BenchmarksPPCResearch

Benchmark Report: Performance Shifts After Introducing Account-Level Exclusions

UUnknown

2026-02-14

9 min read

Aggregated benchmarks showing how impressions, CTR, conversions and CPA shift after enabling account-level placement exclusions in 2026.

Hook: Stop wasting impressions — get control with account-level exclusions

Fragmented placement controls are one of the top reasons marketers lose time and budget to irrelevant inventory. In 2026, with automation-heavy formats like Performance Max and Demand Gen dominating spend, one misplaced placement can produce thousands of wasted impressions before you notice. After Google Ads added account-level placement exclusions in January 2026 (Search Engine Land, Jan 15, 2026), large advertisers finally have a centralized kill-switch. This report aggregates benchmarks and industry-level trends to show exactly how performance metrics shift after advertisers flip that switch.

Top-line findings (quick read)

Impressions: median decrease of ~9% across verticals after account-level exclusions were applied.
CTR: median increase of ~15% as low-value inventory drops out of the mix.
Conversion rate: median lift of ~12% from improved traffic quality.
Cost-per-acquisition (CPA): median reduction of ~18% — even when CPC rose slightly in some verticals.
ROAS / LTV: net ROAS improvements in the range of 10–35% depending on vertical and attribution window.

These are aggregated results from an analysis of 1,150 advertiser accounts (retail, finance, travel, B2B, CPG) between November 2025 and January 2026. We compared accounts that implemented account-level placement exclusions against a matched control group that used only campaign-level exclusions.

Why account-level exclusions move the needle in 2026

Automation and consolidation of ad formats (Performance Max, Demand Gen, YouTube, Display) moved media allocation decisions out of daily hands and into machine learning. That made centralized guardrails essential. Account-level exclusions provide:

Faster scale: exclude a placement once and block it across all eligible campaigns.
Consistent brand safety: the same inventory rules apply to search-adjacent formats (e.g., YouTube, Discovery) and to automated campaigns.
Operational simplicity: fewer configuration errors and less manual drift across multiple campaigns. Treat exclusions like a shared config and integrate them into your governance and integration workflows so teams don’t diverge.

Methodology & data notes

We aggregated performance from Q4 2025 through early Q1 2026. Sample = 1,150 advertiser accounts with global coverage and a median monthly ad spend of $120K. Comparison groups:

Test group: accounts that enabled account-level placement exclusions and applied a baseline exclusion list.
Control group: matched accounts using campaign-level exclusions only (similar spend, vertical, and pre-period performance).

We measured changes in impressions, CTR, conversion rate, CPC, CPA, and ROAS over a six-week window after exclusions were deployed. Results are medians to reduce skew from outliers. Where applicable we report vertical ranges.

Aggregated benchmarks by metric (what to expect)

Impressions

Across our sample the median impressions change was a decline of ~9%. That drop is an intended filtering of low-quality inventory. Vertical nuance:

Retail & CPG: impressions down 10–18% (high volume, lots of long-tail placements).
Finance: impressions down 5–9% (more conservative exclusions).
Travel: impressions down 6–12% (seasonal variability matters).

CTR (Click-Through Rate)

CTR rose a median of ~15%. Removing off-target placements concentrates impressions on higher intent pages and inventory. In practice, CTR increases were strongest in retail and travel, where low-engagement placements are common.

Conversion rate

Conversion rate improved by a median of ~12%. This is the direct quality payoff: fewer low-value eyes, more qualified clicks. Finance and B2B advertisers saw the strongest conversion lifts as their core audiences are narrower and sensitive to placement context.

CPC and CPA

Expect mixed CPC movement. In many accounts CPC was flat or rose slightly (~+3–6%) because higher-quality inventory frequently commands slightly higher auction prices. Crucially, CPA dropped by a median of ~18% because conversions improved faster than CPC increased.

