Account-Level Placement Exclusions: A Practical Playbook to Boost Conversion Rates

Fix wasted ad spend and low conversion rates with one account-level setting — a step-by-step CRO playbook for Google Ads

If your campaigns burn budget on irrelevant sites, YouTube channels, or low-quality apps while conversions lag, you’re not alone. The new account-level placement exclusions in Google Ads (rolled out January 15, 2026) finally gives advertisers a centralized, scalable way to block unwanted inventory across Performance Max, Demand Gen, YouTube and Display — but only if you use them strategically. This playbook shows exactly when to apply exclusions, how to implement them safely, and how to prove measurable conversion uplift and improved budget efficiency.

Why account-level exclusions matter in 2026

Automation (Performance Max, smart bidding, and broader AI-driven delivery) continues to dominate Google Ads. That automation needs reach and signal to optimize; at the same time, brands demand stronger guardrails. The January 2026 update that added account-level placement exclusions solves a key pain point: fragmented controls. Instead of copying exclusion lists across campaigns, you now maintain a single source of truth.

What changed (Jan 15, 2026): Google Ads lets advertisers apply one exclusion list at the account level; it applies across Performance Max, Demand Gen, YouTube and Display campaigns.

This matters for CRO because placement quality directly affects conversion rate, lead quality and CPA. The right exclusions can reduce wasted impressions, increase conversion rate, and free budget for high-intent placements.

The conversion-first framework

Use this three-part framework when adding account-level placement exclusions: Audit → Hypothesize → Test & Measure. Each stage translates into explicit steps and decision rules you can operationalize.

1. Audit: Find placement problems and signal quality

Start with a data-driven inventory audit to identify where low-quality placements are siphoning spend.

1. Pull placement reports (last 30–90 days): Include Display, YouTube placements, app package names, and campaigns using Performance Max or Demand Gen. Use the Google Ads UI Reports, the Report Editor, or the Google Ads API.
2. Segment by conversion quality: Look at conversion rate, CPA, conversion value per conversion, and assisted conversions (if relevant). Also surface metrics like view-through conversions for video-heavy placements.
3. Flag high-traffic, low-conversion placements: Create flags for placements with CTR or conversion rate below your account median and disproportionate spend.
4. Enrich with qualitative signals: Consider landing page mismatch (placement content vs. offer), brand-safety flags, ad fraud indicators (unexpected spikes in eCPA or low session duration), and third-party verification data (IAS, MOAT) if you use it.

Quick audit checklist (template)

• Time window: last 30/60/90 days
• Metrics: Spend, Impressions, Clicks, CTR, Conversions, Conversion rate, CPA, ROAS, View-through conversions
• Segments: Campaign type (PMax/Demand Gen/YouTube/Display), Device, Geography, Placement URL/Channel/App
• Flags: High spend & low conversions, high invalid traffic, brand-safety concerns

2. Hypothesize: Build exclusion rules and risk profiles

Translate audit findings into hypothesis statements and an exclusion risk profile (how aggressive you’ll be). The hypothesis should tie exclusions to expected CRO outcomes.

• Example hypothesis: "Excluding placements in the ‘incentivized-apps’ and top-50 low-converting YouTube channels will increase account-level conversion rate by 12% and reduce CPA by 18% within 30 days."
• Create a risk profile:
• Conservative: Exclude only placements with confirmed high invalid traffic or brand-safety issues.
• Balanced: Exclude low-converting, high-spend placements and low-quality apps/channels.
• Aggressive: Apply broad categories (e.g., app store categories, known incentivized domains) — use only for mature accounts after testing.

3. Test & Measure: Implement using a controlled experiment

Don’t flip the account-block switch and hope for the best. Run a controlled experiment so you can attribute conversion uplifts to exclusions, not seasonality or bid changes.

