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Most campaign retrospectives end with a list of learnings.
The problem is that those learnings often stay in the retrospective.
Three months later, someone starts a new brief, pulls up the usual audience profile and messaging, and makes the same assumptions all over again.
Instead, use ChatGPT to turn campaign results into useful context for whatever you do next.
Give it your campaign results alongside the original brief and other inputs you used. Ask it to identify what you learned and, more importantly, what should change next time.
Maybe customer feedback exposed an objection worth addressing. Maybe one message consistently confused people. Maybe a creative angle performed well enough to test again.
Not every result should become a new rule. One campaign rarely proves that something always works. The goal is to capture what the evidence supports, what is worth testing again, and what the next team needs to know.
Try this prompt:
You are a senior marketing strategist helping me carry campaign learning into future work.
Review:
- Campaign results and retrospective: [attach]
- Original campaign brief: [attach]
- Relevant audience, messaging, and brand references: [attach]
Identify the most useful lessons from the campaign.
For each lesson:
1. Show the evidence behind it
2. Label it as a supported finding or a hypothesis
3. Explain what it could change in our next campaign
4. Draft the specific update you would make to the next brief or relevant reference
Don't turn a single campaign result into a universal rule. Flag anything that needs more evidence before we adopt it more broadly.
Finish with a short "What we learned" section that can be saved with the campaign and used as context for future work.
Then actually use it.
Save the approved learnings with the campaign. When you brief your next campaign, attach them alongside your usual audience, brand, and performance context and tell ChatGPT to use them when building the new brief.
That way, each campaign starts with what you've already learned, not from scratch.
Happy prompting!
Webinar Series: Running Agents in Customer Work
Every AI vendor reports a resolution rate. No two calculate it the same way, and none of them tell you whether the replies were actually good.
Running Agents in Customer Work is four 30-minute sessions for the leaders who have to make agentic AI work in support, success, and ops. Session 4 gets into how to score quality at volume without reading every transcript, and the questions to ask a vendor about what their resolution math leaves out.
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November 12th & 13th