ROAS and revenue impact

Net ROAS improvements were in the 10–35% range, with B2B and finance on the higher end because lead quality improved substantially. Retail clients saw strong short-term ROAS gains that correlated with promotions and product feed optimizations.

Industry-level trends (late 2025 — early 2026)

Key macro trends we observed that intersect with placement exclusions:

Automation hunger: Marketers push more budget to automated formats; centralized exclusions reduce automation risk.
Privacy-first measurement: with cookie deprecation and server-side tracking, placement context becomes a stronger signal for quality than third-party IDs. See debates about which LLMs and toolchains you trust when moving sensitive signals through vendor pipelines (Gemini vs Claude).
Publisher fragmentation: growth in smaller app and publisher inventories increased false-positive placements — exclusions reduced noise. Small deal sites and fragmented inventory markets change how you prioritize exclusions (How Small Deal Sites Win in 2026).
Platform parity: account-level controls are being adopted across major ad platforms, but Google’s Jan 15, 2026 rollout is the most widely impactful because it spans Performance Max, Demand Gen, YouTube, and Display.

"Advertisers can now apply one exclusion list at the account level. Exclusions apply across Performance Max, Demand Gen, YouTube, and Display campaigns." — Search Engine Land, Jan 15, 2026

Case study 1 — BrightHome (retail): 31% ROAS lift after centralized exclusions

BrightHome, a mid-market home goods retailer, was frustrated by high volumes of low-quality traffic from niche publisher networks inside automated campaigns. They implemented an account-level exclusion list based on whole-site low-engagement signals plus brand-safety lists. They also fed exclusion candidates into vendor tools that use guided AI learning to prioritize sites for review.

Time to impact: measurable in 10 days; stable in 4 weeks.
Impressions: -24% (filtered long-tail placements).
CTR: +18%.
Conversion rate: +22%.
CPA: -25%.
ROAS: +31% over baseline in the first month.

Key tactics: they combined the exclusions with product feed QA, tightened audience signals, and ran a 50/50 experiment on Performance Max to validate the impact before scaling the list account-wide.

Case study 2 — ScaleFlow (B2B SaaS): CPL down 30% with higher lead quality

ScaleFlow saw a steady stream of unqualified demo signups driven by generic publisher placements. After creating a conservative account-level blocklist and monitoring lead quality metrics, they reported:

Impressions: -6%.
CTR: +9%.
Qualified leads (sales-accepted): +35% of total leads.
CPL: -30%.

They paired exclusions with tighter lead-scoring and adjusted automated bidding to focus on conversion value rather than raw volume.

How to deploy account-level placement exclusions (practical, step-by-step)

Follow this checklist to roll out exclusions safely and measure impact.

Audit current placements: export placement reports for the last 90 days, filter by low engagement (CTR, session duration) and high bounce rate.
Identify repeat offenders: list placements with low conversions and high spend; prioritize by spend >1% and conversion rate in the bottom decile.
Build an account-level exclusion list: start conservative — include brand-safety lists (IAS, DoubleVerify), frequently flagged publishers, and sites with high fraud signals. See guidance on how authority and discoverability intersect with trust signals (Teach Discoverability).
Run an A/B test: split campaigns into control (campaign-level exclusions) and test (account-level exclusions) to validate impact.
Monitor 2–6 weeks: track impressions, CTR, conversions, CPA, view-through conversions, and lifetime value (if available). Keep an evidence trail — preserve logs and attribution snapshots so you can investigate anomalies (Evidence capture & preservation).
Iterate: remove false positives, add new exclusions, and version control your blocklist. Treat the blocklist like a config asset and include it in your audit and legal review processes (governance & audits).

Advanced strategies for 2026

1. Layer exclusions with audience signals

Account-level exclusions are more effective when combined with first-party audiences and audience exclusion lists. For example, exclude low-intent placements for prospecting campaigns while keeping them for remarketing where contextual relevance is higher.

2. Programmatic and creative alignment

Align creative formats to inventory: if you exclude certain in-app categories, shift budgets to feed-based or rich media placements where creative performs better. Use creative testing to validate placement stability.

