Ad monetization review

Ad Monetization Review

Produce an Ad Monetization Contract that maximizes incremental retained contribution without turning attention, privacy, gameplay, or core utility into an interruption tax.

Atomic boundary

Own ad-side placement semantics, eligible audiences, reward authority, frequency/fatigue, provider/mediation portfolio, economics, experiments, and shutdown. Own the ad domain's required measurement meanings, but let product-analytics-instrumentation-review own the shared event identity, schema/versioning, collection, and product-metric contract. Do not own whole-app/game design, buyer payment ledgers, the broader game economy, one marketing campaign, or provider SDK implementation details.

Use a draft artifact ID and consume sibling decisions by owner and explicit contract. Let deterministic delivery tooling seal serialized versions and digests later; never fabricate them in a design response.

Agent-first invariant

Construct the complete production-shaped system now: provider adapters, consent/age/territory modes, reward ledger, caps, payer states, mediation, fraud, observability, experiments, kill switches, and reconciliation. Separate construction from exposure. A disabled placement or provider performs zero SDK initialization, request, tracking, asset download, background work, or public call until its exact authority and exposure gate pass.

Workflow

1. Label inputs given, observed, assumed, hypothesis, or decision.
2. Define product promise, audience/age modes, session loop, business model, IAP/subscription mix, platforms, minimum devices, consent regimes, and ruin boundaries.
3. Read references/ad-monetization-systems.md. Inventory every potential placement by user intent, natural break, frustration/safety state, format, expected latency, reward, cap, and degradation.
4. Segment new users, engaged non-payers, payers, subscribers/ad-free owners, lapsed users, child/unknown-age modes, high-frustration states, and accessibility/data-saver/low-end tiers. Define explainable eligibility and suppression reason codes.
5. Model reward flow as eligible -> requested -> loaded -> started -> completed -> verified -> granted with no-fill, timeout, abandonment, duplicate callback, provider dispute, rollback, and support correction branches.
6. Define global, format, placement, session, lifecycle-event, and user+campaign caps; cross-provider dedupe; cooldown; quiet/sensitive states; and an emergency kill switch independent of the client release.
7. Design mediation and provider adapters with consent-aware initialization, data maps, latency/no-fill budgets, revenue/impression reconciliation, provider leases, fraud controls, brand safety, and zero-cost dormant state.
8. Evaluate incremental retained contribution after provider/platform fees, IAP/subscription cannibalization, reward cost/inflation, churn, support, fraud, privacy risk, latency, battery, and low-end performance.
9. Specify candidate-specific tests, capped canaries, scale/hold/pause/withdraw predicates, automatic shutdown, and live readback.

Source verification

Retrieve current platform/store advertising, consent, child-directed treatment, rewarded-ad, offerwall, attribution, privacy, and provider SDK authority for the exact platform, territory, audience mode, and release. Static reference text is never a current compliance verdict. Unknown/stale authority disables exposure.

When not to use

• Use game-economy-review when the primary artifact is currency/reward inflation, sources/sinks, progression, gacha, or payer fairness.
• Use marketing-automation-blueprint for external paid acquisition, creative spend, attribution, and lifecycle channel automation.
• Use app-design-blueprint or game-design-blueprint when ads are only one component of a whole-product design.
• Use payment-platform-readiness for purchases, receipts, entitlements, settlement, refunds, and finance-close authority.

Guardrails

• Never place interstitials inside input/mastery, onboarding trust, payment, safety, accessibility, error recovery, or irreversible work.
• State the reward, duration/format expectation, qualification, cap, and failure behavior before start. Never falsely confirm an unverified reward.
• No targeted/personalized ads in child or unknown-age modes unless exact current authority permits; default to the safer mode.
• Do not punish consent refusal, create fake close buttons, force clicks, hide ad identity, or make ads the only practical path through core utility.
• Optimize neither impressions nor eCPM alone. Retention, IAP conversion, complaints, opt-out, latency, crashes, battery, fairness, support, and long-term value are mandatory countermetrics.
• An autonomous optimizer cannot change consent, child mode, reward value, placement class, payer suppression, spend/provider caps, or promotion gates.

Output contract

Return one typed Ad Monetization Contract containing:

1. objective, audience/platform/age modes, monetization mix, assumptions, and ruin boundaries;
2. placement × format × state matrix with value, interruption, latency, eligibility, cap, degradation, and owner;
3. reward/verification/reversal state machine and ledger/idempotency contract;
4. consent, privacy, age/territory, provider, payer/ad-free, and support matrix;
5. mediation/provider adapter, dormant-state, no-fill, timeout, and shutdown design;
6. economics model with IAP/IAA cannibalization and long-term countermetrics;
7. ad measurement requirements and the product-analytics handoff for event schema, plus reconciliation, fraud/brand-safety, and support evidence;
8. experiment/canary plan with scale, hold, pause, withdraw, and live-readback predicates;
9. specialist handoffs by draft ID, owner, required input/output, and acceptance questions, without fabricated proof.

Complete only when another agent can implement every state, prove reward authority and low-end behavior, explain every impression/suppression/grant, and shut the system down without a client release.