Account-Level Placement Exclusions: A Centralized Guide to Protect Brand Safety at Scale

Hook: Stop firefighting placement problems — centralize exclusions and protect your brand at scale

If you manage multiple Google Ads campaigns, you know the pain: unwanted sites, apps, or YouTube videos bleeding spend across formats and manual blocks that never stay in sync. In 2026, with automation-heavy formats like Performance Max and Demand Gen running broad auction logic, fragmented placement controls are a liability. Google’s January 2026 rollout of account-level placement exclusions fixes that by letting you apply one exclusion list across Display, YouTube, Performance Max and Demand Gen. But a central switch is only as useful as your process for building, testing, and auditing exclusion lists.

What this guide delivers

This article gives a step-by-step implementation and audit kit for placement exclusions in Google Ads: how to build rational exclusion lists, audit their performance, run controlled tests, measure effects on reach and CPA, and operationalize updates so brand safety scales without killing performance.

Why account-level exclusions matter in 2026

Recent trends that make centralized exclusions essential:

• Automation is dominant: Google’s Performance Max and Demand Gen automate placement and bidding. Guardrails must be account-wide, not campaign-by-campaign.
• Signal scarcity post-privacy: Loss of third-party cookies and stricter data limits mean fewer behavioral signals. Ads platforms rely more on broad inventory — making well-crafted exclusions more consequential.
• Scale and complexity: Enterprises run hundreds of campaigns. Manual placement blocking is error-prone and slow.
• Regulatory and brand risk: Brand safety expectations (advertiser obligations, partners, procurement) require auditable, centralized controls. See wider debates on Regulatory and brand risk and accountability frameworks.

“Account-level placement exclusions let advertisers block unwanted inventory across all campaigns from a single setting.” — Google Ads rollout, Jan 2026

Core concepts (quick definitions)

• Placement exclusions: Domain, app, channel, or video-level blocks you apply so your ads won't show there.
• Account-level exclusions: One list that applies across eligible campaigns and ad formats in a Google Ads account.
• Inventory blocking: The broader practice of restricting categories or sources of inventory (e.g., sensitive content) using exclusions, content labels, or partner controls.
• Exclusion lists: Named collections of placements you maintain centrally and apply to many campaigns.

Step 1 — Build exclusion lists: sources, prioritization, and naming

Start by collecting candidate placements from multiple sources so decisions are evidence-driven, not emotional.

Sources to mine

• Placement performance reports (Display & YouTube placement reports): filter low-quality placements by high spend + low conversions, high viewable non-conversion, or abnormally high bounce rates.
• Brand-safety feeds from vendors (e.g., Integral Ad Science, DoubleVerify): use their flagged domains and contextual signals.
• Manual complaints from stakeholders: PR, legal, or social monitoring often surfaces risky associations.
• Programmatic partner reports: DSP partners or publishers that repeatedly deliver non-viewable or fraudulent activity.
• Automated signals: high invalid traffic (IVT) in placement-level diagnostics and unusual spikes in CPM without corresponding conversion lift.

Prioritization framework

Don't treat all candidates equally. Use a three-tier model:

1. Tier A — High-risk, high-certainty: Verified brand-safety violations, client-mandated domains, or confirmed fraud. Exclude immediately at account-level.
2. Tier B — Medium-risk or repeated underperformance: Sites with repeated low-quality engagement or contested context. Add to a staging exclusion list for testing.
3. Tier C — Low-confidence or single-incident: One-off complaints or ambiguous signals. Monitor and wait for pattern or escalate to manual review.

Naming and versioning

Use explicit names and dates so teams know list intent and lineage:

• BRAND_EXCL_TIERA_2026-01
• PROSPECTING_EXCL_STAGING_2026-Q1
• YOUTUBE_SENSITIVE_CHANNELS_v3

Step 2 — Implement in Google Ads: UI and API options

Account-level exclusions can be created via the Google Ads UI or programmatically via the Google Ads API / Google Ads Editor. Choose based on scale:

UI path (fast, repeatable)

1. Go to Tools & Settings → Shared library (or the central 'Inventory controls' if surfaced in your UI) → Account-level placement exclusions.
2. Create a new exclusion list and paste domains, app IDs, channel IDs, or individual YouTube video IDs.
3. Apply the list to the entire account or specific campaigns if allowed by your account settings.

