Auction Health Scores: A Seller-Side Framework to Predict and Prevent Header Bidding Revenue Slumps

Introduction

Revenue slumps do not begin on invoicing day. They begin silently inside your auctions: bidding thins out, timeouts creep up, a floor change collides with seasonality, a sellers.json entry drops, or identity coverage falls behind browser changes. By the time daily revenue reporting tells the story, the lost dollars are already gone. Auction Health Scores give supply-side teams a way to see trouble early and act with precision. The idea is simple in spirit and powerful in practice: continuously compute a composite health indicator for each auction surface and its demand paths, then alert on deviations with opinionated, step-by-step remediation. In this thought piece, I will outline a practical seller-side framework that Red Volcano customers can adopt across web, app, and CTV. We will define the core metrics that matter, propose a scoring model, show code-level examples for collection and scoring, and walk through how to interpret and act on the results. We will also connect the framework to transparency standards like ads.txt, app-ads.txt, sellers.json, and SupplyChain, along with instrumentation opportunities in Prebid. Where relevant, I will cite resources from IAB Tech Lab and Prebid documentation so you can align operationally and technically with industry norms :cite[ekx,aqj,a41,ajm,bpp,bfd,cr2,dr7]. The goal is not another dashboard. The goal is a predictive, operations-first system that keeps your auctions healthy and your revenue stable before the end of month scramble begins.

Why header bidding revenue slumps happen

Revenue slumps often arise from layered, compounding issues. A single production change rarely sinks performance alone. Problems compound across supply signals, demand connectivity, and policy. On web, common drivers include:

Rising timeout rate: Network variability, heavier page payloads, or slow analytics scripts reduce time-in-auction for bidders.
Floor misalignment: Aggressive floor increases reduce bid density and suppress win rates, especially when seasonality softens demand.
Identity and privacy shifts: Declines in addressability or enforcement changes reduce effective CPMs and buyer match rates.
Transparency drift: Out-of-date ads.txt or sellers.json breaks trusted supply paths and throttles bid throughput.
Client-side tax: Inefficient wrapper configuration, high bidder counts, or sequential tasks eat into auction timeout windows.

On app and SDK-based environments, these patterns change:

SDK version fragmentation: Mixed versions across installs produce inconsistent demand behavior and error rates.
Connectivity volatility: Mobile network transitions spike timeouts and drop bidder participation.
Store metadata drift: app-ads.txt inaccuracies disrupt authorized selling and deprecate demand confidence.

In CTV and SSAI environments, new fault domains appear:

Pod construction and policies: Ad pod rules, competitive separation, and category exclusions reduce fill unpredictably.
SSAI insertion failures: Manifest manipulation errors, DRM mismatches, or beaconing issues create delivery gaps.
Complex supply chains: Multi-hop reselling paths increase the risk of invalid supply paths and buyer throttling.

Auction Health Scores address these realities head-on. Instead of waiting for revenue to reflect issues, you monitor the auction primitives directly and convert them into an actionable composite that correlates strongly with yield stability.

What is an Auction Health Score

An Auction Health Score is a composite index, typically 0 to 100, computed at a cadence you control, for entities you care about. The entity can be a site, app bundle, channel, ad unit, bidder seat, or a supply path segment. The cadence is usually hourly or daily, with rolling baselines for seasonality and event detection. Key characteristics:

Composite: Combines multiple independent metrics into a single, interpretable number.
Predictive: Incorporates leading indicators like timeout rate and bid density that precede revenue impact.
Explainable: Surfaces the top contributing factors and points to precise fixes, not just anomaly detection.
Scope-aware: Calculated per surface, per demand path, and aggregated for hierarchy reporting.
Standard-aligned: Uses transparency and supply chain standards to validate healthy supply disclosure :cite[ekx,aqj,a41,ajm,bpp].

The score answers one question daily: How healthy were my auctions, and if the score fell, what should I fix first?

