Write a post-sale recap with lessons for next year
A results recap of your seasonal sale, what drove revenue and margin, and a ready playbook for next year.
The prompt
Write a recap of my seasonal sale and the lessons for next time. This is step 5 of a seasonal campaign pack; it works on its own, but paste your original plan and goal from step 1 if you have them.
Campaign and dates: [DETAILS]
Goal: [TARGET]
Results: [revenue, orders, average order value, discount cost, gross margin, new vs. returning customers, by day if possible]
Channel results: [email, SMS, social, ads: sends/spend, clicks, conversions, revenue]
Last year's comparable numbers: [OPTIONAL]
Operational issues: [stockouts, shipping delays, site problems, support volume]
Customer feedback: [NOTES]
Analyze:
1. Result vs. goal, and whether revenue came with acceptable margin after discounts.
2. New-customer acquisition vs. pulling forward sales from people who would have bought anyway.
3. Which phases, offers, products, and channels did the work. Note where attribution overlaps (e.g. email and ads claiming the same sale).
4. Operational problems and their cost.
5. What customers said.
Return:
- A one-page recap for my team or manager: headline numbers, what worked, what didn't, and decisions.
- A table: Channel | Spend | Revenue | Margin after discount | Verdict.
- A "next year playbook": dates to start prep, offer to repeat or change, list-building to do before the season, and 5 things to fix.
- A 30-day follow-up plan for new customers acquired in the sale, so they buy again at full price.
Use only my numbers; label any estimate clearly.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Marketing & Brand prompts
What to fill in
- DETAILS
- TARGET
- OPTIONAL
- stockouts, shipping delays, site problems, support volume
- NOTES
How this prompt is built
This prompt follows the RTF structure (Role, Task, Format). RTF tells the model who to be, what to do and what shape the answer should take. It is the quickest structure that still gives noticeably better results than a bare request.
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