API path (automate updates)

For enterprise-scale management, use the Google Ads API to create and update shared exclusion lists. Pseudo-code example:

<!-- PSEUDO: Use your Google Ads client to create a SharedSet or AccountExclusion resource, then populate with placements. -->
  POST /googleads/v14/customers/{customerId}/accountExclusions
  { "name": "BRAND_EXCL_TIERA_2026-01", "placements": ["example.com", "youtube.com/channel/UCxxxx", "app:com.example.app"] }
  

Note: API objects and endpoint names may differ by release; check the official Google Ads API docs for the exact schema in 2026.

Step 3 — Testing strategy: how to test exclusions without breaking learning

Applying exclusions globally without experiments risks unintended changes in reach and machine-learning performance. Test methodically.

Option A — Controlled campaign cloning (recommended)

1. Clone representative campaigns (same budgets, creatives, audiences).
2. Apply your exclusion list to the clones (test group) and leave originals as control.
3. Run for a statistically meaningful period (see measurement guidance below).
4. Compare CPA, conversion volume, reach, and CPM between control and test.

Option B — Time-split testing

Apply the exclusions to all campaigns for a defined window and compare performance against a pre-roll baseline. Less ideal because of seasonality risk and platform learning windows.

Option C — Geographic split

Run the exclusion list in one region and not in another with similar performance profiles. Ensure you account for market differences.

Key testing considerations

• Statistical power: Use a sample-size calculator. For conversion-based KPIs, you need enough conversions per group — a rule of thumb is 200+ conversions per variant for modest lifts detection.
• Learning windows: For automated formats, allow a 7–21 day learning window after significant changes.
• Budget parity: Keep budgets and bids consistent across control and test to avoid confounding.
• Attribution consistency: Use the same conversion settings and attribution model across variants.

Step 4 — Audit playbook: what to check and when

Set a recurring audit cadence: weekly for high-risk accounts, monthly for standard. An audit is both technical (did the list apply correctly?) and performance-oriented (did CPA, reach change?).

Audit checklist

1. Verify list application: confirm the exclusion list is attached at account level and active.
2. Placement leakage: run a placement report to ensure excluded domains are not serving ads. If any show, capture IDs and escalate to Google Support.
3. Performance delta report: compare pre/post metrics over the test period (impressions, unique reach, CPM, CTR, conversion rate, CPA).
4. Learning signal health: for Performance Max, monitor conversion latency and share of conversions by campaign types.
5. Inventory overlap review: check if exclusions are pushing budget into risky new placements; inspect the top 20 placements post-exclusion.
6. Version control: archive the prior list as a dated snapshot for rollback if needed.

Measuring the effect on reach and CPA — metrics and formulas

To quantify impact, track these core metrics and derived ratios:

• Impressions — absolute delivery change.
• Unique reach — how many unique users were reached (use Google Ads or GA4 reach metrics).
• CPM and CPC — cost efficiency shifts.
• Conversion volume and Conversion rate.
• CPA = Cost / Conversions.
• Relative change = (Post - Pre) / Pre. Example: CPA_change = (CPA_test - CPA_control) / CPA_control.

Interpreting typical outcomes

• CPA improves, conversions stable: Good sign — excluded placements were low-quality and wasted spend.
• CPA worsens, conversions steady or up: Exclusions pushed spend to more expensive placements; consider partial rollback or tiered exclusions.
• Conversions drop significantly: You reduced reach in core audiences — check if excluded domains contained valuable users. Reclassify and re-test.

Example (anonymized case)

Hypothetical mid-market e-commerce advertiser ran a controlled test by cloning prospecting campaigns and applying a Tier B exclusion list:

• Control conversions (2 weeks) = 1,200; CPA = $28.40
• Test conversions (2 weeks) = 1,150; CPA = $23.30
• Impressions dropped by 12%, unique reach down 9%.

