https://github.com/thaolst/meu-planning-toolkit
MEU Planning Toolkit - backward planning from engagement targets to campaign calendar. Prompts, calculator, and lifecycle treatment framework.
https://github.com/thaolst/meu-planning-toolkit
ai-prompts campaign-planning engagement fintech growth-marketing lifecycle-marketing meu sea
Last synced: 2 days ago
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MEU Planning Toolkit - backward planning from engagement targets to campaign calendar. Prompts, calculator, and lifecycle treatment framework.
- Host: GitHub
- URL: https://github.com/thaolst/meu-planning-toolkit
- Owner: thaolst
- Created: 2026-06-15T13:51:41.000Z (17 days ago)
- Default Branch: main
- Last Pushed: 2026-06-15T14:04:39.000Z (17 days ago)
- Last Synced: 2026-06-15T15:26:18.558Z (17 days ago)
- Topics: ai-prompts, campaign-planning, engagement, fintech, growth-marketing, lifecycle-marketing, meu, sea
- Language: Python
- Size: 30.3 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MEU Planning Toolkit
Backward planning from Monthly Engaging User (MEU) targets to campaign calendar - with prompts, lifecycle treatment framework, and MEU calculator.
Built from real fintech growth campaigns in SEA.
## Why backward planning
Most campaign planning starts from a brief or a budget. This repo starts from the MEU gap:
> "How many more MEU do I need to hit target? Which campaigns will contribute the most?"
That question drives everything else - event mix, lifecycle treatment, channel selection, budget split.
## What's inside
| Folder | What it does |
|---|---|
| 01-framework | MEU backward planning logic + event tiering (Mega/Big/Medium/Small) |
| 02-prompts | 4 prompt workstreams, each with bootstrap + resumption files |
| 03-tools | meu_calculator.py - input target, output campaign ladder |
| 04-templates | Annual calendar template, blank and reusable |
## How to use
**Step 1 - Gap analysis first**
Open `02-prompts/01-meu-gap-analysis/bootstrap.md`. Input your current MEU base and target. The prompt outputs your gap by lifecycle stage.
**Step 2 - Pick your event mix**
Use `02-prompts/02-event-mix-planner/bootstrap.md` to translate the gap into a campaign calendar (seasonal spikes + repeated mechanics + always-on treatment).
**Step 3 - Lifecycle treatment**
`02-prompts/03-lifecycle-treatment/bootstrap.md` assigns campaign mechanics to each lifecycle stage: Acquisition, Activation, Retention, Resurrection.
**Step 4 - Run the calculator**
`03-tools/meu_calculator.py` - input your numbers, get a prioritized campaign ladder with estimated MEU contribution per activity.
## Stack
- Prompts: Claude (tested on Sonnet 4.6)
- Calculator: Python 3.10+
- No external dependencies for prompts
## Who this is for
Growth marketers in fintech, super-apps, or any platform where MEU/MAU is the north star metric. Works best if you have historical campaign data to calibrate the calculator.
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