Build a Blacklist: Operationalizing Account-Level Placement Exclusions

Stop Losing Control: Build a Placement Blacklist That Protects Brand Safety Without Killing Reach

Marketers in 2026 face two hard truths: platforms are more automated than ever, and inventory quality still varies wildly. If you don’t centralize and operationalize placement exclusions, you’ll waste spend on low-quality inventory or overly fragment reach with hyper-aggressive blocks. This guide gives a pragmatic, operations-first checklist and ready-to-use reporting templates to maintain a centralized placement blacklist across accounts while minimizing negative impact on reach and performance.

Why account-level placement exclusions matter in 2026

In January 2026 Google Ads introduced account-level placement exclusions — a capability that makes centralized control realistic across Performance Max, Demand Gen, YouTube and Display campaigns. That change is part of a larger trend: ad platforms are automating targeting and budgeting, so guardrails must be centralized and smart to avoid both risk and reach loss.

The tradeoff: block too widely and you reduce scale and increase CPAs; block too narrowly and brand safety, fraud and low-quality inventory leak into reports. The operational challenge is not just building a blacklist — it’s maintaining it, measuring the impact, and integrating it into automated buying strategies without manual chaos.

Quick bottom-line guidance (inverted pyramid)

• Start centralized: use manager/shared lists and account-level exclusions where available.
• Tier your blocks: urgent (block now), conditional (monitor & block on thresholds), and optional (low priority).
• Measure impact: always run holdout or experiments and monitor reach-loss metrics before blanket bans.
• Automate workflows: sync blacklist via API and enforce governance with clear cadence and owners.

Operational checklist: Build, govern, and maintain a centralized placement blacklist

Use this checklist as the backbone of your ad ops playbook. It’s ordered for launch — discovery, build, enforce, measure, iterate.

1. Discovery: collect evidence and establish signals
   • Aggregate placement-level data across platforms: impressions, clicks, viewability, VCR (for video), conversion rate, CPA, invalid traffic (IVT) flags, user complaints, and brand safety vendor scores.
   • Use sources: platform reports (Google Ads placement reports), DSP logs, third-party sellers.json/ads.txt checks, brand-safety providers (e.g., Integral Ad Science, DoubleVerify), and direct user reports.
   • Define minimum sample windows: 14–30 days for stable signals; 7 days for urgent fraud or egregious content.
2. Classification: map placements to tiers
   • Tiers determine action and review cadence: Urgent (block immediately), Conditional (monitor; block when X threshold triggers), Monitor (flag for pattern watch), and Allowlist (explicitly allowed despite low-quality signals).
   • Criteria examples: sustained high IVT (>1.5x account baseline), viewability <10% with high spend, VCR <10% on video placements with high CTR but low conversion, or repeated brand safety violations.
3. Centralize: implement shared and account-level exclusions
   • Where available, use platform shared lists (Manager/Account-level). In Google Ads, move to the new account-level placement exclusions to apply blocks across PMax, Display, Demand Gen and YouTube.
   • For multi-account setups, maintain a manager-level master blacklist and sync to child accounts using Google Ads API, scripts, or your ad ops platform (SA360, DV360, or DSP APIs).
4. Automation: enforce, sync and version-control
   • Keep a canonical source (e.g., a Git-backed CSV or a Google Sheet with version history) and use automated sync jobs to push updates to platform accounts daily or hourly as needed.
   • Build simple safeguards: do not auto-block placements without 2+ corroborating signals unless flagged as critical fraud/brand safety.
5. Governance: roles, SLAs and escalation
   • Assign owners per account group and a cross-account blacklist steward who is empowered to make fast decisions in crises.
   • Define SLAs for reviews (e.g., urgent blocks reviewed within 24 hours, conditional placements reviewed weekly).
6. Measurement: track reach impact and performance delta
   • Always measure direct impact on impressions, unique reach, CPA, ROAS and conversion volume. Use holdouts to quantify incremental effect before rolling out mass exclusions.
   • Monitor both immediate KPI changes and longer-term automation responses (e.g., bid shifts in PMax).
7. Iterate: continuous improvement loop
   • Review quarterly for strategic updates and monthly for tactical cleanup. Keep an archive of historical blocks and why each one was added.
   • Apply a ‘sunset’ policy: placements blocked for less than 90 days should be reviewed and re-tested before permanent banning.

