Scripts can sum the visible rows, quantify the gap and analyse n-grams. They receive no secret version of the withheld strings.
• GA4 as a back door. Analytics can help validate landing-page behaviour and conversions, but it does not provide a complete paid-query ledger that Google Ads refused to expose.
• Blindly moving everything to exact match. It may reduce query diversity, but it can also cut valuable volume and does not guarantee reporting. Test the outcome; do not optimise for a prettier percentage. Our broad-match analysis explains where tighter controls can still help.
• Turning every insight theme into a negative. Search Terms Insights aggregates different queries into a theme. A relevant label may contain both good and bad searches, so use it to investigate rather than block automatically.
• Switching bidding strategy to reveal data. tCPA or tROAS can control outcomes when conversion data is sound. Neither changes the privacy threshold. Smart Bidding is a performance lever, not a reporting fix.
• Using Microsoft Ads as a mirror. Microsoft data may suggest useful negative themes for the same market, but its users, auctions and queries are not a record of what happened on Google.

You cannot inspect every hidden query, but you can stop the missing data from turning into an excuse for weak analysis.

1. Calculate the share from the right total

Add the cost of the visible search-term rows to the “Other search terms” cost, then divide Other by that report total. For example, £4,300 visible plus £5,700 Other equals £10,000; hidden cost share is 57%. Keep the date range and filters identical.

2. Separate standard Search and PMax

Do not divide visible Search terms by all PMax spend. Analyse each reporting surface on its own terms, then connect the findings at the business-outcome level.

3. Split brand and non-brand

This is the first protection against a misleading average. If the hidden performance problem disappears in non-brand, investigate brand campaign matching and structure rather than condemning the entire hidden bucket.

4. Compare the hidden aggregate with the real target

Suppose the £5,700 Other segment produced 38 conversions: its CPA is £150. If the account target is £125, the hidden aggregate is 20% above target. That supports investigation, not a claim that all £5,700 was wasted. For lead generation, use qualified or revenue-producing leads where possible.

5. Mine visible evidence and privacy-safe themes

Run n-gram analysis across visible terms to find repeated costly concepts such as jobs, research or the wrong service. Review Search Terms Insights alongside it. Google says those categories take account of terms omitted from the standard report. Both sources create hypotheses; only visible, reviewed evidence should become a specific negative.

6. Test one controllable lever

Test the segment implicated by the data: a reviewed negative set, Search Partners, location, device, schedule, match type, or campaign structure. Define the expected CPA/ROAS movement, a volume guardrail, a learning period and a rollback point. Avoid changing bidding, budget, match type and negatives at once; you will not know what worked.

7. Trend visibility and outcomes together

Recalculate the percentage on comparable periods, but make the decision on commercial outcomes. A fall from 67% to 40% is not a win if qualified-lead CPA rises. A stable 60% may be tolerable if the hidden aggregate meets target and customer quality holds.

This same discipline underpins our Google Ads audits and wasted-spend analysis: quantify what the evidence supports, then isolate the lever before changing it.

More than half of Search-term-report spend being hidden should not be waved away. It limits negative-keyword work, weakens explanations and transfers evidence from the advertiser to the platform. The concern is legitimate.

The evidence does not establish that either account is a statistical outlier because no representative distribution exists, and it certainly does not prove that 57% or 67% was wasted. Published accounts extend well above those levels. The strongest performance study also shows why segmentation matters: an alarming overall gap became almost no gap in non-brand ROAS.

So treat “Other” as a measurable line item with an unmeasurable interior. Compare its aggregate outcome with the target. Split the account before drawing conclusions. Use the visible sample and Search Terms Insights to form hypotheses. Then test only the levers you can control.

That will not restore the complete query report advertisers once had. It will, however, produce a decision you can defend to a client and a campaign you can manage without pretending the black box is transparent.