How do I stop Performance Max from wasting spend on mobile game apps?

You must exclude mobile app categories at the account level, as Performance Max explicitly bypasses standard campaign-level placement exclusions. Google's algorithm actively routes inventory to mobile games because accidental screen taps generate extremely low CPCs, padding the campaign's perceived engagement metrics while killing actual lead quality. The Placement Leakage Audit calculates your specific App Placement Wastage Rate and generates the exact account-level exclusion list required to sever this traffic.

How can I see exactly where my Performance Max ads are showing?

You must utilize the Google Ads API or custom script reporting to extract placement data, as the native UI aggregates this into useless high-level summaries. PMax deliberately obfuscates placement-level performance data to prevent advertisers from manually blocking low-converting network properties, limiting you to basic impression counts. The Placement Leakage Audit extracts these PMax Shadow Placements to reveal exactly which URLs and apps are draining your Smart Bidding budget without yielding conversions.

Why are my Google Ads Search campaigns getting Display Network clicks?

Your campaigns are likely suffering from quiet auto-enrollment into the Google Display Network (GDN) expansion and Search Partners. Google opts new Search campaigns into these expanded networks by default to maximize eligible inventory, resulting in bottom-of-the-barrel display traffic and click fraud that masquerades as search intent. The Placement Leakage Audit exposes your Network Dilution Penalty to show precisely how much budget was bled into non-search placements over the last 30 days.

Why am I getting leads from foreign countries when my location targeting is strictly US?

Your location settings are likely set to 'Presence or interest in' rather than 'Presence,' allowing Google to serve ads to users outside your targeted region who show search interest. This setting combined with expanded network placements often results in overseas bot farms or low-quality display networks capturing your budget through spoofed location intent. The Placement Leakage Audit calculates your Geographic Leakage Variance, instantly flagging campaigns where off-target locations are eating your domestic budget.

Why does my Display campaign have a high CTR but a 100% bounce rate?

You are experiencing placement fraud from malicious mobile apps or hidden iframes that force accidental clicks without rendering your actual landing page. These placements exploit the CPC model by generating massive click volumes, forcing Smart Bidding to bid higher on them while Google Analytics registers immediate user abandonment. The Placement Leakage Audit isolates these fraudulent sources using a Click-to-Session Discrepancy metric, pinpointing exactly which apps and sites are stealing your spend.

How do I stop Google Ads from bidding on parked domains?

You must proactively exclude the specific 'parked domains' content category in your account settings, as Google includes them in network inventory by default. Parked domains offer zero content value but generate massive volumes of accidental clicks from users mistyping URLs, which drains your daily budget on zero-intent traffic. The Placement Leakage Audit reviews your Content Exclusion Deficits to ensure parked domains and other zero-value categories are permanently blocked across your account.

Why is my YouTube video campaign only showing on kids channels?

YouTube's algorithm prioritizes kids' content because toddlers on shared devices generate the highest engagement rates and cheapest CPVs on the platform. These accidental clicks feed false positive signals to Smart Bidding, which then creates a feedback loop heavily biasing your delivery toward gaming and nursery rhyme channels rather than your actual demographic. The Placement Leakage Audit quantifies your Juvenile Placement Ratio and cross-references your delivery against a 50,000-channel blocklist to stop the bleed.

Can I use campaign-level placement exclusions for Performance Max?

No, Performance Max ignores campaign-level placement exclusions and requires you to apply negative placement lists at the account level or MCC level. Google designed PMax to operate across all channel inventories fluidly, meaning traditional campaign-specific guardrails are bypassed in favor of algorithmic freedom. The Placement Leakage Audit highlights your Vulnerable PMax Campaigns and provides a consolidated, account-safe exclusion file you can upload directly.

What are MFA (Made for Advertising) sites and how do they drain Google Ads budget?

MFA sites are low-quality, ad-stuffed webpages designed solely to arbitrage cheap traffic and harvest programmatic ad spend. Because these sites engineer high click-through rates and artificially inflate time-on-site metrics, Smart Bidding algorithms misinterpret them as high-intent placements and aggressively bid for their inventory. The Placement Leakage Audit flags your exposure using an MFA Identification Index to instantly identify and exclude these domains from your PMax and Display campaigns.

Does Target CPA protect my budget from junk display placements?

