Automating Your Placement Exclusions: Building Dynamic Blocklists with Scripts and APIs

Stop chasing bad placements—automate your account-level blocklist

Ad ops and marketing teams waste hours managing placement exclusions across dozens of campaigns. Google Ads' 2026 account-level exclusion capability finally centralizes blocklists—but manual updates still cost time and let bad inventory slip through. This guide shows how to fully automate adding and removing placements from the new account-level exclusion list using the Google Ads API, lightweight scripts, and third-party micro apps so you can cut wasted spend and scale guardrails without slowing automation-driven formats like Performance Max.

Why automate placement exclusions in 2026?

Google's Jan 2026 rollout of account-level placement exclusions solves fragmentation by letting a single exclusion list apply across Performance Max, Demand Gen, YouTube, and Display. But the new control only solves centralization—not the decisioning and operational overhead of maintaining that list.

In 2026, ad ecosystems run at programmatic speed and campaigns are increasingly automated. That means guardrails need to be: fast, data-driven, auditable, and reversible. Automation delivers:

• Speed: block risky placements within minutes of detection.
• Consistency: a single source of truth for exclusions across campaigns.
• Scalability: apply consistent rules across hundreds of accounts.
• Auditability: logs and versioning for compliance and creative QA.

High-level architecture: signals, decision layer, enforcement

Design the automation as three layers:

1. Signals — where you detect problematic placements. Examples: placement-level metrics from Google Ads, viewability drops from third-party measurement, crawl-based brand safety matches, or manual reports from brand safety teams.
2. Decision layer — rules and ML that decide whether to add, hold, or remove a placement from the account-level blocklist. Implement throttles, confidence thresholds, and human approval gates here.
3. Enforcement — the mechanism that writes to Google Ads. Use the Google Ads API for reliable account-level changes; lightweight scripts or micro apps can orchestrate and provide UIs.

What to block—and when

Not every underperforming placement should be blocked. Use data-driven rules:

• High invalid traffic signals from verification vendors.
• Viewability below a set threshold over a rolling 7–14 day window.
• CTR and conversion-rate outliers suggesting fraud or non-human traffic.
• Brand-safety category hits from crawlers or 3rd-party classifiers.
• Manual takedown requests for specific domains, channels, or apps.

Combine signals to reduce false positives. For example, require two independent signals before auto-blocking a placement (e.g., low viewability + brand-safety hit).

Google Ads API: the enforcement backbone

The Google Ads API is the authoritative path for account-level changes. In 2026 the API supports account-level placement exclusions that apply across eligible campaign types. Use the API for:

• Creating and deleting exclusion entries.
• Batch operations to update hundreds of placements in one request.
• Audit logs and response codes to drive retry logic and alerts.

Practical approach: typical flow

1. Detect candidate placements via analytics or 3rd-party data export.
2. Send candidates to the decision layer for scoring and human review.
3. Once approved, call the Google Ads API to add the placement to the account-level exclusion list.
4. Store the change in your internal blocklist DB with metadata and reason codes.
5. Monitor results and optionally schedule reevaluation to remove placements after cooling periods.

Example pseudo-workflow using Python

Below is a simplified pseudocode flow. This is conceptual — adapt to your client libraries and auth patterns.

# 1. Authenticate with Google Ads API client
# 2. Get candidate placements (from your signaling pipeline)
# 3. For each approved placement, call a mutate operation
for placement in approved_placements:
    body = { 'placement': placement, 'reason': 'low_viewability+brand_safety' }
    response = google_ads_client.add_account_placement_exclusion(customer_id, body)
    if response.success:
        record_blocklist_entry(placement, response.id, body)
  

Google Ads Scripts vs API vs micro apps

Choose tools based on complexity and governance needs:

• Google Ads API — use when you need reliable, auditable, bulk updates across accounts. Best for enterprise ad ops and integrations with data warehouses.
• Google Ads scripts — fast to prototype detection jobs inside Google Ads. Useful for harvesting low-level signals, but in 2026 account-level exclusions are best applied via the API for cross-campaign consistency.
• Micro apps and low-code tools (Zapier, Make, n8n, or custom Flask/Cloud Function micro apps) — great for approval workflows, Slack integration, and giving non-devs a UI to review candidates before the API writes changes.

Example: hybrid pattern

Use a Google Ads script to scan placements and post candidates to a Slack channel via webhook. A micro app receives approvals from Slack and then calls the Google Ads API to update the account-level exclusion list. This pattern gives ad ops quick detection and a controlled enforcement path without requiring full dev cycles.

