How to Build a Blocklist That Scales: Best Practices for Account-Level Placement Exclusions

Stop Losing Conversions to Bad Placements: Build a Blocklist That Scales

Hook: If you manage multi-channel accounts and you still manually add placement exclusions campaign-by-campaign, you’re wasting ad spend and breaking automation. In 2026, with Google Ads’ account-level placement exclusions and increasingly automated buying, brands need a dynamic, data-driven blocklist that scales—one that uses performance signals to block harmful inventory without gutting reach.

The problem: over-exclusion, wasted time, and brittle controls

Marketers face three persistent problems when building placement blocklists:

• Over-exclusion: Blocking broadly to avoid risk reduces scale and raises CPCs.
• Operational drag: Campaign-level exclusions are slow to update and error-prone.
• Poor signal use: Decisions often rely on hearsay or brand-safety lists rather than campaign performance signals.

In early 2026 Google introduced account-level placement exclusions, letting advertisers apply exclusions across Display, YouTube, Performance Max and Demand Gen from one centralized list. That change is a tipping point: account-level controls eliminate operational friction, but they also raise stakes—one bad exclusion list can starve every campaign.

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

Why a data-driven exclude strategy matters now

The advertising ecosystem in 2026 is dominated by automation: algorithmic bidding, creative optimization, and budget orchestration (e.g., total campaign budgets across Search and Shopping). Automation optimizes toward volume and conversions—unless you give it the wrong inventory. A static, opinion-based blocklist either:

• Over-blocks, harming reach and driving CPMs up, or
• Under-blocks, allowing toxic placements that harm brand safety and conversion quality.

A data-driven blocklist uses objective performance signals to precisely exclude placements that demonstrably lower ROI, while preserving high-quality inventory and campaign scalability.

Core principles for scalable, account-level exclusion lists

1. Make exclusions evidence-based. Use conversion quality, not just clicks or impressions, to decide.
2. Prefer scores over binary blocks. Rank placements with a risk score and apply graduated exclusions.
3. Automate safe, auditable rules. Use scripts, APIs, and CI-like review processes to change lists.
4. Test before globalizing. Run experiments to confirm causal impact before applying account-level exclusions.
5. Refresh frequently but conservatively. Performance changes; run scheduled reviews and keep a rollback path.

Performance signals to use (and how to use them)

Not all signals are equal. Use a mix of short-term and long-term metrics, weighted by business goals.

Primary signals (use these first)

• Cost per conversion / CPA — Compare placement CPA to campaign baseline. A sustained 2x+ variance is actionable.
• Conversion rate (CVR) — Low CVR with high click volume can indicate poor intent traffic.
• Return on ad spend (ROAS) / Value per Conversion — Critical for ecommerce and LTV-driven decisions.

Secondary signals (contextualize exclusions)

• Bounce rate & pages/session — High bounce from a placement signals poor landing experience or low intent.
• Viewability & engaged-view metrics — For video or display placements, low viewability reduces impact of impressions.
• View-through conversions — Use cautiously—high VTC but no clicks can be misleading for direct-response campaigns.

Brand-safety & fraud signals

• Third-party verification (DoubleVerify, IAS) — integrate scores for explicit brand-safety fails.
• Invalid traffic rates — Programmatic partners and publisher reports on IVT are non-negotiable.

Audience & downstream-quality signals

• Post-conversion metrics — Refund rates, churn, LTV differences by placement.
• Attribution-adjusted value — Use MTA/U-shaped models to see if placements assist rather than convert directly.

Combine these signals in a composite placement quality score (0–100) that weights signals by business priority (e.g., LTV-heavy B2B brands weight downstream quality higher).

Designing a placement scoring model

Here’s a practical, reproducible model you can implement in a BI tool or via a script.

1. Gather data — Pull placement-level metrics for at least 28–90 days: impressions, clicks, spend, conversions, revenue, post-conversion metrics, viewability, IVT.
2. Normalize metrics — Convert CT R, CVR, CPA, ROAS into z-scores or percentiles to combine disparate scales.
3. Weight metrics — Example: CPA (30%), ROAS (25%), CVR (15%), IVT (10%), viewability (10%), bounce (10%). Weights should reflect strategic priorities.
4. Compute composite score — Weighted sum, then scale to 0–100.
5. Bucket placements — Green (70–100): keep; Amber (40–69): monitor and test; Red (0–39): exclude candidate.

Tip: Use Bayesian smoothing for low-sample placements to prevent noisy data from driving exclusions.

Avoiding over-exclusion: rules and guardrails

Over-exclusion happens when teams apply hard thresholds without context. Prevent it with these guardrails:

• Minimum sample size — Only exclude placements with at least N conversions (e.g., 10 conversions) or 1,000 impressions in the lookback window.
• Decay windows — Older poor performance shouldn’t drive current exclusions; decay older data (e.g., 30/60/90-day weighting).
• Gradual enforcement — Start with campaign-level or channel-specific tests before account-wide exclusion.
• Exclusion probation — Auto-exclude for a short probation (7–14 days) and re-evaluate before permanent account-level action.

Experimentation: validate exclusions before scaling

Run controlled experiments to prove causality. Follow this simple framework:

1. Create a matched control — Split traffic or duplicate campaigns (A/B) with the same budget, bids, and creative, differing only by the exclusion.
2. Run to statistical significance — Use conversion-level outcomes to test for improvement in CPA or ROAS. If you need a reference for advanced measurement approaches, see advanced strategies that use preference signals and A/B testing.
3. Analyze secondary effects — Watch for lift or drop in impression share, CPM, and overall campaign volume.
4. Roll out progressively — If successful, push to account-level exclusions with monitoring and rollback triggers.

