jangwook.net Blog Launch Analysis Report

Transparency Declaration: This report is an honest record of the early launch stage. Instead of impressive numbers, I’m sharing actual data and the learning process as it is.

1. Overview

Background of Blog Launch

In October 2025, I officially launched jangwook.net, a technical blog based on Astro 5.14. This blog is designed to be more than just a technical blog - it’s a platform that realizes content automation, SEO optimization, and data-driven decision making.

Key Differentiators:

🌏 Multilingual Support: Korean, English, and Japanese content
📊 GA4 MCP Integration: Automated analysis using Google Analytics MCP
🚀 Islands Architecture: Ultra-fast static site based on Astro
🔄 Automated Reporting: Data-driven content strategy

Analysis Environment

GA4 Property ID: 395101361
Property Name: jangwook.net
Analysis Tools: Google Analytics 4 (MCP Integration)
Analysis Date: October 6, 2025
Time Zone: Asia/Tokyo (JST)
Currency: USD
Data Collection Start: July 2023 (Property creation date)

Current Status: Early Data Collection Phase

At the time of writing this report, GA4 is installed, but due to a 24-48 hour data processing delay, historical data has not yet been collected.

However, real-time data is being collected normally, allowing us to observe current user behavior.

Data Processing Pipeline:

Real-time Collection (0-5 min delay)
    ↓
Real-time Reports (Immediately queryable) ← Current stage
    ↓
Batch Processing (24-48 hours)
    ↓
Standard Reports (Historical analysis available) ← Waiting

2. Real-time Data Analysis

2.1 Current Active Users

Real-time data collected at the time of analysis:

Activity by Page:

EffiFlow: 4 pageviews, 1 active user
Contact: 2 pageviews, 1 active user
Blog: 2 pageviews, 1 active user
About: 2 pageviews, 1 active user
Social: 2 pageviews, 1 active user

Device Distribution:

Desktop: Main traffic (Japan region)
Mobile: Small amount of traffic (no region info)

Geographic Distribution:

Japan: Source of all desktop traffic

2.2 Initial Observations

Positive Signals:

Diverse Page Navigation: Users visit multiple pages instead of staying on a single page
EffiFlow Page Engagement: High interest in specific project page (4 pageviews)
Navigation Usage: Exploration of various sections like Contact, About, Social

Areas for Improvement:

Traffic Source Diversification: Currently focused on single region (Japan)
Mobile Optimization: Very little mobile traffic
Tracking Expansion: Need more sophisticated event tracking

3. Practical GA4 MCP Query Examples

3.1 Ready-to-Execute Analysis Queries

For readers starting blog analysis, I’m sharing actually usable MCP query examples.

Query 1: Real-time Visitor Status

// Who's on your blog right now?
mcp__analytics -
  mcp__run_realtime_report({
    property_id: 395101361,
    dimensions: ["unifiedScreenName", "country"],
    metrics: ["activeUsers"],
  });

Result Interpretation:

Current active user count
Which pages they’re viewing
Which country they’re from

Query 2: Last 7 Days Traffic Trend

// How's the weekly growth?
mcp__analytics -
  mcp__run_report({
    property_id: 395101361,
    date_ranges: [{ start_date: "7daysAgo", end_date: "today" }],
    dimensions: ["date"],
    metrics: ["activeUsers", "sessions", "screenPageViews"],
    order_bys: [
      { dimension: { dimension_name: "date", order_type: 1 }, desc: true },
    ],
  });

How to Use:

Identify daily traffic patterns
Analyze weekend vs weekday differences
Confirm growth trends

Query 3: Top 10 Popular Blog Posts

// Which content is performing best?
mcp__analytics -
  mcp__run_report({
    property_id: 395101361,
    date_ranges: [{ start_date: "30daysAgo", end_date: "today" }],
    dimensions: ["pagePath", "pageTitle"],
    metrics: ["screenPageViews", "activeUsers", "userEngagementDuration"],
    dimension_filter: {
      filter: {
        field_name: "pagePath",
        string_filter: {
          match_type: 2,
          value: "/blog/",
          case_sensitive: false,
        },
      },
    },
    order_bys: [{ metric: { metric_name: "screenPageViews" }, desc: true }],
    limit: 10,
  });

Analysis Points:

screenPageViews: Popularity
activeUsers: Reach
userEngagementDuration: Content quality