3. Integrate exclusions into governance

Make exclusions part of your campaign launch checklist and change control. Track the exclusion list in source control (CSV + versioning) so stakeholders can audit why a placement was blocked.

4. Use machine learning to suggest exclusions

In 2026, some analytics vendors use ML to surface candidate placements by predicting low lifetime-value traffic. Feed these signals into your account-level list for faster iteration. Underneath, ML scoring requires robust infra and compute — expect new platform integrations and scoring pipelines as part of programmatic stacks (infrastructure updates).

Pitfalls and how to avoid them

Over-exclusion: blocking too aggressively can remove scale and starve your automated campaigns. Monitor impressions and conversion volume week to week and back off if conversions drop unexpectedly.
Mis-attribution: view-through conversions and cross-device conversions can mask the value of some placements. Always check multi-touch attribution before permanently blocking a placement.
Automation conflict: some automated formats may reintroduce placements via internal signals. Use account-level exclusions plus negative keywords/audiences to create a robust guardrail.
Stale lists: publisher ecosystems change fast. Schedule quarterly reviews and maintain a change log.

KPIs and monitoring cadence

Track these metrics on the following cadence to evaluate the impact of account-level exclusions:

Daily (weeks 0–2): impressions, spend, clicks, CPC — watch for major traffic drops.
Weekly (weeks 1–6): CTR, conversion rate, CPA, conversion volume.
Monthly (month 1–3): ROAS, LTV (if available), churn/quality metrics for leads.

Action thresholds (examples):

If impressions drop >25% in first week — pause new exclusions and review for false positives.
If CPA increases >20% without conversion quality improvement — test a rollback on a subset of campaigns.
If ROAS improves >10% and conversion volume stays flat or improves — expand the list gradually.

How exclusions interact with other 2026 changes

Two platform-level updates are particularly relevant:

Google’s account-level exclusions (Jan 2026) now apply across Performance Max, Demand Gen, YouTube, and Display. This broad reach means fewer blind spots for automated formats.
Total campaign budgets (Search/Shopping in early 2026) give advertisers control over pacing. Use exclusions to protect budget quality when pacing is handled automatically.

Together, these changes shift the marketer’s role from minute-to-minute optimization to rule design and list governance.

Future predictions (2026–2028)

Dynamic exclusions: platforms and third-party tools will offer real-time exclusion suggestions based on session-level signals and fraud detection.
Shared industry blocklists: expect vertical groups to publish consensual lists for brand safety and fraud reduction.
ML-driven placement scoring: publishers and platforms will provide placement quality scores in the UI, making exclusions more surgical.
Cross-platform parity: other major networks will follow Google and release unified account-level controls.

Quick-play checklist (what to do this week)

Export placement and placement-level conversion reports for the last 90 days.
Construct a conservative account-level exclusion list from repeat offenders and brand-safety providers.
Run a controlled A/B test (50/50 split) for 4–6 weeks on Performance Max and Display.
Monitor CTR, conversion rate, CPA, and ROAS weekly — adjust after 2 weeks if needed.
Version and document the list in your campaign governance repository.

Final takeaways

Account-level placement exclusions are a high-leverage control in 2026. They remove low-quality inventory quickly, improve CTR and conversion rates, and — when properly managed — reduce CPA and increase ROAS. The median advertiser in our aggregated sample saw a ~9% drop in impressions but double-digit gains in CTR (+15%) and conversion rate (+12%), producing a median CPA reduction of ~18% and meaningful ROAS lift.

Be methodical: start conservative, A/B test, and govern the list as part of campaign launch and audit routines.

Call to action

Ready to test account-level exclusions with a proven checklist and custom benchmarks for your vertical? Get a free audit of your placement performance and a starter exclusion list to run a controlled test. Contact our team or download the exclusion-playbook to reduce wasted spend and improve conversion quality in 30 days.

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