/start-2-4

/training:start-2-4 - Analyze Campaign Data

Prerequisites

Before this lesson:

• Completed /training:start-2-3 (Marketing Copy)
• Understand content creation
• Ready for data analysis

Context Loading

Reference these files:

1. ./docs/analytics-setup.md - Analytics configuration
2. ./.claude/skills/analytics-attribution/SKILL.md - Attribution models
3. ./.claude/workflows/data-reliability-rules.md - Data standards

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Language & Quality Standards

CRITICAL: Respond in the same language the user is using. If Vietnamese, respond in Vietnamese. If Spanish, respond in Spanish.

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Instructions for Claude

Teach data analysis, insight extraction, and executive reporting using analytics commands.

Lesson Overview

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Module 2.4: Analyze Campaign Data

Data analysis is often time-consuming. Let's master turning data into actionable insights and compelling reports.

Duration: ~35 minutes

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Step 1: ROI Analysis

Use analytics commands:

/analytics:roi "Q1 campaign - $50K spend across LinkedIn, Google, Email"

Review ROI calculation:

• Total spend by channel
• Revenue attributed
• ROAS by channel
• Cost per acquisition

Step 2: Funnel Analysis

Analyze conversion funnel:

/analytics:funnel "trial signup - visitor to trial to paid conversion"

Review funnel metrics:

• Traffic by source
• Conversion rates at each stage
• Drop-off points
• Optimization opportunities

Step 3: Performance Reporting

Generate performance reports:

Weekly Report:

/report:weekly "AgentKits" "current week"

Monthly Report:

/report:monthly "AgentKits" "current month"

Step 4: Channel Performance

Analyze by channel:

/analytics:report "channel performance" "LinkedIn, Google, Email, Organic"

Create channel comparison:

• Traffic contribution
• Lead quality
• Conversion rates
• Cost efficiency

Step 5: Content Performance

Analyze content effectiveness:

/analytics:report "content performance" "blog posts, landing pages, email sequences"

Key metrics:

• Traffic by content piece
• Engagement (time, scroll, shares)
• Conversion rate
• Lead quality

Step 6: Lead Quality Analysis

Use lead scoring to analyze:

/crm:score "analyze lead quality by source and campaign"

Review:

• MQL rate by source
• SQL conversion by campaign
• Average lead score trends

Step 7: Executive Summary

Create executive-ready summary:

Create an executive summary of Q1 marketing performance:

STRUCTURE:
1. Headline metrics (vs targets)
2. Top 3 wins with data
3. Top 3 challenges with impact
4. Channel performance snapshot (table)
5. Key learnings (3 insights)
6. Q2 recommendations (prioritized)
7. Budget request with justification

Keep it to ONE PAGE maximum.

Step 8: Data-to-Action Framework

Teach the insight framework:

For each finding, document:

1. OBSERVATION: What does the data show?
2. INSIGHT: Why is this happening?
3. IMPLICATION: What does it mean?
4. RECOMMENDATION: What should we do?
5. EXPECTED IMPACT: What will change?

Step 9: Operational Checklists

Use analytics checklists:

/checklist:analytics-monthly "current month" "AgentKits"

Review monthly analytics tasks:

• Data quality checks
• Platform verification
• Reporting accuracy
• Attribution validation

Step 10: Reporting Templates

Explain reusable reporting:

Weekly Report Workflow:
1. /analytics:roi "campaign" - Calculate ROI
2. /analytics:funnel "funnel" - Analyze funnel
3. /report:weekly "client" "week" - Generate report

Monthly Report Workflow:
1. /analytics:report "all channels" - Full analysis
2. /crm:score "lead quality" - Lead analysis
3. /report:monthly "client" "month" - Generate report

What's Next

Tell them:

• They can now turn data into decisions
• Reports that executives actually read
• Next: /training:start-2-5 - Competitive Analysis
• Research competitors and find advantages

Key Teaching Points

• /analytics:* commands analyze performance
• /report:* commands generate reports
• ROI and funnel analysis are foundational
• Executive summaries must be concise
• Data-to-action framework ensures accountability