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Create an execution checklist for a promotion

Auteur: AILesson8 min de préparationTesté avec:ChatGPTRévisé: 2026-08-28

Réponse rapide

Turn approved promotion facts into an owned, timed checklist with cross-channel controls and stop conditions. Fournir: Approved promotion facts, Operational context, Measurement and control rules. Résultat attendu: A phase-based promotion checklist covering offer setup, content, inventory, service, measurement, rollback, and evidence.

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Create an executable promotion checklist using only the approved facts and operating rules below.

Promotion facts:
[promotion]

Operations:
[operations]

Measurement and controls:
[measurement]

Separate confirmed requirements, proposed tasks, and unresolved decisions. Organize the checklist into readiness, build, prelaunch QA, launch, live monitoring, close, reconciliation, and retrospective. For every task show timing or trigger, one accountable owner, action, dependency, expected evidence, and status field. Cover offer configuration, terms consistency, tracking, creative and link QA, accessibility, consent, stock or capacity, fulfillment, support briefing, finance reconciliation, data-quality checks, alerts, stop conditions, rollback, and customer correction. Do not invent discounts, eligibility, claims, owners, approvals, inventory, system behavior, attribution, or legal requirements. Flag anything that blocks safe launch. End with go/no-go criteria, an incident contact tree, and a compact launch-day checklist.
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De l'entrée au résultat

Un exemple détaillé

Voyez comment un contexte concret transforme cette recette en résultat utilisable.

Entrée réelle

Approved promotion facts
Weekend studio promotion approved for 5–7 September: 15% off the first four-session beginner pottery pack, online purchase only, new customers aged 18+, one pack per person. Code FIRSTCLAY. Excludes materials, gift cards, private classes, and existing bookings. The pack expires 60 days after purchase. Channels: website banner, email to opted-in local prospects, and two organic social posts. Approved claim: small-group beginner instruction; maximum eight learners per class. Paid-media budget is zero. Terms must show all exclusions and expiry.
Operational context
Owner Maya approves final pages and may stop the offer. Web lead Ren configures checkout and tracking. Instructor Jo confirms seats. Support lead Kim updates the reply guide. Finance lead Arun reconciles orders. There are 24 promotional packs available across scheduled classes; no waitlist and no overselling. Checkout can limit total redemptions but per-person enforcement must be tested. Refund policy is unchanged. Known failures: code applies to gift cards, capacity count lags cancellations, and scheduled email links can point to a draft page.
Measurement and control rules
Baseline is the prior four weekends: 180 landing-page sessions, 22 checkout starts, 8 pack purchases. Source systems: web analytics, checkout orders, class roster, and support log. Report sessions, checkout starts, eligible purchases, net revenue, remaining capacity, refunds, and issue count; do not claim channel causality because cross-device attribution is incomplete. Test one valid purchase and every exclusion before launch; preserve screenshots and order IDs. Alert Maya at 18 packs or any incorrect discount. Stop new purchases at 24 packs, overselling, misleading terms, or checkout charging the wrong amount. Ren disables the code and banner for rollback. Daily report at 18:00; reconcile within two business days.

Exemple de sortie

Promotion execution checklist — FIRSTCLAY

Launch blockers: per-person enforcement is unverified; capacity synchronization after cancellation is a known risk. Maya must accept a tested control before go-live.

Readiness

  • Jo | before build | Confirm the 24 sellable pack places against the class roster | Evidence: dated capacity export | Status: open.
  • Maya | before publication | Approve final offer terms, exclusions, 60-day expiry, and unchanged refund policy | Evidence: approval record | Depends on final copy.

Build and QA

  • Ren | before 5 September | Configure 15% code, new-customer eligibility, one-pack limit, 24-pack ceiling, start/end time, tracking, banner, and landing page | Evidence: configuration screenshots.
  • Ren with Arun | prelaunch | Run one valid transaction plus tests for gift card, materials, private class, existing booking, second purchase, wrong age, expiry display, and pack 25 | Evidence: test order IDs and expected/actual log. Any wrong discount is no-go.
  • Kim | prelaunch | Verify website, email, social, checkout, and support guide state the same exclusions and expiry | Evidence: signed content matrix.
  • Ren | prelaunch | Test live URLs, mobile layout, keyboard access, readable contrast, analytics events, and the scheduled email destination | Evidence: QA log.

Launch and monitor

  • Maya | launch gate | Go only if offer math, exclusions, limits, links, capacity, support guide, and rollback have passed; unresolved enforcement means no-go.
  • Ren | launch then ongoing | Publish approved assets and monitor checkout. Alert Maya at 18 packs or any bad charge; disable code and banner at 24 packs, overselling, misleading terms, or wrong amount.
  • Jo | after each sale/cancellation batch | Reconcile orders with roster capacity | Evidence: timestamped count.
  • Kim | continuously | Log customer issues without promising exceptions; escalate incorrect offer display or charge immediately.

Close and reconcile Ren disables code and removes the banner after 7 September; exact timezone remains to be confirmed. Arun reconciles eligible orders, gross discount, refunds, net revenue, and roster count within two business days. Daily 18:00 report shows sessions, checkout starts, eligible purchases, remaining capacity, refunds, issues, data gaps, and comparison with the stated baseline; it labels channel attribution as incomplete.

Incident tree: Ren detects/configures rollback → Maya decides stop/restart → Kim handles customer correction → Arun verifies charges → Jo protects capacity. Launch-day compact check: approved terms; valid/exclusion tests; capacity 24; live URLs; consent list; support brief; tracking; alert at 18; stop at 24; rollback ready.

Pourquoi cela fonctionne

  1. 1

    Phase gates expose missing approvals and dependencies before customers see the offer

  2. 2

    Evidence and rollback fields make execution observable and recoverable

Vérifier le résultat

  • Does every launch-critical task have one owner, a trigger, evidence, and a dependency state?

  • Are the offer, terms, channels, stock, support guidance, and tracking mutually consistent?

  • Can the team identify when to stop, who decides, and how to roll back or correct customers?

Utilisez-la en toute confiance

Questions fréquentes

Des réponses pratiques sur le bon moment pour utiliser cette recette, ce qu’il faut fournir et les cas où une vérification humaine reste nécessaire.

What should I prepare before using “Create an execution checklist for a promotion”?

For “Create an execution checklist for a promotion,” prepare Approved promotion facts, Operational context, and Measurement and control rules. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Create an execution checklist for a promotion” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A phase-based promotion checklist covering offer setup, content, inventory, service, measurement, rollback, and evidence—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Create an execution checklist for a promotion”?

The published test record for “Create an execution checklist for a promotion” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

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