audience-analytics
Analyze visitor data, attendance patterns, and engagement metrics to inform programming decisions and measure organizational impact
Best use case
audience-analytics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze visitor data, attendance patterns, and engagement metrics to inform programming decisions and measure organizational impact
Teams using audience-analytics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/audience-analytics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How audience-analytics Compares
| Feature / Agent | audience-analytics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze visitor data, attendance patterns, and engagement metrics to inform programming decisions and measure organizational impact
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Audience Analytics Analyze visitor data, attendance patterns, and engagement metrics to inform programming decisions and measure organizational impact. ## Overview This skill enables systematic analysis of audience data for cultural organizations. It encompasses visitor research, attendance analysis, engagement measurement, and impact assessment to inform strategic and programmatic decision-making. ## Capabilities ### Visitor Data Analysis - Analyze ticketing and admission data - Segment audiences by demographics - Track membership and repeat visitation - Evaluate geographic reach - Assess visitor pathways and behaviors ### Attendance Patterns - Identify seasonal and temporal trends - Analyze program-specific attendance - Compare historical performance - Forecast future attendance - Benchmark against peers ### Engagement Metrics - Measure digital engagement - Track social media performance - Assess email and marketing effectiveness - Evaluate program participation - Monitor membership engagement ### Impact Assessment - Develop impact indicators - Measure educational outcomes - Assess community benefit - Calculate economic impact - Report on mission achievement ## Usage Guidelines ### Data Collection 1. Establish data collection protocols 2. Ensure privacy compliance 3. Integrate multiple data sources 4. Maintain data quality standards 5. Document methodology ### Analysis Approaches - Apply descriptive statistics - Conduct trend analysis - Perform segmentation studies - Use predictive modeling - Create visualization dashboards ### Reporting Standards - Tailor reports to stakeholder needs - Visualize data effectively - Provide actionable insights - Document limitations - Track metrics over time ## Integration Points ### Related Processes - Marketing Campaign Process - Visitor Experience Design Process - Cultural Impact Assessment Process ### Collaborating Skills - donor-relationship-management - digital-engagement-strategy ## References - AAM Visitor Studies resources - Visitor Studies Association standards - Cultural analytics methodologies - Museum metrics frameworks
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