How to minimize negative impact on reach and performance

Blocking placements reduces scale. Your job is to reduce risk while preserving the performance that automation delivers. Here’s how to do that with surgical precision.

1. Tiered blocking (don’t flip the switch all at once)

• Start with Urgent-only blocks for fraud or clear brand safety issues.
• Apply Conditional blocks with threshold rules: e.g., block if IVT > 2x account baseline for 7 consecutive days AND CPA for that placement > 150% of account CPA.
• Keep a monitor bucket for low-evidence placements and re-evaluate after 30 days.

2. Run holdout experiments before enterprise-wide application

Use randomized holdouts at the campaign or audience level to measure real incremental lift. For example:

• Create A/B groups: Group A uses the blacklist, Group B does not.
• Measure incremental conversions, CPA and reach loss over two weeks to a month. If conversions fall but CPA improves, calculate absolute revenue impact.

3. Apply exceptions and allowlists

Some premium publishers or known-performing channels may look poor by automated signals yet drive real conversions. Maintain an allowlist and require data to remove it.

4. Use soft signals instead of hard blocks when possible

Where platforms allow bid modifiers for placements (or inventory quality tiers), prefer reduced bids or placement exclusions at campaign-level for less-critical inventory to test impact first.

Reporting templates: what to track and sample CSV & SQL

Below are two practical templates: a blacklist operational CSV for import and a reporting dashboard template for weekly monitoring. Use the CSV as your canonical source of truth; use the report to justify decisions to stakeholders.

Blacklist master CSV (canonical source)

domain,placement_type,first_reported,reason,signal_count,last_reviewed,status,owner,evidence_url
examplebadsite.com,web,2025-11-17,Brand safety - hateful content,3,2026-01-10,blocked,AdOpsLead,https://screenshots/123
lowviewapp://bundleid,app,2025-12-12,Viewability < 10% with $500+ spend,2,2026-01-08,conditional,AccountMgr,https://logs/456
youtube.com/channel/UC123,video,2026-01-02,High CTR,Low CVR (bot-like),4,2026-01-14,monitor,Analytics,https://dv/789

Weekly blacklist monitoring dashboard: required metrics

• Total placements blocked (account-level)
• Impression loss vs. prior week and unique reach delta
• Spend saved on blocked placements (actual spend stopped)
• Conversion change (absolute and %)
• CPA and ROAS delta for impacted campaigns
• Top 10 placements triggering alerts by spend and IVT score
• Number of exceptions/allowlist entries and their performance

Sample SQL (BigQuery) to surface risky placements from Google Ads export

-- Returns placements with high spend but poor conversion efficiency
SELECT
  placement_domain,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks,
  SUM(cost_micros)/1e6 AS cost,
  SUM(conversions) AS conversions,
  SAFE_DIVIDE(SUM(cost_micros)/1e6, NULLIF(SUM(conversions),0)) AS avg_cpa
FROM
  `project.dataset.google_ads_placement_performance`
WHERE
  segments.date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) AND CURRENT_DATE()
GROUP BY
  placement_domain
HAVING
  cost > 500 -- $500 spend threshold
  AND COALESCE(conversions,0) < 2
ORDER BY
  cost DESC
LIMIT 200;

Adapt table and column names to your Google Ads export schema. Add joins to IVT or partner provider tables to enrich signals.

Automation patterns and API playbook

Centralization only works at scale with automation. Here are reliable patterns to keep lists in sync across Manager accounts and platforms.