Interpretation: Slight conversion volume dip but a 18% CPA improvement. The team marked several Tier B sites as Tier A and implemented a staged rollout to maintain conversion volume while keeping CPA gains.

Advanced strategies for scaling exclusions

Dynamic staging

Create a pipeline:

1. Staging exclusion list for candidates (Tier B).
2. 30-day test with clones.
3. Promote to production list (Tier A) if CPA improves or risk confirmed.

Use granular exclusions where possible

Instead of blocking entire domains, prefer channel or video-level exclusions on YouTube when the domain has mixed content. That preserves valuable inventory while avoiding problematic content.

Combine exclusions with contextual controls

Account-level exclusions are one tool in the brand safety toolkit. Also leverage:

• Content suitability settings and inventory filters in Google Ads.
• Third-party verification for continuous monitoring — integration with verification partners and media workflows helps automate flags into your staging lists.
• Creative-level controls and exclusion of sensitive category labels.

Common pitfalls and how to avoid them

• Over-blocking: Blocking broad swathes of inventory reduces scale and starves ML models of learning signals. Mitigate by staged rollouts and measuring reach impact.
• Attribution mismatch: Changing placements during an attribution window skews results. Use consistent windows for test and control.
• No rollback plan: Keep dated snapshots and a rollback playbook to restore prior lists quickly.
• Ignoring automated formats: Performance Max may reallocate spend aggressively; test with extra caution and monitor longer.

Operationalizing for teams: processes and SLAs

To keep exclusions effective and auditable, set easy-to-follow processes:

• Intake ticket: All brand-safety or complaint-driven candidates go through a ticket system with documented evidence.
• Decision SLA: Tier A — immediate action within 24 hours; Tier B — evaluate within 72 hours; Tier C — monitor for 14 days.
• Review cadence: Monthly executive summary and weekly operational checks.
• Change log: Store each list change with approver, rationale, and test outcome.

Audit template (quick copy-paste checklist)

1. Confirm account-level exclusion lists active and correctly named.
2. Export placement report for last 14 days; look for excluded placements.
3. Collect performance pre/post metrics (14–28 day windows): Impressions, Reach, CPM, Conversions, CPA.
4. Run significance test on CPA (two-proportion or t-test depending on data distribution).
5. Check top 20 placements after exclusions for unintended traffic shifts.
6. Archive current list snapshot and document decisions.

2026 predictions and what's next

As Google and the wider ad ecosystem evolve, expect these developments:

• More granular supply signals: Publishers and platforms will provide richer contextual metadata, enabling smarter contextual exclusions rather than blunt domain-level blocks.
• Real-time brand-safety scoring: Expect APIs that feed dynamic risk scores into account-level exclusions for near-real-time blocking; edge and personalization plays will matter more (edge personalization).
• Stronger integration with verification partners: Shared signals between verification vendors and ad platforms will enable automated promotion of high-confidence flags to account-level exclusions.

Final checklist — quick start to protect brand safety at scale

• Gather placement data from reports, vendors, and complaints.
• Prioritize with a Tier A/B/C model.
• Create named, versioned exclusion lists at account level.
• Test via cloned campaigns or geographic splits; allow learning windows.
• Audit regularly: check for leakage, CPA impact, and reach loss.
• Document, snapshot, and maintain a rollback plan.

Closing — actionable takeaways

Account-level placement exclusions are a high-leverage feature in Google Ads for 2026: they simplify inventory blocking and make brand safety auditable. But centralization raises stakes — the right process matters. Build evidence-driven exclusion lists, test with control groups, measure CPA and reach impacts rigorously, and operationalize versioned updates. Start small, measure, then scale.

Call to action

Ready to implement account-level exclusions without sacrificing performance? Download our free audit checklist and exclusion list template (CSV ready) to start a staged rollout today. If you want a tailored audit, request a 30-minute account review — we'll map immediate Tier A candidates and a 30-day test plan that preserves reach while protecting brand safety.

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