CROPPCOptimization

Account-Level Placement Exclusions: A CRO Perspective

cclicky

2026-01-24

9 min read

Use account-level placement exclusions to cut waste, improve conversion quality, and run smarter CRO tests in 2026.

Stop wasting ad dollars on low-quality inventory — fast

If your paid channels drive traffic but not conversions — or if conversions drop after a surge of low-quality clicks — you already know the frustration: great creative, carefully tuned audiences, and still a pool of wasted spend and noisy data. In 2026, with Google Ads' new account-level placement exclusions, you can block problematic inventory centrally and start improving conversion quality and ROAS without ripping apart automated campaigns.

Why account-level placement exclusions matter now (2026)

On January 15, 2026, Google announced that advertisers can apply one exclusion list at the account level across Performance Max, Demand Gen, YouTube, and Display campaigns. That change is more than a convenience — it’s a lever for campaign optimization and cleaner conversion signals in an automation-first ad ecosystem.

“Placement controls have long been fragmented... Account-level exclusions give brands more control without undermining automation.” — industry reporting, Jan 2026

The ad ecosystem context

Automation-first formats (Performance Max, Demand Gen) now capture the majority of display budgets — but they need guardrails.
Privacy-first measurement (post-cookie, enhanced server-side tagging) shifts focus from raw volume to high-quality, privacy-safe conversions.
Scale and complexity: large accounts with hundreds of campaigns previously duplicated placement exclusions across campaigns — costly and error-prone.

Account-level exclusion lists let CRO and PPC teams apply consistent blocking rules instantly — a foundational move for any conversion-focused optimization program in 2026.

From a CRO perspective: what blocking inventory at account level does for you

Think of account-level exclusions as a pre-filter for the traffic feeding your funnels. Instead of reacting to bad conversions campaign-by-campaign, you stop the waste at scale. The benefits include:

Higher conversion quality: fewer accidental or low-intent conversions from fraudmy or in-app sources improves the signal-to-noise ratio.
Lower wasted spend: centralized blocking reduces spend on poor-performing apps, sites, or YouTube channels across all campaigns.
Cleaner experimentation: fewer outlier placements means A/B tests and holdouts reflect true creative or landing page performance — not placement bias.
Faster troubleshooting: one list to update when a new bad placement appears — no hunting through dozens of campaigns.
Brand safety and compliance at scale: consistent blocking across formats helps meet regulatory or brand requirements (important in EU + privacy regimes in 2026).

Concrete impact metrics to expect

After a disciplined rollout, teams typically see improvements in both efficiency and quality metrics within 2–6 weeks. Watch for:

Conversion rate (CVR) — should increase as low-quality clicks fall away.
Cost per conversion (CPA) — often rises or falls depending on whether low-cost, low-quality conversions were inflating results; the goal is lower true CPA for high-quality conversions.
Post-click engagement (session duration, pages/sess) — improves when you remove distracting or fraudulent placements; validate these signals in your data warehouse and reporting.
Return on ad spend (ROAS) and long-term LTV metrics — improve as acquisition quality rises.
Attribution clarity — fewer ghost conversions from low-trust placements improves multi-touch models and incrementality tests.

How to implement account-level placement exclusions (step-by-step)

Below are practical steps tailored for Google Ads in 2026, plus best practices that apply across ad ecosystems.

1. Inventory triage: build the initial blocklist

Export placement performance across all campaigns for the last 90 days: clicks, conversions, CPA, viewability, and invalid traffic flags.
Flag placements with high clicks + low engagement (e.g., CTR high but session duration < 10s) and placements with suspicious conversion patterns (many conversions but zero post-click activity).
Prioritize obvious categories first: known fraudulent apps, low-viewability domains, and unbranded YouTube content that drives irrelevant traffic.

2. Create account-level exclusion list in Google Ads

In Google Ads, navigate to Tools & Settings → Shared Library → Placement Exclusions (or the new centralized exclusions panel).
Create a new exclusion list and paste the prioritized placements (sites, app IDs, YouTube channels). Label the list clearly (e.g., “Q1-2026 CRO Blocklist”).
Apply the list at the account level. Confirm it’s applied to Performance Max, Demand Gen, YouTube, and Display where supported.

3. Roll out incrementally, not all at once

Start with a conservative blocklist (top 10–20 placements) and monitor. Ramping allows you to spot any unintended conversion drops from 'false positives' — placements that looked bad but were driving valuable niche conversions.

4. Set automated alerts and reporting

Alert on sudden shifts in CVR, CPA, or new spikes of conversions from previously unknown placements.
Connect to your data warehouse or GA4 (or server-side analytics) for post-click engagement metrics — this is crucial for validating conversion quality.

What to measure to prove improvement in conversion quality

Standard PPC metrics can be misleading after exclusions; add CRO-focused signals to the dashboard.

Primary: Macro conversions (purchase, signup) and CPA/ROAS.
Secondary: Micro-conversions (email opt-ins, add-to-cart, form starts), session duration, pages per session, bounce rate for paid traffic.
Quality signals: return visits, conversion-to-first-session delay, 7/30/90-day LTV cohorts, churn rates.
Validity checks: click-to-conversion time distributions (fraud often shows ultra-short windows), device and OS breakdowns (some bad placements skew mobile app traffic), and conversion duplicates.

Use cohort analysis to ensure that the users you keep converting post-exclusion stay engaged and monetize better over time.

What to test after you implement exclusions (actionable PPC & CRO tests)

Blocking inventory is a change to your input traffic. Treat it like a site redesign or new creative and run structured tests that confirm and amplify gains.