The signals that matter

Every publisher and SSP stack is unique. That said, the following signal categories generalize well across web, app, and CTV. The first five are usually the strongest predictors in practice: timeout rate, bid density, win rate, floor collision rate, and transparency compliance.

Demand dynamics

Bid rate: Percent of ad requests that receive at least one bid.
Bid density: Average bids per request per auction surface.
Win rate: Won impressions divided by valid bids.
Seat diversity: Unique buyer seats per day or per hour; lower diversity can indicate demand concentration risk.

Timing and performance

Timeout rate: Percent of bids arriving after the auction deadline; the earliest warning sign in client-side header bidding.
TTFB distribution: Round-trip latency for bidder endpoints; can be segmented by geography and device.
Wrapper load time: Time to initialize the auction framework; rising times dent time-in-auction.

Floors and pricing

Floor collision rate: Share of bids below floor; good for spotting floor misalignment after changes.
Floor elasticity: Correlation between floor changes and bid density or win rate; helps calibrate floor strategy.
Price truncation artifacts: Odd spikes at floor boundaries that hint at suboptimal rounding or floor bucket design.

Transparency and trust

ads.txt / app-ads.txt compliance: Missing or stale authorized sellers depress buyer confidence :cite[ekx,aqj].
sellers.json publication and accuracy: Public, accurate seller disclosure fosters buyer trust and pass-through :cite[a41,ajm].
SupplyChain (schain) completeness: Full, correct chains reduce throttling of indirect paths :cite[bpp].

Identity and addressability

Cookie or device ID availability: Panel by browser or OS to catch addressability shifts.
Alternative IDs coverage: Adoption of interoperable IDs where appropriate, observed at the bidstream.
Contextual match rates: If used, coverage of contextual signals buyers rely on when identity is scarce.

Quality and policy

IVT indicators: If available, aggregate signals or third party scores used by buyers to downgrade or exclude inventory.
Brand safety blocks: Categories or page-level signals that reduce eligibility for certain buyers.
Creative policy rejects: System-level feedback from exchanges indicating delivery failures due to creative compliance.

CTV and SSAI specifics

Pod fill rate and pacing: Holes in pod construction or under-delivery in specific positions.
Beacon integrity: Quartile and completion beacon rates; fragile in SSAI chains.
SSAI insertion error rate: HTTP error codes on manifest manipulation or origin issues.
Content and channel integrity: Accurate metadata for content, channel, and show-level information that buyers require.

You do not need all of these to start. A v1 often focuses on 10 to 15 metrics that you can reliably instrument today.

A reference scoring model

Start with a weighted, bounded, and explainable score. Normalize each metric to a 0 to 1 range with clear “healthy” bounds, then compute a weighted average. Keep weights transparent and tune them over time. Here is a reference design for a web header bidding surface:

Timeout rate: 20 percent weight
Bid density: 15 percent weight
Win rate: 15 percent weight
Floor collision rate: 10 percent weight
ads.txt coverage: 10 percent weight
sellers.json accuracy: 5 percent weight
Seat diversity: 10 percent weight
TTFB: 10 percent weight
Identity coverage: 5 percent weight

For app and CTV, swap in SDK integrity, SSAI error rate, and pod fill dynamics where appropriate.

Normalization examples

For metrics where higher is better, use a clamped linear scale between a minimum acceptable value and a target ideal. For metrics where lower is better, invert.