No, Target CPA actually accelerates spend on junk placements if they manage to trigger soft conversions or view-through events. When accidental app clicks or bot traffic hit your site and fire top-of-funnel conversion tags, the algorithm views these garbage placements as highly efficient and aggressively routes more budget to them to satisfy the tCPA constraint. The Placement Leakage Audit runs a Quality-Adjusted CPA check to expose how junk inventory is artificially deflating your reported acquisition costs.

How do view-through conversions hide bad placements in Google Ads?

View-through conversions allow Google to claim credit when a user scrolls past an ad on a low-quality site but later converts through a different, legitimate channel. By assigning full or partial conversion value to these phantom views, the algorithm justifies continuous spending on garbage placements that never actually drove a direct click or engagement. The Placement Leakage Audit separates your metrics to reveal the View-Through Illusion Rate, exposing which placements rely solely on coincidental attribution to survive.

ClickCatalyst

Placement Leakage · Methodology

Back to Placement Leakage Audit

Authority Layer · 7 metrics · 4 data sources

Inside the Placement Leakage Engine

Every metric in your Placement Leakage audit traces back to three distinct placement data sources. This page documents the aggregation logic, cost estimation methodology, network segmentation breakdown, and exclusion candidate thresholds.

Jump to metric

Mobile App Impression Percentage01 Source Split (PMax vs Standard vs Video)02 Exclusion Candidate Detection03 Network Segmentation04 Placement Cost Estimation (CPM Proxy)05 App vs Other Placement Trend06 Placement Health Score07

Data Sources

read-only OAuth · no campaign changes ever

All data is read via Google Ads API and GA4. Derived intelligence tables are pre-computed in our pipeline before your audit runs.

Google Ads API — PMax Placements

google_ads_pmax_placement_performance

Placement URLs, types, display names, impressions, and ad_network_type for PMax campaigns

Google Ads API — Standard Placements

google_ads_standard_placement_performance

Placement URLs, types, display names, impressions for Standard Display campaigns

Google Ads API — Video Placements

google_ads_video_placement_detail

YouTube placement URLs, impressions, video_views, cost for engagement analysis

Google Ads API — Campaign Performance

google_ads_campaign_performance

Account-level cost and impressions for CPM proxy calculation

Mobile App Impression Percentage

The single number that quantifies junk placement exposure

Aggregated from three separate placement tables — PMax, Standard, and Video — using UNION ALL to prevent double-counting. Each table contributes impressions with placement_type classification. The mobile app percentage is the ratio of MOBILE_APPLICATION impressions to total impressions across all three sources.

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Why this matters: Mobile app placements in Google's Display network are dominated by games, idle apps, and utility apps where clicks are accidental and purchase intent is zero. Above 50% means the majority of your Display/Video inventory is junk — the algorithm routes there because CPMs are cheap, not because users convert.

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In plain English: We count every impression your ads received, then calculate what percentage appeared inside mobile apps. The higher this number, the more of your ad budget goes to places where nobody is shopping — they're playing games or tapping accidentally.

< 30%Manageable — individual exclusions sufficient

30–60%Significant — review app categories

> 60%Critical — category-level app exclusions needed

Source Split (PMax vs Standard vs Video)

Which campaign type is driving junk placements?

Three separate SUM(impressions) queries — one per source table — presented as a comparison. PMax impressions come from pmax_placement_performance, Standard from standard_placement_performance, Video from video_placement_detail. This reveals whether junk placements originate from PMax (no manual control) or Standard campaigns (where you can set placement targeting).

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Why this matters: If PMax drives 80% of junk placements, your only option is account-level exclusions — campaign-level placement controls don't work for PMax. If Standard campaigns also leak, the fix is different: switch to managed placements (opt-in only) in those campaigns.

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In plain English: We show how many impressions came from PMax, how many from Standard Display, and how many from Video campaigns. This tells you which campaign type is responsible for junk placements — and whether you have control over the fix or need account-level changes.

PMax > 70%No campaign-level control — account exclusions only

Standard significantFixable — switch to managed placements

Exclusion Candidate Detection

How we identify placements to block

All placements from PMax and Standard sources with impressions above 500, aggregated by placement URL. Every entry in the exclusion list had zero conversions in the reporting period. The display_name is derived from COALESCE(display_name, placement) to show human-readable names where available.

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Why this matters: The 500-impression threshold prevents noise — a placement with 50 impressions isn't worth the exclusion effort. Above 500, the placement has received meaningful visibility and still produced zero conversion signal. The exclusion list is the operational output of the entire audit.