1. Create a baseline: Record key metrics for at least 14–30 days before changes: total conversions, conversion rate, CPA, ROAS, impression share, and spend by campaign type.
2. Set up a holdout group: If possible, run exclusions in only part of the account (e.g., a subset of campaigns, or duplicate campaigns) or use geo-based holdouts. For enterprise accounts, use account-level exclusions on a mirrored account if your structure allows.
3. Apply exclusions incrementally: Phase 1: conservative list (brand-safety/high invalid traffic). Phase 2: add low-converting placements. Phase 3: optional aggressive blocks.
4. Control for bid/creative changes: Don’t change bidding strategies or creative during the test window unless the change is part of the experiment.
5. Duration & statistical confidence: Minimum 14 days for stable traffic channels; 30+ days recommended for granular significance. Use a statistical test or tools like Google Ads experiments and lift testing to confirm significance.
6. Measure uplift: Key KPIs: relative lift in conversion rate, % change in CPA, spend reallocation to higher-performing placements, and conversion quality (lead-to-opportunity rate if CRM-connected).

Step-by-step: Implementing account-level placement exclusions

The implementation steps are straightforward. Follow them in sequence and document changes in your change log.

Step 1 — Create the exclusion list

• In Google Ads: Tools & settings → Shared library → Placement exclusions (or equivalent under the new UI)
• Click "+ New exclusion list" and name it clearly (e.g., "Q1-2026 Account-level Exclusions — Phase 1").
• Add placements using domains, YouTube channel URLs, channel IDs, or app package names. Use comments for why each item was added (data-driven reason).

Step 2 — Apply at account level

• Apply the exclusion list to the account (not campaign). Confirm the list applies across Performance Max, Demand Gen, YouTube and Display — verify eligibility for each campaign type.
• Document the date/time and the list version. Export a copy of the list for version control and rollback.

Step 3 — Monitor closely (first 48–72 hours)

• Check spend redistribution; high-quality placements may receive more budget as ads stop showing on excluded placements.
• Watch for dips in conversions that indicate over-exclusion. If conversions drop, review the list and rollback items selectively.
• Track conversion rate, CPA, and share-of-traffic changes daily for the first week, then weekly thereafter.

Step 4 — Iterate and scale

• After a successful Phase 1, add Phase 2 items and repeat the testing process.
• Use exclusions as a living document: prune items that no longer warrant blocking and add newly flagged placements.
• Share results with stakeholders: conversion uplift, reduced wasted spend, and examples of reallocated budgets.

How exclusions translate into measurable conversion uplift

Account-level exclusions impact conversion metrics via two core mechanisms:

• Waste reduction: Removes low-intent or fraudulent inventory, improving overall conversion rate and lowering CPA.
• Signal clarity: By removing noisy placements, your automated bidding algorithms receive cleaner signals from higher-quality conversions, improving bid efficiency and ROAS.

Quantifying uplift — measurement best practices

• Primary metric: Conversion rate change (account-level and by campaign type).
• Secondary metrics: CPA, conversion value/ROAS, conversions per 1,000 impressions (conv/1k), and lead-to-opportunity conversion in CRM.
• Incrementality testing: For reliable attribution, use geo experiments, campaign duplication with holdout, or Google’s experimental tools to measure true incremental conversions.
• Time-lagged effects: Allow 14–30 days post-implementation to see stabilized effects — especially for video and display which have view-through and longer conversion windows.

Example outcomes (typical ranges from industry cases in 2025–2026)

• Conservative exclusions: 5–10% conversion rate increase, 8–12% CPA reduction.
• Balanced exclusions: 10–18% conversion rate increase, 12–22% CPA reduction.
• Aggressive exclusions (mature accounts with controlled tests): 18–30% conversion rate increase and meaningful quality-of-lead uplift.

Risk management & common pitfalls

Account-level exclusions are powerful — but misuse can harm volume and automation learning.

• Over-excluding: Removing too much inventory starves automated algorithms of signal and may increase CPA. Avoid mass blocking in early stages.
• Attribution confusion: Ensure your conversion windows and attribution models remain consistent during tests. Google’s shift toward modeled conversions (post-ATtribution changes since 2023) means you should pair platform metrics with CRM-level validation.
• Ignoring creative/landing issues: Low conversion rates are not always placement-related. Confirm landing pages, offers and creatives are optimized before aggressive exclusions.
• Not documenting changes: Use a change log and versioned exclusion lists to track what was applied and when; this makes rollback and analysis easier.