# Example: normalizing auction health metrics and computing a composite score
def clamp(x, lo, hi):
return max(lo, min(hi, x))
def normalize_higher_better(value, acceptable, target):
# 0 at acceptable threshold, 1 at target or above
if target <= acceptable:
return 0.0
return clamp((value - acceptable) / (target - acceptable), 0.0, 1.0)
def normalize_lower_better(value, target, unacceptable):
# 1 at target or below, 0 at unacceptable or above
if unacceptable <= target:
return 0.0
return clamp((unacceptable - value) / (unacceptable - target), 0.0, 1.0)
def auction_health_score(metrics):
# metrics keys: timeout_rate, bid_density, win_rate, floor_collision_rate,
# ads_txt_coverage, sellers_json_accuracy, seat_diversity, ttfb_ms, identity_coverage
weights = {
"timeout_rate": 0.20,
"bid_density": 0.15,
"win_rate": 0.15,
"floor_collision_rate": 0.10,
"ads_txt_coverage": 0.10,
"sellers_json_accuracy": 0.05,
"seat_diversity": 0.10,
"ttfb_ms": 0.10,
"identity_coverage": 0.05,
}
# Normalize each metric
score_components = {
"timeout_rate": normalize_lower_better(metrics["timeout_rate"], target=0.05, unacceptable=0.20),
"bid_density": normalize_higher_better(metrics["bid_density"], acceptable=1.5, target=3.0),
"win_rate": normalize_higher_better(metrics["win_rate"], acceptable=0.10, target=0.25),
"floor_collision_rate": normalize_lower_better(metrics["floor_collision_rate"], target=0.10, unacceptable=0.40),
"ads_txt_coverage": normalize_higher_better(metrics["ads_txt_coverage"], acceptable=0.95, target=1.00),
"sellers_json_accuracy": normalize_higher_better(metrics["sellers_json_accuracy"], acceptable=0.95, target=1.00),
"seat_diversity": normalize_higher_better(metrics["seat_diversity"], acceptable=10, target=30),
"ttfb_ms": normalize_lower_better(metrics["ttfb_ms"], target=300, unacceptable=800),
"identity_coverage": normalize_higher_better(metrics["identity_coverage"], acceptable=0.40, target=0.70),
}
composite = sum(score_components[k] * w for k, w in weights.items())
return round(composite * 100, 1), score_components

These bounds are starting points. You will tune acceptable and target thresholds based on your inventory profile and buyer mix. For CTV, for example, TTFB is often less relevant than beacon integrity and SSAI error rates.

Scoring bands and alerting

Define four bands to help operations:

90 to 100: Excellent. Maintain and monitor.
75 to 89: Healthy but watch. Investigate top negative contributors.
60 to 74: Degraded. Trigger playbooks and assign ownership.
Below 60: Incident. Escalate and coordinate across ad ops and engineering.

Alerts should include a ranked list of negative contributors and recommended actions mapped to your environment.

Instrumentation and data collection

You can compute Auction Health Scores from a combination of wrapper events, server logs, and transparency checks. The goal is to minimize overhead and reuse what you already have.

Web header bidding with Prebid

Prebid exposes a rich stream of browser-side events. Use pbjs.onEvent to capture auctionInit, auctionEnd, bidTimeout, bidRequested, bidResponse, and bidWon, then summarize them client-side or send to your analytics endpoint :cite[bfd,d3c,cr2].

<script>
(function() {
var queue = [];
function safePush(evt) {
try {
queue.push({
type: evt.eventType,
ts: Date.now(),
data: evt.args || {}
});
} catch (e) {}
}
pbjs.onEvent('auctionInit', function(args) { safePush({ eventType: 'auctionInit', args: args }); });
pbjs.onEvent('auctionEnd', function(args) { safePush({ eventType: 'auctionEnd', args: args }); });
pbjs.onEvent('bidRequested', function(args) { safePush({ eventType: 'bidRequested', args: args }); });
pbjs.onEvent('bidResponse', function(args) { safePush({ eventType: 'bidResponse', args: args }); });
pbjs.onEvent('bidTimeout', function(args) { safePush({ eventType: 'bidTimeout', args: args }); });
pbjs.onEvent('bidWon', function(args) { safePush({ eventType: 'bidWon', args: args }); });
// Flush to your endpoint periodically
setInterval(function() {
if (queue.length) {
navigator.sendBeacon('/auction-telemetry', JSON.stringify(queue));
queue = [];
}
}, 5000);
})();
</script>

On the server side, aggregate events into per-surface metrics at hourly or daily cadence.