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In plain English: We find every app, website, and YouTube channel where your ads appeared more than 500 times with zero conversions. These are the specific placements to copy into your Google Ads exclusion list — each one you add immediately stops waste.

> 5,000 impHigh-volume junk — exclude immediately

500–5,000Moderate — exclude in batch

Network Segmentation

Breaking down placements by Google's ad network type

From pmax_placement_performance using the ad_network_type field (Google's new network segmentation reporting). Each placement row is classified by which Google network served it: DISPLAY, YOUTUBE_WATCH, CONTENT, or SEARCH. Crossed with placement_type to reveal which network-type combinations drive the most junk.

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Why this matters: DISPLAY network + Mobile App is the highest-risk combination across all Google Ads accounts. YouTube Watch + YouTube Channel generates brand impressions without conversion signal. Knowing the network lets you apply the right type of exclusion — app category exclusions for Display, topic exclusions for YouTube.

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In plain English: Google now tells us WHICH network each placement was on — Display, YouTube, Content, or Search. We cross this with the placement type (mobile app, website, YouTube channel) to show exactly which network-type combinations generate the most junk inventory.

DISPLAY + AppHighest junk concentration

YOUTUBE_WATCHBrand exposure without conversion

CONTENTWebsite placements — quality varies

Placement Cost Estimation (CPM Proxy)

Turning junk impressions into a dollar figure

PMax placement tables don't report cost per placement. We estimate cost using the account's average CPM (cost per thousand impressions from campaign_performance) multiplied by each placement's impression count. For Video placements that DO report actual cost and view data, we use real figures and calculate view_rate as an engagement quality signal.

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Why this matters: Without a dollar figure, placement waste feels abstract — '50,000 junk impressions' doesn't motivate action. Translating to '$150 estimated cost' makes the waste tangible and creates urgency for exclusion.

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In plain English: PMax doesn't tell you how much each individual placement cost. So we calculate your average cost per 1,000 impressions across the account, then multiply by each placement's impression count. For YouTube, we have actual cost data and can also show what percentage of people actually watched the ad.

LOW ENGAGEMENT< 20% view rate — impressions ignored by users

MODERATE20–40% — monitor engagement quality

OK> 40% — users engaging with ad

App vs Other Placement Trend

Is the algorithm routing more budget to apps over time?

Daily PMax app impressions vs non-app impressions from pmax_placement_performance, split by placement_type = MOBILE_APPLICATION. A widening gap means the algorithm is progressively choosing app inventory because CPMs are lower — it interprets cheap impressions as efficient spending.

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Why this matters: Without exclusions, this trend only accelerates. The algorithm learns that app inventory is cheap and accessible, routes more budget there, gets more cheap impressions, and the cycle deepens. The trend line shows whether the problem is stable or actively growing.

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In plain English: We plot two lines over time: impressions on mobile apps vs impressions on everything else. If the app line is growing faster, the algorithm is choosing more junk inventory each day — the problem is getting worse, not better.

Widening gapAlgorithm accelerating into junk — exclude now

StableConsistent ratio — manageable

NarrowingExclusions working — maintaining

Placement Health Score

The structural verdict on your placement hygiene

All placements from PMax and Standard sources classified into three buckets based on placement_type: EXCLUDE (Mobile Apps — consistently junk), REVIEW (YouTube Channels/Videos — may have value), OK (Websites — generally acceptable). The distribution across buckets reveals whether the fix is surgical (a few bad apps) or structural (category-level changes needed).

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Why this matters: If EXCLUDE exceeds one-third of all placements, individual exclusions can't keep pace with the volume. You need category-level mobile app exclusions, topic exclusions, and potentially a rethink of which campaign types serve on Display network.

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In plain English: We classify every placement into three groups: definitely exclude (mobile apps), maybe exclude (YouTube — depends on the channel), and probably fine (websites). The proportion tells you whether this is a quick cleanup or a structural overhaul.

EXCLUDE < 20%Clean — surgical exclusions sufficient

EXCLUDE 20–33%Moderate — batch exclusions needed

EXCLUDE > 33%Structural — category-level changes required

Proprietary notice: The methodology, scoring models, waste classification logic, and recovery projections are proprietary to ClickCatalyst Digital and provided for informational purposes only. Results are subject to standard market volatility. Recovery projections are estimates, not guarantees. Contact us within 14 days if any metric appears inaccurate.

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