anomaly-scan

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Detect marketing anomalies. Use when: traffic drops, cost spikes, conversion changes, deliverability issues, budget overruns.

AI & Automation 136 stars 37 forks Updated 3 days ago MIT

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Skill Content

# /digital-marketing-pro:anomaly-scan ## Purpose Scan all connected marketing platforms for anomalies — statistically significant deviations from established baselines that could indicate problems (traffic drops, CPA spikes, deliverability collapse, budget overruns) or opportunities (viral content, conversion rate improvements, unexpected channel growth). Designed to catch issues early, before they compound into costly problems, and to surface wins worth amplifying. ## Input Required The user must provide (or will be prompted for): - **Sensitivity level**: Strict (flags deviations >1.5 standard deviations from baseline), normal (>2 std dev), or relaxed (>3 std dev). Defaults to normal - **Time period**: The window to scan for anomalies — today, last 3 days, last 7 days, last 30 days, or custom range. Defaults to last 7 days - **Platforms** (optional): Specific platforms to focus the scan on (e.g., "Google Ads and Meta only"). If omitted, all connected platforms are scanned - **Metrics focus** (optional): Specific metrics to prioritize (e.g., "CPA and conversion rate only"). If omitted, all available metrics are evaluated - **Baseline period** (optional): Custom baseline for comparison instead of the default. Defaults to the rolling 30-day average maintained by performance-monitor.py - **Exclude known events** (optional): List of known events to filter out (e.g., "Black Friday sale", "site migration on Jan 15") so expected deviations are not flagged as anomali...

Details

Author
indranilbanerjee
Repository
indranilbanerjee/digital-marketing-pro
Created
4 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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