Server-side header bidding and exchange logs

If you control an SSP or server-side adapter, server logs provide lower-variance measurements for bid rate, win rate, and latency. Correlate client-side timeouts with server-side response times to see where time is lost.

Transparency checks

Adopt scheduled validation of ads.txt, app-ads.txt, and sellers.json. The IAB Tech Lab provides specs and implementation guidance, and even an aggregator program for large-scale crawling :cite[ekx,aqj,a31]. Ensure that:

ads.txt and app-ads.txt list the right exchanges and reseller records, updated as partners change :cite[ekx,aqj].
sellers.json is published and accurate for each advertising system in your supply chain :cite[a41,ajm].
SupplyChain objects are complete and correct in OpenRTB bid requests :cite[bpp,dr7].

App and SDK telemetry

Collect app-level metrics by SDK version and device class:

Version penetration: Distribution of SDK versions across active sessions.
Crash and error rates: Auction-impacting errors, like failing to call the auction due to initialization issues.
Network characteristics: Timeout correlation with connection type and geography.

CTV and SSAI observability

For CTV, build or integrate SSAI observability:

Pod construction logs: Which position failed to fill and why.
Beacon validation: Per DSP or partner, to spot breaks in measurement chains.
Manifest error telemetry: Origin or DRM-related errors that cause unseen drops.

Data model and storage

A practical schema helps you compute scores consistently and explain deviations.

{
"entity_id": "site:example.com:adunit:top_728x90",
"entity_type": "web_adunit",
"date": "2025-09-28",
"hour": 14,
"metrics": {
"requests": 128945,
"bids": 264321,
"valid_bids": 221900,
"wins": 43521,
"timeouts": 18750,
"ttfb_ms_p50": 320,
"ttfb_ms_p95": 820,
"floor_hits": 71400,
"ads_txt_coverage": 0.99,
"sellers_json_accuracy": 0.98,
"seat_diversity": 42,
"identity_coverage": 0.63
},
"score": {
"value": 84.6,
"components": {
"timeout_rate": 0.79,
"bid_density": 0.88,
"win_rate": 0.81,
"floor_collision_rate": 0.72,
"ads_txt_coverage": 0.98,
"sellers_json_accuracy": 0.96,
"seat_diversity": 0.77,
"ttfb_ms": 0.68,
"identity_coverage": 0.76
},
"band": "healthy"
},
"top_drivers": [
{ "metric": "ttfb_ms", "impact": -0.08, "note": "p95 latency rose after release r2025.09.27" },
{ "metric": "floor_collision_rate", "impact": -0.05, "note": "floor increased from $0.70 to $1.10" }
],
"recommendations": [
"Reduce bidders in low-performing geo segments to recover timeout budget",
"Test floor rollback by 10 percent in midday hours"
]
}

Keep metrics dense but not noisy. For each entity and time bucket, store raw counts and precomputed rates to make scoring and visualization fast.

Computing and querying scores

Hourly updates are ideal for visibility without chasing noise. A basic SQL aggregation can produce most inputs for your score function.

-- Example: aggregate Prebid event logs into hourly metrics per ad unit
WITH events AS (
SELECT
ad_unit_code,
DATE(event_ts) AS dt,
EXTRACT(HOUR FROM event_ts) AS hr,
event_type,
bidder,
CASE WHEN event_type = 'bidResponse' THEN price ELSE NULL END AS price,
CASE WHEN event_type = 'bidResponse' THEN time_to_respond_ms ELSE NULL END AS ttfb_ms,
CASE WHEN event_type = 'bidResponse' AND price < floor_price THEN 1 ELSE 0 END AS is_floor_hit
FROM raw_prebid_events
WHERE event_ts >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 DAY)
)
SELECT
ad_unit_code,
dt,
hr,
COUNTIF(event_type = 'auctionInit') AS requests,
COUNTIF(event_type = 'bidResponse') AS bids,
COUNTIF(event_type = 'bidResponse' AND price IS NOT NULL) AS valid_bids,
COUNTIF(event_type = 'bidWon') AS wins,
COUNTIF(event_type = 'bidTimeout') AS timeouts,
APPROX_QUANTILES(ttfb_ms, 100)[SAFE_OFFSET(50)] AS ttfb_ms_p50,
APPROX_QUANTILES(ttfb_ms, 100)[SAFE_OFFSET(95)] AS ttfb_ms_p95,
SUM(is_floor_hit) AS floor_hits,
COUNT(DISTINCT bidder) AS seat_diversity
FROM events
GROUP BY ad_unit_code, dt, hr;

