Case Study: How Centralized Placement Exclusions Improved Brand Safety for a Travel Advertiser

Hook: When brand safety eats into reach, travel marketers lose bookings — fast

Travel advertisers in 2026 face a double bind: demand is rebalanced across markets and brand loyalty is shifting as AI-driven channels reshape how people discover and book trips. At the same time, ad platforms have pressed forward with automation (Performance Max, Demand Gen), which can amplify reach — and risk — if controls are fragmented. This case study shows how one travel advertiser used centralized placement exclusions — leveraging Google Ads' account-level exclusions rolled out in January 2026 — to reduce unsafe inventory spend, improve ad performance, and protect long-term loyalty without sacrificing reach.

Executive summary: Quick results for busy leaders

In a 12-week rollout (Q4 2025 to Q1 2026), the advertiser reduced unsafe-placement spend by 85%, improved viewability by 23%, lowered CPA by 18%, and realized a 15% uplift in incremental bookings on controlled test campaigns. These outcomes came from a centralized, data-driven inventory blocking program aligned to loyalty and remarketing tiers, third-party verification, and a strict measurement framework that used holdouts for causation.

About the advertiser (anonymized)

“VoyageCo” is a mid-size online travel brand operating in 10 markets, with a marketing mix spanning Performance Max, Demand Gen, YouTube, Display, and programmatic buys. They rely on both new-customer acquisition and loyalty-driven repeat bookings. By late 2025 they were seeing higher variance in booking efficiency across markets and a small but growing number of brand-safety incidents tied to UGC and low-quality app inventory.

Why centralized placement exclusions mattered in 2026

There were three converging trends that made a single, account-level approach essential:

• Google's January 2026 update introduced account-level placement exclusions across Performance Max, Demand Gen, YouTube, and Display — removing the need to replicate lists campaign by campaign.
• Automation-first formats increasingly control placement decisions; without centralized guardrails, unsafe inventory can be surfaced automatically and at scale.
• Travel demand rebalancing (Skift, late 2025) meant growth was uneven across markets, so manual, fragmented lists risked blocking valid inventory in emerging regions while failing to protect core markets.

Baseline audit: Where VoyageCo started

Before changes, VoyageCo's Q3–Q4 2025 performance showed:

• Monthly ad spend: $1.2M
• Spend on placements flagged by verification vendors as “low-quality” or risky: ~4% (~$48k/month)
• Brand-safety incidents (reported/verified): 12 in 90 days
• Overall viewability: 55%
• Cross-channel CPA (bookings): $120
• ROAS (last-click attribution): 2.0
• Repeat-booking rate: 22% amongst users acquired in the prior 12 months

Strategy overview: Principles that guided implementation

The team set three non-negotiable principles:

1. Protect the brand with minimal impact on reach — use tiered exclusions so high-trust loyalty and remarketing campaigns keep full access.
2. Make exclusions data-driven and reversible — use short holds, measurable lift tests, and easy rollback via account-level lists and API automation.
3. Operationalize governance — create clear ownership, approval workflows, and quarterly reviews tied to market shifts.

Implementation: Step-by-step

1) Inventory & risk mapping (week 1–2)

Pull and combine:

• Platform placement reports (Google Ads placement reports, DV/IAS feeds)
• Programmatic partner lists
• CRM-driven ad interaction data (GA4/server-side)

Tag placements by risk category: Fraud, Low Quality, Sensitive Context, and Unknown. Cross-reference with recent booking conversion rates and LTV by market so you don’t block efficient channels in high-growth regions.

2) Define tiered blocklists (week 2–3)

Instead of a single “all-or-nothing” block, VoyageCo created three lists:

• Safety-critical account-level blocklist — sites/apps that are always blocked globally (hate, violence, explicit adult, fraud-heavy domains).
• Performance blocklist (campaign-level) — placements that underperform for new acquisition campaigns but may still be useful for remarketing/loyalty.
• Regional exceptions list — allows select inventory in growth markets where the inventory is trusted locally but unknown to global models.

3) Apply central exclusions (week 3)

Using Google Ads' account-level exclusion feature, the Safety-critical list was applied across all eligible Google campaigns (Performance Max, Demand Gen, Display, YouTube). Programmatic partners were updated with the same list via shared S3-hosted exclusion files and prebid filters.

4) Verification & layered controls (week 3–4)

Integrations with DoubleVerify and Integral Ad Science were enabled. The team also activated contextual targeting layers using taxonomy scoring (IAB categories) and excluded high-risk content categories at the platform level.

5) Measurement design & holdout tests (ongoing)

To prove causality, the team set up:

• A 12-week test window with a 10% holdout (no centralized exclusions) against 90% treatment (full centralized exclusions).
• Primary metric: incremental bookings per 1,000 impressions (lift adjusted for seasonality).
• Secondary metrics: CPA, viewability, brand-safety incidents, and repeat-booking rate at 90 days.

Tools, APIs and automation

Key technical moves:

• Programmatic: Use of S3-hosted master blocklist updated hourly and distributed via prebid adapters.
• Google Ads: Account-level exclusion list via the new API endpoint plus campaign-level overrides.
• Verification: DV/IAS connected to BigQuery exports for cross-platform analytics.
• Reporting: Server-side tagging to unify click, impression, and booking events for accurate attribution, minimizing losses from platform signal changes.

Results: Before vs. after (12-week test)

Below are VoyageCo’s aggregated results comparing the 90-day pre-test period to the 12-week test period. All dollar figures are USD.