Advanced tactics: automation, scripts, and API integration

For large accounts and agencies, manual management is slow. Use automation to maintain and scale your exclusion strategy.

Automation ideas

• Automated placement flags: Use Google Ads scripts or BigQuery exports (if using Google Ads data exports) to flag placements with recurrent low conversion rates or high invalid traffic.
• Version control via APIs: Use the Google Ads API to push and version exclusion lists programmatically. Maintain a central CSV or database with reason codes and campaign tags.
• Third-party feeds: Ingest brand-safety and fraud feeds (IAS, Integral Ad Science, DoubleVerify) into your exclusion process.

Pseudocode (high-level) for automated exclusion workflow

<!-- Pseudocode; check API docs for exact calls -->
fetch placement_performance(last_30_days)
filter placements where spend > threshold AND conversion_rate < account_median
for each placement in filtered_list:
  score = compute_risk_score(ctr, conv_rate, invalid_traffic_score)
  if score > exclusion_threshold:
    add_to_exclusion_list(placement, reason_code)
push_exclusion_list_to_google_ads(account_level=true)
notify_stakeholders(summary)

Case study (compact): 2025 e‑commerce account — balanced approach

Context: Mid-market e-commerce advertiser running Performance Max + Display campaigns. Problem: High spend on low-converting apps and unknown YouTube channels. Audit revealed 22 domains and 15 apps responsible for 28% of display spend with conversion rate 40% below account median.

• Action: Built Phase 1 exclusion list (brand-safety + top offenders) and applied at account level. Ran a 30-day holdout by duplicating 20% of campaigns as controls.
• Result: Conversion rate increased 14% in test group vs control; CPA decreased 16%. Savings were reallocated to high-performing video inventory, lifting ROAS by 9%.
• Learning: Incremental reach fell 6% but revenue per user rose, improving LTV-focused metrics.

Templates & quick resources

Use these practical templates to accelerate implementation:

• Exclusion list template (CSV): columns = placement_url, placement_type, reason_code, date_flagged, flagged_by, data_evidence_link
• Audit report template: dashboard with placements filtered by spend > X and conv_rate < Y
• Experiment plan doc: hypothesis, test population, baseline metrics, duration, success criteria, rollback plan

2026 trends and future predictions

Looking ahead in 2026, expect these developments:

• More centralized control features: Google and other ad platforms will continue to add account-level guardrails as automation expands.
• Tighter measurement integration: With increased focus on privacy and modeled conversions, advertisers will lean on incrementality testing and CRM-linked outcomes more than last-click metrics.
• Exclusions become dynamic: Automations that adjust exclusion lists in near-real-time (based on performance thresholds or third-party signals) will become mainstream for enterprise accounts.

Final playbook checklist

1. Run a 30–90 day placement audit with key metrics.
2. Develop hypothesis and risk profile (conservative → aggressive).
3. Create a versioned account-level exclusion list with documented reason codes.
4. Apply exclusions incrementally and run a controlled experiment (holdout or geo test).
5. Measure conversion rate lift, CPA change, and conversion quality (CRM verification).
6. Iterate, automate, and document every change.

Conclusion — turn exclusions into predictable CRO wins

Account-level placement exclusions are a practical new lever for CRO in the era of automated ad delivery. When applied as part of a disciplined audit‑test‑measure cycle, exclusions reduce waste, clarify signal for automated bidding, and produce measurable conversion uplifts. The key is to test incrementally, track the right KPIs, and automate routine updates so your exclusion strategy scales without disrupting learning.

Ready to implement? Download our exclusion list CSV template and experiment plan, or book a 30‑minute audit with our conversion team to map your Phase 1 list. Use account-level exclusions smartly — they can be the fastest path to higher-quality conversions and better ROI in 2026.

Call to action

Start your 30‑day exclusion experiment today. Request the template or an audit at convince.pro/CRO-audit — and get a prioritized, data-backed account-level exclusion list you can apply in under an hour.

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