Join these aggregates with daily transparency validation results for ads.txt and sellers.json, and with identity coverage snapshots by browser or OS.

From score to action: remediation playbooks

A score is only valuable if it tells you what to do next. For each metric, encode a set of data-driven recommendations and rank them by expected impact and effort.

Timeout rate spikes: Reduce bidder count for slow bidders, increase auction timeout where safe, lazy-load nonessential scripts, and route traffic to lower-latency endpoints. Validate wrapper version and compare before-after :cite[bfd,cr2].
Bid density drops: Inspect partner-level response volume. Look for recent floor changes or targeting rule edits. Check sellers.json and ads.txt for partner path breaks :cite[ekx,a41].
Floor collision surges: Roll back aggressive floor changes, or split floors by device, geo, and time-of-day. Validate bucket granularity to avoid bunching near thresholds.
Win rate declines: Segment by bidder seat and creative category. Lifecycle changes in buyers or budget can be masked without segmentation.
Transparency compliance issues: Refresh ads.txt/app-ads.txt, correct reseller entries, and verify sellers.json. Use Tech Lab implementation guidance to test at scale :cite[aqj,a31,ajm].
Identity coverage erosion: Track browser and OS shifts. Expand support for privacy-compliant identifiers where possible and enrich contextual signals when addressability declines.
CTV beacon and SSAI errors: Validate beacon integrity per partner, inspect pod policies, and coordinate with SSAI vendors on manifest manipulation error budgets.

You can codify these into alert templates and Jira tickets. The alert should include the score drop, top three drivers, and the recommended sequence of fixes.

Floors, elasticity, and OpenRTB 2.6 context

Price floors are a common culprit. A useful practice is to plot bid density and win rate against floor changes while considering OpenRTB dynamics. OpenRTB 2.6 introduced constructs for more granular floor signaling in video and audio, which you should leverage to avoid coarse floors that block legitimate demand :cite[dr7]. If your score degrades following floor changes, experiment with smaller adjustments and capture the elasticity curve by surface. Combine with buyer-specific feedback to refine floors per segment.

Bringing it to CTV

CTV is different enough that you should define a CTV-specific score variant. Suggested signal substitutions:

Pod fill rate in place of win rate for mid-roll and long-form content.
SSAI insertion error rate in place of client-side timeouts.
Beacon integrity score to reflect measurement delivery health.
SupplyChain completeness and sellers.json validation remain critical for trust :cite[bpp,ajm].

CTV demand is more sensitive to metadata quality. Ensure content-level metadata is accurate and consistently passed in the bidstream.

Packaging Auction Health Scores inside a seller tech stack

Where does this live? The answer depends on your role.

SSPs: Integrate Auction Health as a native diagnostic view for publisher success and TAM teams. Pair score drops with playbooks and partner-level root cause analysis.
Publishers: Place the score next to yield and revenue reporting. Scores trigger daily triage and escalation with your ops and engineering teams.
Intermediaries and networks: Use per-path scoring to monitor upstream and downstream partners and prevent brittle hops in your chains.

For Red Volcano customers, Auction Health Scores nest tightly with our core products:

Magma Web: Add a Health tab that displays historical scores, drivers, and actions per domain and ad unit.
Technology stack tracking: Detect tech changes that often correlate with score dips.
ads.txt / sellers.json monitoring: Feed compliance signals directly into the score and alerting tier :cite[ekx,a41].
Mobile SDK intelligence: Track SDK fragmentation and error spikes that pull scores down in app.
CTV data platform: Compute pod and SSAI-specific scores and notify when beacon integrity falls.