Brand-safety & inventory

• Unsafe placement spend: From $48k/mo to $7.2k/mo (85% reduction)
• Brand-safety incidents: From 12 in 90 days to 1 in 90 days (92% reduction)
• Viewability: From 55% to 68% (+23%)

Performance metrics

• Overall CPA: From $120 to $98 (-18%)
• Conversion rate on controlled channels: From 3.1% to 3.78% (+22%)
• ROAS (attribution-aware): From 2.0 to 2.5 (+25%)
• Incremental bookings (treatment vs holdout): +15% per 1,000 impressions
• Repeat-booking rate (cohort): From 22% to 25.5% (+3.5 pp)

What moved the needle?

Several causal factors were validated by the holdout:

• Removing low-quality inventory reduced noise and improved CTRs and viewability, which fed better signals into automated bidding.
• Higher-quality placements increased downstream engagement and conversions, particularly for long-funnel travel purchases.
• Better alignment with loyalty segments (keep remarketing excluded from account-level blocking) retained efficient repeat-customer paths.

Centralized exclusions improved the signal-to-noise ratio. Better inventory == better learning for automation == better performance.

Tradeoffs and how they were managed

Blocklists can reduce scale if applied too aggressively. VoyageCo handled this by:

• Applying only a narrow, safety-critical account-level list globally; using campaign-level lists for performance tuning.
• Creating regional exception zones so lower-known inventory in high-growth markets wasn't automatically blocked.
• Running weekly inventory reviews to release false positives quickly and reduce unnecessary reach loss.

Lessons learned — practical takeaways for travel advertisers

1) Start with a risk-weighted inventory map

Don’t block first and ask questions later. Build a risk-weighted map that folds in conversion performance and market context. In 2026, with demand rebalancing, what’s risky in one market may be valuable in another.

2) Use tiered exclusions — safety vs. performance

Keep a strict account-level safety list and separate campaign-level performance lists. That preserves reach for loyalty marketing and small-market growth while enforcing brand safety globally.

3) Integrate verification into attribution and reporting

Link DV/IAS flags back to your ad server and CRM via BigQuery or server-side exports. This gives you a single source of truth for measuring the impact of exclusions on conversions and lifetime value.

4) Measure with holdouts and incremental lift

A/B tests with holdouts are the only way to prove the exclusion program is causally improving bookings and LTV, not just shifting spend.

5) Automate but keep human oversight

Use API-driven exclusion updates and hourly syncs, but maintain weekly human review to catch market nuances and false positives, especially as AI-driven content and UGC evolve.

6) Make exceptions for loyalty and remarketing

Users already in your CRM often respond differently to placements. Preserve remarketing reach where contextual fit is acceptable and LTV justifies risk tolerance.

Advanced strategies for 2026 and beyond

As platforms and creative ecosystems continue to change, advanced teams should consider:

• Contextual + semantic scoring: Use real-time NLP and semantic classifiers to score page-level risk instead of relying solely on domain lists.
• Dynamic exclusion lists: Auto-adjust exclusions by market and by time-of-day using conversion signals and seasonality models.
• Signal-forwarding to automation engines: Feed verified-quality signals into Google and DSPs (where supported) so automated bidding can prefer high-quality inventory.
• Hybrid measurement: Combine holdout-based lift tests with MMM and advanced MTA to model long-term loyalty effects of safer inventory.
• Creative-safe matching: Pair high-sensitivity creatives (brand films, loyalty propositions) with the strictest placement controls via placement whitelists.

Future predictions: How brand safety and loyalty will evolve

Based on late 2025–early 2026 trends, expect:

• Platform-level guardrails will expand. More platforms will offer account-level, cross-format exclusions and native controls as advertisers demand fewer manual processes.
• Contextual intelligence will matter more than domain blacklists. AI-driven content creation and UGC will continue to blur domain-level signals, so page-level semantics will be the new frontier.
• Loyalty marketing will become more personalized and atomized. Travel brands will lean into CRM-first buys and server-side match to preserve high-LTV audiences while excluding risky inventory for prospecting channels.
• Verification signals will be tied into ad auction preferences. Expect DSPs and ad exchanges to offer quality bids that incorporate third-party verification scores natively.

Governance checklist: Quick operational blueprint

• Designate an owner for account-level exclusions and a cross-functional review committee.
• Maintain three lists: safety-critical (global), performance (campaign), and regional exceptions.
• Sync blocklists via API/S3 hourly and export verification signals to a central BI instance.
• Run 10% holdout tests quarterly to validate impact.
• Review and update lists monthly with market and creative teams.

Real-world checklist: First 30 days

1. Run a placement and verification audit.
2. Build the safety-critical account-level list and apply it across Google Ads and programmatic partners.
3. Create campaign-level performance lists for experimentation.
4. Enable DV/IAS reporting and export to BigQuery.
5. Start a 12-week holdout test and baseline measurement.

Closing thoughts

For travel brands in 2026, brand safety is not just a compliance or PR task — it’s a growth lever. Centralized placement exclusions — when executed with data, measurement, and market nuance — protect the brand while improving ad performance and the quality of audience signals feeding automated bidding. VoyageCo's case shows it’s possible to both tighten safety and lift bookings when you design exclusions as part of a broader measurement and loyalty strategy.

Call to action

If your travel brand is balancing rebalanced demand and shifting loyalty, start with a 30-minute assessment: we’ll audit your current placement exposure, recommend a tiered exclusion plan, and outline a measurement design tailored to your markets. Visit admanager.website or contact our team to schedule a demo and get a custom action plan for centralized placement exclusions and loyalty-driven ad optimization.

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