Implementation details and best practices

Follow these to make your automation robust and trusted:

• Idempotency — design your calls so re-sending the same placement does not create duplicates or unintended state. Store external IDs in your DB.
• Batching — group changes into sensible batches to reduce API quota usage and improve throughput.
• Backoff and retries — implement exponential backoff for rate limits and transient errors.
• Approval gates — auto-block only when multiple signals align; otherwise route to human review.
• Staging — test in a sandbox or test account to validate logic without harming live spend.
• Rollback — store metadata (who approved, why, timestamp) and expose one-click rollback in your micro app.
• Logging and reporting — integrate with BigQuery or a logging service to retain change history for compliance and post-mortem analysis.

Sample decision rules (practical)

• Rule A: If viewability < 10% AND conversions per thousand impressions < threshold → auto-queue for human review.
• Rule B: If invalid traffic score > 80% AND recent spend > $100/day → auto-block with high confidence.
• Rule C: If brand safety crawler matches category 'Hate' or 'Adult' → immediate block and Slack alert.

Use cases and a short case study

Example: Retail brand with Programmatic Display and Performance Max campaigns. Prior to automation, ad ops manually blocked domains across 120 campaigns. After implementing the automation stack:

• Signal sources: Google Ads placement reports, IAS viewability, internal CRM conversion mapping via BigQuery.
• Decision layer: rule-based engine that required 2 signals or a manual report to auto-block.
• Enforcement: central micro app calling Google Ads API to update account-level exclusions and write to a blocklist table in BigQuery.

Results in first 30 days: 18% reduction in wasted impression spend on unwanted placements, 12% improvement in conversion rate for display cohorts, and a 40% reduction in manual ticket volume for ad ops.

Third-party integrations and micro apps

The rise of micro apps in 2025–2026 means non-developers can rapidly build internal tools to manage exclusions. Use cases:

• Slack approval workflows: post candidates with reason codes and thumbnails for quick decisions.
• Google Sheets or Airtable as a temporary blocklist database for small accounts, with a micro app syncing approved rows to the Google Ads API.
• Zapier/Make connectors to automatically convert third-party measurement alerts into blocklist candidates.

Micro apps lower the barrier for ad ops teams to iterate on rules and keep control in-house without full engineering resources.

Security, governance, and audit

When you automate account-level exclusions, governance matters:

• Restrict API keys and service accounts to least-privilege roles.
• Require multi-person approvals for high-impact blocks (e.g., domains with large historical spend).
• Keep an immutable audit trail (who, when, why) stored outside the ad account.
• Regularly review blocklist churn to spot over-blocking and false positives.

Monitoring and maintenance

Automation isn't "set and forget." Set up routines:

• Weekly dashboards showing new blocks, removed blocks, and their impact on spend and conversions.
• Quarterly reviews of rules and thresholds informed by new ad formats and industry trends.
• Automated re-evaluation pipelines that consider removing a placement after a cooling period if signals normalize.

Future-proofing for 2026 and beyond

Expect continuous platform changes—Google will refine placement data, and new formats will surface different kinds of inventory. To stay resilient:

• Keep your decision layer modular so new signals (e.g., AI-based brand-safety models) can be added easily.
• Use open data schemas (SVAs, reason codes) to make blocklists interoperable across DSPs and verification vendors.
• Invest in micro apps that empower non-developers to tune guardrails quickly.

"Account-level exclusions are a major step forward—but the real power comes from automating the decision and enforcement layers so brands can react at programmatic speed." — Ad ops lead, anonymous

Actionable checklist to start automating today

1. Enable account-level placement exclusions in your Google Ads accounts and confirm API support for your manager accounts.
2. Connect signal sources: Google Ads placement reports, viewability/VAST verification, BigQuery exports, and 3rd-party crawlers.
3. Build a lightweight decision layer (rule engine or simple ML) and define approval thresholds.
4. Implement enforcement via the Google Ads API with idempotent, batched mutate operations and retries.
5. Wrap enforcement with a micro app for approvals, logs, and rollback controls.
6. Monitor impact with a dashboard and schedule regular reviews to refine rules.

Final thoughts

Centralized account-level exclusions are a pivotal 2026 development. But to truly regain control over inventory and ROI, you must automate the decisioning and enforcement process. A hybrid stack of the Google Ads API, targeted scripts, and micro apps gives you the speed, control, and human-in-the-loop governance modern ad ops teams need.

Start small, scale safely

Prototype with a single business unit or test account. Use conservative thresholds and built-in rollbacks. Once you prove the value—reduced wasted spend and faster response times—scale to additional accounts and clients.

Call to action

Ready to stop firefighting placements and build a scalable blocklist automation? We can help design a tailored pipeline—signals, rules, micro app, and API enforcement—that fits your ad stack. Reach out to schedule a 30-minute technical audit and get a runnable automation blueprint for your first account-level exclusion rollout.

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