Daily sync job (recommended)

1. Canonical list stored in a version-controlled CSV or database.
2. Daily job queries recent placement signals and flags new candidates.
3. Human reviewer approves urgent candidates (or auto-approve on clear fraud signals).
4. Approved entries are pushed to Google Ads account-level exclusions via Google Ads API and to DSPs via their APIs.

Use feature flags for rollouts

Feature-flag placement exclusions to control scope (e.g., apply to non-brand campaigns first). This reduces shock to performance and lets automation re-optimize gradually.

APIs and tools to consider

• Google Ads API — push account-level exclusion lists programmatically.
• DV360 and SA360 — manage shared inventory controls for programmatic buys.
• DSP APIs — synchronize exclusions for open-auction buys.
• BigQuery + Looker/Datastudio — centralized reporting with scheduled extracts.

Real-world example: staging a safe, centralized rollout

Scenario: A retail advertiser with 25 accounts noticed a spike in low-viewability video placements and a surge of unusual clicks on several YouTube channels. They were using multiple campaign-level negative lists, causing inconsistent blocking.

1. They created a master blacklist CSV and set the classification tiers.
2. They launched a 14-day A/B holdout on 20% of spend to measure the blacklist’s impact.
3. Using Google Ads account-level exclusions, they blocked only Urgent placements initially and moved Conditional placements into monitoring with automated thresholds.
4. Result: 6% impression loss overall, CPA improved 8%, and conversions were flat — a net positive since CPA fell without revenue loss. They then expanded the list to additional accounts with automated sync and SLA governance.

Common pitfalls and how to avoid them

• Over-blocking: Avoid blocking placements with thin evidence. Use tiering and holdouts.
• No audit trail: Maintain version control for the blacklist and keep reasons/evidence to defend decisions internally.
• One-size-fits-all: Allow account-level exceptions (e.g., brand vs. performance campaigns have different tolerance).
• Ignoring automation effects: Watch for algorithmic reallocation in PMax or auto-bidding strategies after exclusions; re-measure performance after 7–14 days.

“Centralized exclusions don’t eliminate risk — they let you control it predictably. The goal is a lean, evidence-driven blacklist plus a rapid test-and-measure rhythm.” — Senior AdOps Director, 2026

Actionable takeaways: 7 steps to implement this week

1. Export placement data across accounts for the last 30 days and identify the top 200 placements by spend.
2. Classify those placements into Urgent/Conditional/Monitor using IVT, viewability, and conversion signals.
3. Create a canonical blacklist file with evidence links and owner assignments.
4. Push Urgent blocks to account-level exclusions using the Google Ads UI or API.
5. Run a two-week A/B holdout to measure incremental performance impact.
6. Set up a daily sync job and a weekly review meeting with owners and analytics.
7. Archive changes and implement a 90-day sunset review policy.

Looking ahead: trends shaping blacklist strategy in 2026

Late 2025 and early 2026 solidified a few trends: platforms ship more centralized controls (like Google Ads account-level exclusions), automation fills targeting gaps, and measurement tools emphasize incrementality. Blacklists will increasingly be integrated with real-time signals (IVT feeds, contextual classifiers and third-party brand-safety APIs). The teams that win are those that operationalize blacklist governance into automated, auditable workflows and pair exclusions with rigorous A/B testing.

Final checklist and templates download

Use the above CSV and SQL snippets as your starting point. Build the canonical file, automate the sync, and commit to a weekly governance rhythm. Keep decisions evidence-based and reversible.

Ready to centralize your blacklist without sacrificing reach? Start with the 7-step plan above, run one A/B holdout this month, and schedule a 30-minute audit to identify your top 50 risky placements. If you want a ready-made template and API script to push account-level exclusions, contact our team for a hands-on implementation pack tailored to Google Ads and common DSPs.

Call to action

Don’t let fragmented placement controls erode performance or brand safety. Download the operational CSV template, implement the 7-step checklist this week, and book a 1:1 audit to get a custom sync script for your manager accounts. Centralize smarter — protect brand and performance at scale.

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