Alerting and operations

Scores drive action. Alerts should be noise-resistant and context-aware.

# Example alert policy for Auction Health Scores
policies:
- name: "Adunit health degradation"
entity_type: web_adunit
condition:
any:
- drop_pct: { window: "24h", threshold: 15 }  # score drops 15 percent vs 24h mean
- absolute_below: { threshold: 70, duration: "3h" }
min_volume:
requests_per_hour: 5000
notify:
- channel: "slack"
room: "#adops-alerts"
- channel: "pagerduty"
service: "yield-ops"
remediation:
runbook:
- "Check top negative contributors in dashboard"
- "If timeout rate > 12 percent, reduce slow bidders by 2 for this adunit"
- "If floor collision rate > 35 percent, roll back floor by 10 percent and observe for 2 hours"

Track the alert’s time-to-ack and time-to-mitigate. Over time, improve your playbooks with measured impact.

Governance and privacy by design

Auction Health Scores do not require personally identifiable information. Work with aggregated, operational metrics that describe the performance of your auctions, not your users. Ensure:

Data minimization: Only collect events necessary for health computation.
Aggregation: Store hourly or daily aggregates for most use cases and control access to raw logs.
Retention policy: Keep raw event data for the shortest period required to compute baselines and investigate incidents.
Compliance alignment: Follow applicable consent and privacy frameworks even for operational telemetry when it touches identifiers.

Transparency standards also promote a healthier market. Maintaining accurate ads.txt/app-ads.txt, sellers.json, and SupplyChain reduces fraud vectors and stabilizes demand trust :cite[ekx,aqj,a41,ajm,bpp].

Measuring the ROI of Auction Health

It is essential to treat Auction Health like a product, not a vanity metric. Prove ROI with clear outcomes:

Time-to-detection: Hours between issue onset and alert vs baseline reporting workflows.
Time-to-mitigation: Hours between alert and recovery to healthy score band.
Revenue at risk saved: Difference between projected loss without intervention and actual loss with intervention.
Stability: Reduction in variance of daily yield for monitored surfaces.
Trust: Fewer buyer feedback escalations related to transparency or supply chain issues.

Tie these metrics to quarterly objectives for your ad operations and publisher success teams.

Getting started in 30 days

You do not need a multi-quarter program to ship value. Here is a pragmatic roadmap.

Days 1 to 10: Instrument and baseline

Choose entities: Start with top 10 web ad units or top CTV channels by revenue.
Capture events: Implement Prebid event collection and server log exports where available :cite[bfd].
Run transparency checks: Crawl ads.txt/app-ads.txt and sellers.json for your properties and key partners :cite[ekx,aqj,a41].
Define v1 metrics: Timeout rate, bid density, win rate, floor collision rate, and transparency coverage.

Days 11 to 20: Score and alert

Implement scoring: Use the reference Python function and store results daily.
Add bands and alerts: Slack alerts for drops and absolute thresholds.
Build a simple UI: List entities, historical scores, top drivers, and recommended actions.

Days 21 to 30: Operationalize

Playbooks: Document standard remediations mapped to each metric.
Weekly review: Review score trends and alert quality, tune thresholds.
Expand coverage: Add seat-level breakouts and identity coverage panels. For CTV, add beacon integrity.

Advanced topics and extensions

Once the basics work, consider these enhancements.

Forecasting: Train a simple time series model on health metrics to forecast tomorrow’s score and highlight likely regressions.
Anomaly explanations: Use Shapley-like attributions on normalized metrics to rank contributors to score change.
Experiment design: Run controlled floor tests and bidder configuration changes tied to score stability, not only revenue outcomes.
Partner scorecards: Share partner-facing views of health to collaboratively reduce timeouts or misconfigurations.
API access: Expose scores via API so internal tools and alerting systems can consume them.