Operationalizing account-level exclusions (scale & governance)

Account-level exclusions are powerful—treat them like product changes. Implement these operational practices:

• Change control — Require PR reviews (owner, analyst, brand safety) for any account-level list modification.
• Auditable logs — Track who added/removed placements, and why. Keep timestamps and the signal snapshot used for the decision. Keep this alongside your incident playbooks and monitoring — e.g., rollback alerts and postmortems in the style of an outage postmortem playbook.
• Automated rule engine — Convert your scoring thresholds into automated rules that can add or remove placements, with guardrails (probation periods, sample-size checks). For examples of field playbooks that convert manual steps into repeatable automation, compare marketing automation to how other industries build rules for micro-events such as an advanced pop-up playbook.
• Daily monitoring — Dashboards for spend affected by exclusions, CPAs of excluded vs active placements, and a rollback alert if CPA rises unexpectedly.
• Cross-channel mapping — Maintain publisher mapping across Google, Meta, and DSPs so exclusions are consistent across platforms where feasible. For agency-level patterns, see how teams centralize brand lists in a cross-account model.

Case study (hypothetical, repeatable)

Retailer X runs omnichannel prospecting campaigns across Display, YouTube and Performance Max. They centralized exclusions after Google’s Jan 2026 launch. Here’s what they did and the results:

1. Collected 60 days of placement-level metrics and applied a composite scoring model.
   • Initial red placements: 1,200 sites/apps with average CPA 3x account baseline.
2. Filtered placements with fewer than 15 conversions to avoid noisy exclusions.
3. Run a 28-day A/B test: control vs. account-level exclusion list applied to 50% of prospecting spend.
4. Results: CPA decreased 18% in test group; overall impressions dropped 4% while conversion volume rose 6% (attributed to higher quality traffic).
5. After staged rollout, Retailer X preserved scale by moving 40% of red placements to a probationary suppression (lower bids) instead of outright block—this prevented CPM inflation.

Key lesson: smart exclusions improved ROI and preserved scale by mixing exclusion with bid suppression and monitoring.

Practical playbook: step-by-step to implement this month

1. Inventory discovery — Export placement-level data from ad platforms and programmatic partners for the last 28–90 days.
2. Score placements — Build the composite placement score in your analytics stack (Looker, BigQuery, Snowflake, Tableau, or Python).
3. Create buckets — Green/Amber/Red with predefined actions for each bucket.
4. Test exclusions — Use A/B experiments for 2–4 weeks targeting only prospecting budgets first. For thoughtful A/B approaches and measurement nuance see advanced measurement guides.
5. Operationalize — Implement automation rules in your DSP/Google Ads via API, schedule weekly reviews, and set rollback alerts.
6. Govern & document — Publish exclusion policies and keep an audit trail.

Advanced strategies for 2026 and beyond

As platforms evolve, so must blocklists. Here are advanced tactics that separate mature programs from ad-hoc lists:

• Signal enrichment — Ingest CRM and post-purchase data to see how placements impact LTV and churn.
• Uplift modeling — Build models that estimate the incremental value of each placement, not just last-click conversion.
• Dynamic suppression — Instead of binary blocks, dynamically reduce bids or frequency caps on marginal placements to retain reach without wasting spend.
• Cross-account lists — For agencies, maintain master exclusion lists and deploy tenant-specific variants with different thresholds.
• Publisher whitelists for key formats — For high-value audience segments, use curated allowlists (rather than exclusions) to guarantee quality.

Common pitfalls and how to avoid them

• Basing decisions on surface metrics: Don’t exclude solely because CTR is low—look at conversion quality.
• Letting manual opinions rule: Avoid “I don’t like this site” blocks without evidence.
• Making account-wide changes without tests: Always validate with experiments first.
• Ignoring platform differences: YouTube engagement dynamics differ from display—use channel-specific scoring adjustments.

Tools and integrations to make this work

To scale, use these capabilities and tools:

• Platform APIs (Google Ads API, DV360, Meta Marketing API) — Automate list updates and maintain sync across channels.
• Analytics & BI (BigQuery, Snowflake, Looker) — For high-volume placement scoring and cohort analysis.
• Verification vendors — DoubleVerify, Integral Ad Science, Moat for brand-safety signals.
• Tag management & server-side tracking — Improve signal fidelity and match conversions to placements accurately.

Final checklist before flipping the account-level switch

• Run a placement scoring model and generate Green/Amber/Red lists.
• Validate Red placements with an A/B test or a 14-day probation block.
• Implement automated rules with minimum-sample and probation safeguards.
• Create governance: owners, change control, rollback triggers, and audit logs.
• Map cross-channel equivalents and apply consistent policies where possible.

Conclusion: balance protection with performance

In 2026, centralized exclusions are essential—but they must be precise. A data-driven placement blocklist preserves your brand while keeping automation effective and campaigns scalable. Use composite scoring, experiment before account-wide enforcement, and automate with strong governance. When blocklists are built on signals—not opinions—you’ll reduce wasted spend, protect brand safety, and keep the funnel full.

Call to action: Ready to convert your ad inventory signals into a scalable account-level exclude strategy? Contact our team for a free placement audit and a templated scoring model you can deploy this week.

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