Promotion campaign review

Promotion Campaign Review

Produce a Promotion Campaign Contract that grows incremental retained value without fake urgency, loyal-customer punishment, review manipulation, spam, or unreconciled rewards.

Atomic boundary

Own one campaign/offer's objective, audience, eligibility, placement, message, economics, applicable benefit fulfillment/reversal, channel handoffs, fraud, support, experiment, and shutdown. Do not own the whole marketing system, payment provider ledger, referral program, store listing, or game/app design.

Use a draft artifact ID. Consume payment, refund, economy, referral, notification, analytics, marketing, and product decisions by owner and explicit contract. Let deterministic delivery tooling seal versions/digests later; never invent them during design.

Agent-first invariant

Construct every applicable campaign state, eligibility policy, channel asset, provider authority, experiment, fraud/support path, telemetry, cap, expiry, kill switch, and reconciliation before activation. Build ledger/grant/reversal adapters only for a discount, reward, credit, entitlement, or other fulfilled benefit; informational/lifecycle campaigns record that mode as non-applicable. Low volume or unknown ROI never justifies manual fulfillment or later hardening. Separate construction, exposure, qualification, spend, and any grant authority; dormant campaigns perform zero sends, SDK work, grants, or data collection.

Workflow

1. Define exact objective and incremental-value hypothesis, audience/cohorts, platforms/territories/age modes, baseline, budget/reward/margin caps, time horizon, owner, and ruin boundaries.
2. Read references/promotion-campaign-patterns.md. Select campaign/benefit mode (informational, discount, reward, credit, entitlement, or another explicit type), state the normal alternative, and mark fulfillment/reversal branches applicable or not applicable. Model loyal/active, new, lapsed, payer/non-payer, ownership, refund/dispute, consent, region, platform, and abuse states.
3. Define deterministic view and exposure eligibility, plus redeem/qualify/grant eligibility only when the selected benefit requires it, with reason codes, user+offer cooldowns, conflict/stacking, caps, expiry, ownership overlap, baseline path, and support explanation.
4. Place only at a value/intent transition. Specify cross-channel dedupe, dismissal/suppression, quiet hours, preference/consent, privacy/redaction, deep link, and notification/store/referral handoffs.
5. Write transparent benefit, condition, duration, renewal/normal state, limits, expiry, reversal, and support route. Localize meaning, not just strings.
6. Model exposure and, when applicable, authoritative fulfillment separately, including no-op, failure, duplicate/out-of-order, refund/chargeback, fraud, rollback, compensation, restore, migration, support correction, and live readback.
7. Compute incremental retained contribution after discount/reward, fees, cannibalization, refunds/chargebacks, fraud, fatigue, support, economy, and loyal-customer effects. Define randomized/causal cells where feasible.
8. Set exact scale/hold/pause/withdraw predicates, provider-side end times, upstream caps, emergency shutdown, reconciliation-only withdrawal state, and post-campaign closeout.

Source verification

Retrieve current store/payment/promotion, advertising, notification, consent, child/age, referral, review/rating, pricing, tax, territory, and channel policy for the exact campaign. Explicit prohibitions and missing authority are hard floors. Genuinely ambiguous but admissible behavior may use the bounded risk contract below; known policy violations, deception, consent bypass, child harm, or unlawful behavior are not “grey experiments.”

Bounded risk-reward contract

Use arithmetic to compare admissible options without pretending fat-tail or irreversible harm is an ordinary average cost:

expected_net_value
= P(success) * incremental_retained_contribution
- sum(P(loss_i) * direct_and_secondary_loss_i)
- trust_and_reputation_cost
- recovery_and_opportunity_cost

constraints:
maximum_loss <= declared_ruin_budget
CVaR_at_selected_confidence <= declared_risk_budget
exposure <= blast_radius_cap
time_to_detect + time_to_contain <= recovery_window

Record probability ranges and sensitivity rather than false precision. Include platform/account action, customer remedy, refunds, support, reputation, cannibalization, reversibility, detection lag, rollback limits, and opportunity cost. Unknown material facts widen the loss range; they do not become zero.

An experiment may proceed only when the behavior is not explicitly prohibited, the customer proposition is truthful, consent and remedies remain intact, exposure/spend are capped, stop signals are observable, and recovery has been tested. A small company’s ability to correct quickly lowers some recovery cost; it does not erase platform-account, legal, child-safety, privacy, or trust ruin.

When not to use

• Use marketing-automation-blueprint for the full organic/lifecycle/paid, creative, attribution, spend, reputation, and shutdown operating system.
• Use referral-loop-review for persistent inviter/invitee qualification, attribution, pending grants, reversals, fraud, and support.
• Use store-listing-optimization when the artifact is one channel's metadata, screenshot/video/capsule sequence, proof, localization, and conversion test.
• Use daily-reward-and-streak-review for a durable recurring return-loop state machine, or game-economy-review for economy balance and inflation.

Guardrails

• No fake scarcity/countdown, hidden renewal/normal price, shame, blocked exit, reset timer, or loss threat involving creations/history/identity/access.
• Never reward review/rating, permission, ad click, spend, forced referral, or mere update installation. Update benefit requires verified new-value use.
• A user cannot see an offer they cannot redeem; regional/platform differences and suppression are support-explainable.
• Never confirm reward before authority and idempotent grant commit. Ordinary refund/reversal cannot delete unrelated value or auto-ban an account.
• Optimize neither gross revenue nor clicks alone. Retention, renewal, margin, refunds, churn, loyal cohort, fatigue, support, fairness, economy, privacy, and fraud false positives are mandatory countermetrics.
• Autonomous optimizers cannot alter eligibility floors, prices/terms, consent, reward value, grant/reversal, spend caps, or their own promotion gates.

Output contract

Return one typed Promotion Campaign Contract with:

1. objective, audience/cohort, baseline, exact offer, economics, authority, budgets/caps, horizon, assumptions, and ruin boundaries;
2. view/exposure and applicable redeem/qualify/grant eligibility, reason, conflict/stacking, user+offer cooldown, ownership, expiry, and support matrix;
3. placement/message/localization, channel dedupe/suppression, consent, preference, privacy, and deep-link contract;
4. exposure state machine plus applicable fulfillment/reversal/compensation state machines and ledger, or an explicit non-benefit disposition;
5. applicable fraud, refund/dispute, restore/migration, support, and reconciliation rules;
6. event schema, causal experiment, economics and trust countermetrics;
7. expected-value/CVaR record where uncertainty is material, plus scale/hold/pause/withdraw, provider end/cap, kill switch, live readback, and closeout evidence;
8. specialist handoffs with draft IDs, owners, required inputs/outputs, acceptance questions, and no fabricated proof.

Complete only when every eligible/ineligible impression, applicable qualification/grant/reversal, and withdrawal is explainable, bounded, replayable where applicable, and support-safe.