campaign-review

Solid

Post-mortem on recent campaign

AI & Automation 448 stars 122 forks Updated today NOASSERTION

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Quality Score: 83/100

Stars 20%
88
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
75
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

## Purpose Capture learnings from completed campaigns so you compound marketing knowledge and make better decisions next time. ## Usage - `/campaign-review [campaign-name]` - Review specific campaign --- ## Steps 1. **Gather campaign context:** - Search 05-Areas/Content/ for campaign files - Search 00-Inbox/Meetings/ for campaign discussions - Find campaign brief/plan 2. **Prompt user for:** - Campaign goals (awareness, leads, pipeline, etc.) - Target audience - Channels used - Budget spent - Timeline 3. **Collect results:** - Ask for metrics: impressions, clicks, leads, pipeline, revenue - Qualitative feedback received - Unexpected outcomes 4. **Document learnings:** - What worked well? - What didn't work? - What would you do differently? - Surprises or insights? 5. **Create post-mortem document** in 06-Resources/Learnings/Campaign_[Name]_[Date].md --- ## Output Format ```markdown # Campaign Post-Mortem: [Campaign Name] **Date:** [Today] **Campaign period:** [Start] - [End] ## Overview - **Goal:** [Primary objective] - **Audience:** [Target] - **Channels:** [List] - **Budget:** $[Amount] ## Results - **[Metric 1]:** [Result] (Goal: [Target]) - **[Metric 2]:** [Result] (Goal: [Target]) ## What Worked 1. [Success 1] 2. [Success 2] ## What Didn't Work 1. [Challenge 1] 2. [Challenge 2] ## Key Learnings 1. [Learning 1] 2. [Learning 2] ## Recommendations for Next Time 1. [Recommendation 1] 2. [Recommendation 2] ```

Details

Author
davekilleen
Repository
davekilleen/Dex
Created
6 months ago
Last Updated
today
Language
Python
License
NOASSERTION

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