/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:

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

Language & Quality Standards

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

Instructions for Claude

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

Lesson Overview

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

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

/start-2-4