1. Holdout experiment (baseline vs exclusion)

Design a controlled test where 10–20% of traffic continues to run without the new exclusion list (holdout) and 80–90% runs with the exclusions. Measure macro conversions, CVR, CPA, and post-click engagement over 4–6 weeks.

Why it works: isolates the effect of exclusions from other changes.
Watch the quality uplift, not just volume drop — a small volume drop with a higher-quality conversion mix is a win.

2. Landing page alignment tests

With cleaner traffic, your landing page treatments show their real power. Run a series of A/B tests focused on message match and friction reduction:

Test hero copy that speaks to the top intent signals you now see in analytics.
Reduce form fields for audiences coming from certain placements; test progressive profiling instead of full forms.
Match creative to placement type — mobile-friendly landing pages for mobile traffic, longer-form pages for high-intent search-driven users.

3. Creative-to-placement experiments

Some placements perform poorly for one creative but well for another. After excluding low-quality inventory, run creative-level tests targeted at remaining placements to see where each creative drives the best quality conversions.

4. Bid strategy and budget reallocation

With wasted placements removed, reallocate bids to channels and placements that show improved conversion quality. Test conservative target CPA bids vs value-based bidding tuned to first-week LTV.

5. Audience and contextual tests

Account-level blocking often reveals more reliable audience signals. Test narrower remarketing windows, affinity segment bids, or custom intent audiences that align to the cleaner traffic profile.

6. Advanced: incrementality and multi-touch experiments

Run geo-based or time-based holdouts to measure lift. Use matched cohorts or synthetic control techniques to measure long-term LTV effects from higher-quality conversions.

Common pitfalls and how to avoid them

Over-blocking: Don’t exclude everything that looks “weird” without testing — you might cut off niche sources that convert highly. Start conservative and expand.
Lack of attribution updates: After exclusions, your attribution model may need recalibration. Re-run multi-touch models or incremental tests and use modern observability to monitor shifts (see monitoring patterns).
Ignoring post-click data: Don’t rely solely on on-platform conversion counts. Validate with session-level analytics or server-side events and data catalogs to confirm quality.
Reactive list management: Keep a documented cadence for reviewing exclusions (monthly). Create a change log for why a placement was added or removed. Pair this with crisis and comms playbooks to communicate changes internally and to stakeholders (playbook guidance).

Real-world examples (short case studies)

Example 1 — SaaS lead-gen: better MQL quality, lower spend

A mid-market SaaS vendor saw a surge of low-quality leads from in-app placements on Android apps in late 2025. After building a 30-domain account-level blocklist and running a 3-week holdout test, the team cut wasted spend by 22% and improved qualified lead rate by 34%. The result: less time spent qualifying leads and a 14% increase in pipeline conversion-to-opportunity.

Example 2 — Ecommerce seasonal push: cleaner data, smarter creatives

An ecommerce brand optimized for Black Friday 2025. Using account-level exclusions to block low-viewability domains and suspicious YouTube channels, their post-click engagement rose 18% and return visits improved. The team then tested new hero messaging tailored to the high-intent cohort, delivering a 9% lift in AOV.

Example 3 — Lead quality across geos

A global advertiser used account-level exclusions to remove country-specific low-trust inventory. They combined this with geo holdouts to verify lift and reallocated budgets to premium placements, improving LTV by 12% in priority markets. For a similar measurement approach see a serialized holdout case study (geo/time-based holdouts example).

Operational checklist: get from blocklist to better conversions

Export placement data (90 days) and flag low-quality placements.
Create labeled account-level exclusion list in Google Ads (e.g., Q1-2026 CRO Blocklist).
Roll out incrementally and set a 2–6 week monitoring window.
Run a holdout experiment to measure true impact on conversion quality.
Recalibrate attribution and bid strategies based on new signals.
Test landing pages and creatives with the cleaned traffic.
Document every exclusion change and schedule monthly reviews.

Future-proofing: trends to watch in 2026 and beyond

Greater automation with stronger guardrails: Platforms will offer more centralized controls like account-level exclusions; treat them as part of your standard setup.
Privacy-first measurement: As server-side and modeled conversions become mainstream, exclusion lists help maintain high-quality inputs for modeling systems. For tactical guidance on privacy-first personalization, see designing privacy-first personalization.
AI-driven placement scoring: Expect platforms and third-party tools to surface placement quality scores automatically, but always validate with your conversion-quality KPIs. (Emerging tooling for AI annotation and scoring can help — see early AI annotation patterns here.)
Integrated CRO + PPC workflows: The best-performing teams treat exclusions & landing page experiments as a joint workflow, not separate tasks.

Final takeaways — immediate actions you can take today

Start small: export placement data and create a conservative account-level exclusion list.
Run a holdout test to prove impact on conversion quality, not just volume.
Use post-click engagement and LTV cohorts to validate that remaining traffic is higher-quality.
Treat exclusions as an ongoing optimization lever — pair them with landing page and bid strategy tests for compound gains.

Account-level placement exclusions are not a silver bullet, but in 2026 they’re a high-leverage control: they reduce waste, improve the fidelity of your conversion data, and let CRO and PPC teams focus on tests that actually move business metrics. Implement them thoughtfully, measure rigorously, and use the cleaner traffic to run smarter experiments.

Call to action

If you manage paid channels and conversions, take 30 minutes this week: export placement-level data, create a small account-level exclusion list, and launch a 2-week holdout test. If you want a ready-made template and step-by-step dashboard, download our CRO-ready exclusion & testing playbook — it includes the workspace for the exact reports and alerts you should use in 2026.

clicky

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Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.