A simple REST endpoint contract works well:

GET /v1/auction-health?entity_type=web_adunit&entity_id=site:example.com:adunit:top_728x90&start=2025-09-01&end=2025-09-30
200 OK
Content-Type: application/json

{
"entity_id": "site:example.com:adunit:top_728x90",
"scores": [
{"date": "2025-09-01", "value": 86.5, "band": "healthy"},
{"date": "2025-09-02", "value": 82.1, "band": "healthy"},
{"date": "2025-09-03", "value": 71.9, "band": "degraded", "drivers": ["timeout_rate", "ttfb_ms"]}
]
}

Common pitfalls to avoid

Even good scoring systems fail if they ignore operational realities.

Too many metrics too soon: Start with the small set that moves the needle and is objectively measurable.
Opaque weights: Keep the model explainable so ops teams trust and use it.
Chasing noise: Use volume guards and require sustained deviations to alert.
One-size-fits-all thresholds: Calibrate by entity, buyer mix, and geography.
Ignoring transparency hygiene: Broken ads.txt or sellers.json creates quiet demand throttling that score alone will not cure :cite[ekx,a41].

Conclusion

Healthy auctions create healthy revenue. Waiting for revenue reports to surface issues is a slow and expensive habit. Auction Health Scores flip that script by capturing the operational signals that move yield before the budget is gone. The framework above is simple enough to ship in a month and strong enough to anchor your seller-side observability strategy for years. Start with timeouts, bid density, win rate, floors, and transparency. Wire alerts with actions. Tune thresholds as your inventory and buyer mix evolve. Expand to app and CTV signals as your footprint demands. At Red Volcano, we believe that transparency and data discipline win over silver bullets. Auction Health Scores are not a fad metric. They are a practical, explainable, and privacy-respecting way to keep your auctions on track, your teams focused, and your revenue stable. If you want a jumpstart, our team can help wire up the telemetry, compute the scores, and operationalize the playbooks across web, app, and CTV.

References

IAB Tech Lab — ads.txt: Authorized Digital Sellers and implementation guidance :cite[ekx,aqj]
IAB Tech Lab — sellers.json: Seller transparency specification and FAQ :cite[a41,ajm,b8n]
IAB Tech Lab — SupplyChain object: Supply chain transparency in OpenRTB :cite[bpp,ekh]
IAB Tech Lab — OpenRTB: OpenRTB updates relevant to pricing and floors :cite[dr7]
Prebid.org: Publisher API events and troubleshooting :cite[bfd,d3c,cr2]

Citations: :cite[ekx] — https://iabtechlab.com/ads-txt/ — accessed Sep 29, 2025 :cite[aqj] — https://iabtechlab.com/wp-content/uploads/2022/04/Ads.txt-1.1-Implementation-Guide.pdf — accessed Sep 29, 2025 :cite[a41] — https://iabtechlab.com/sellers-json/ — accessed Sep 29, 2025 :cite[ajm] — https://iabtechlab.com/wp-content/uploads/2019/07/Sellers.json_Final.pdf — accessed Sep 29, 2025 :cite[b8n] — https://iabtechlab.com/wp-content/uploads/2019/07/FAQ-for-sellers.json_supplychain-object.pdf — accessed Sep 29, 2025 :cite[bpp] — https://iabtechlab.com/wp-content/uploads/2019/07/Sellers.json_Final.pdf (SupplyChain references) — accessed Sep 29, 2025 :cite[ekh] — https://iabtechlab.com/sellers-json-and-supplychain-object-ready-for-industry-adoption/ — accessed Sep 29, 2025 :cite[dr7] — https://iabtechlab.com/standards/openrtb/ — accessed Sep 29, 2025 :cite[bfd] — https://docs.prebid.org/dev-docs/publisher-api-reference/onEvent.html — accessed Sep 29, 2025 :cite[d3c] — https://docs.prebid.org/dev-docs/publisher-api-reference/getEvents.html — accessed Sep 29, 2025 :cite[cr2] — https://docs.prebid.org/troubleshooting/troubleshooting-guide.html — accessed Sep 29, 2025