The AdTech auction, from request to impression ↗
Trace the handoffs between publisher, exchange, DSP, ad server, and measurement.
Practical guide · updated October 2026Learn / Investigate / Verify
The AdTech field guide.
Practical explanations and repeatable checks for publishers, buyers, exchanges, DSPs, product teams, and QA engineers.
16 guides
Follow the system from request to evidence.
A systematic checklist for missing header bids ↗
Find where a header-bidding integration stops before changing configuration.
Practical guide · updated October 2026Header bidding explained ↗
Understand the browser auction, bidder requests, timeouts, targeting, and rendering.
Practical guide · updated October 2026Prebid.js vs Prebid Server ↗
Compare browser-side and server-side architectures by evidence and operational trade-offs.
Practical guide · updated October 2026OpenRTB request and bid response ↗
Validate protocol fields, business eligibility, no-bid outcomes, and regression fixtures.
Practical guide · updated October 2026OpenRTB testing checklist ↗
Turn the protocol into positive, negative, timeout, performance, and reconciliation cases.
Practical guide · updated October 2026ads.txt: authorization is different from validation ↗
Understand seller records and what a local file check can actually establish.
Practical guide · updated October 2026ads.txt explained ↗
Validate syntax, authorization records, seller IDs, and deployed files.
Practical guide · updated October 2026sellers.json and SupplyChain ↗
Trace seller identity and supply-path consistency across systems.
Practical guide · updated October 2026Publisher floor prices ↗
Design controlled tests for floors, currencies, bids, wins, and filtering.
Practical guide · updated October 2026VAST testing guide ↗
Validate tags, XML, media files, tracking events, redirects, and player failures.
Practical guide · updated October 2026Investigating impression discrepancies ↗
Normalize scope and definitions before comparing reporting systems.
Practical guide · updated October 2026How to test impression discrepancies ↗
Use identifiers and controlled comparisons to classify reporting gaps.
Practical guide · updated October 2026A DSP campaign checklist ↗
Translate targeting, budgets, and creative rules into observable acceptance tests.
Practical guide · updated October 2026AdTech QA checklist ↗
Cover functional, API, integration, negative, reporting, and performance tests.
Practical guide · updated October 2026Website AdSense readiness checklist ↗
Prepare content, trust, navigation, and technical fundamentals before applying.
Practical guide · updated October 2026How to use these guides
Start at the earliest observable failure.
Choose one placement or transaction, record the expected behavior, inspect each handoff, and change one variable at a time. The goal is a reproducible conclusion, not a collection of guesses.
Need a checklist?
Use the QA guide.
Turn the concepts here into functional, API, integration, regression, and reconciliation coverage.
Open the QA checklist →Test lab scenarios
Reproducible examples without fake production results.
Prebid no-bid lab scenario
Find the first broken stage in a controlled auction.
Floor-price lab scenario
Isolate the effect of a bid floor.
Discrepancy lab scenario
Classify a reporting gap before changing tracking.
OpenRTB no-bid scenario
Separate protocol defects from business no-bids.
VAST playback scenario
Trace a video failure from tag retrieval to tracking.
AdTech Engineering Research & Labs
Ten original engineering investigations with synthetic case studies, evidence models and reproducible debugging methods. These are educational methods, not reports of production incidents.
- The Invisible Revenue Leak: Requests That Never Reach Auction — Causal measurement for monetization changes
- Publisher Revenue Forensics: A Reconciliation Workbook — Causal measurement for monetization changes
- AdTech Failure Injection: Safe Experiments for Auction Infrastructure — Causal measurement for monetization changes
- A Supply Path Evidence Graph for Ads.txt, Sellers.json and Schain — Causal measurement for monetization changes
- The Hidden Cost of Auction Latency: An Experimental Model — Causal measurement for monetization changes
- Designing an AdTech Incident Replay System — Causal measurement for monetization changes
- A Bidstream Data Quality Observatory — Causal measurement for monetization changes
- Privacy-Safe AdTech Debugging Without Raw User Identifiers — Causal measurement for monetization changes
- Detecting Silent Configuration Drift in Ad Servers — Causal measurement for monetization changes
- The AdTech Experiment Design Handbook — Causal measurement for monetization changes