Query 4: Traffic Source Analysis

// Where are your visitors coming from?
mcp__analytics -
  mcp__run_report({
    property_id: 395101361,
    date_ranges: [{ start_date: "30daysAgo", end_date: "today" }],
    dimensions: ["sessionDefaultChannelGroup", "sessionSource"],
    metrics: ["sessions", "bounceRate", "averageSessionDuration"],
    order_bys: [{ metric: { metric_name: "sessions" }, desc: true }],
  });

Benchmark Comparison:

Source	Tech Blog Average	Target
Organic Search	25-40%	30% (3 months), 65% (12 months)
Direct	20-30%	40% (initial)
Social	15-25%	20%
Referral	10-20%	10%

3.2 Setting Measurement Baselines

Core KPI Framework (Excerpted from strategy document):

Primary KPIs (North Star Metrics)

1. Monthly Active Readers (MAR)

Definition: Unique visitors who viewed at least one blog post per month
3-month target: 500
6-month target: 2,000
12-month target: 5,000

2. Organic Search Traffic %

Definition: Percentage of search engine traffic out of total traffic
3-month target: 30%
6-month target: 50%
12-month target: 65%

3. Average Engagement Time

Definition: Average engagement time per blog post
3-month target: 3:00 min
6-month target: 4:30 min
12-month target: 6:00 min

Secondary KPIs

Traffic Metrics:

Daily Active Users (DAU)
Pageviews
Session count
Average session duration

Engagement Metrics:

Bounce Rate: <60% (good), <40% (excellent)
Pages/Session: 1.5+ (acceptable), 2.5+ (good)
Returning Visitor Rate: 20%+ (3 months), 35%+ (12 months)

Conversion Metrics:

Portfolio page click-through rate: 8-12% target
Contact page visit rate
Social link click rate

4. Expected Performance and Benchmarks

4.1 Technical Blog Industry Benchmarks

Typical personal technical blog metrics for the first 3 months:

Traffic:

Daily visitors: 10-50 (varies by content quality)
Monthly pageviews: 300-1,500
Main sources: Direct (30%), Organic Search (25%), Social (20%)

Engagement:

Average session duration: 1-3 minutes
Bounce rate: 60-80%
Pages/session: 1.5-2.5

Devices:

Desktop: 60-70%
Mobile: 25-35%
Tablet: 5-10%

4.2 jangwook.net Goal Setting

1-month target (November 2025):

DAU: 20-30
Monthly pageviews: 500-800
Average session duration: 2+ minutes
Bounce rate: <70%
Traffic channels: Direct 40%, Organic 30%, Social 20%, Referral 10%

3-month target (December 2025):

DAU: 50-80
Monthly pageviews: 2,000-3,000
Organic Search ratio: 40%+
Returning visitor rate: 20%+

5. Insights from Data Scarcity

5.1 Advantages of Early Launch

Paradoxically, this moment without data is the most important:

Clean Slate: Build correct tracking structure from the start without wrong settings
Establish Baseline: Can clearly measure all improvement effects
Experimentation Opportunity: Freely try A/B tests, content strategies, etc.

5.2 Learning from Current Real-time Data

Finding 1: Importance of Project Pages

EffiFlow page records most pageviews
Action: Strengthen project portfolio as main content

Finding 2: Effectiveness of Navigation Structure

Users naturally explore multiple pages
Action: Maintain current navigation structure, strengthen internal links

Finding 3: Regional and Device Patterns

Early traffic centered on Japan region, desktop
Actions:
- Consider expanding multilingual content (Japanese content)
- Prioritize mobile UX optimization

6. Immediate Action Plan

6.1 Short-term Actions (1-2 weeks)

1. Enhanced Event Tracking

// Events to add
- blog_post_read_complete (100% scroll reached)
- contact_button_click (contact click)
- social_link_click (social link by type)
- external_link_click (external link click)

2. Content Strategy

2-3 technical blog posts per week
Project case study writing
SEO-optimized titles and meta descriptions

3. Technical Improvements

Mobile responsive design verification
Page loading speed optimization (Core Web Vitals)
Structured data (Schema.org) addition

6.2 Medium-term Strategy (1-3 months)

1. Traffic Source Diversification

SEO: Keyword research and content optimization
Social: LinkedIn, Twitter(X) activation
Community: Developer community participation (Reddit, Dev.to)

2. Content Performance Analysis

Identify top 10 posts
Analyze success patterns (topic, length, structure)
Improve or consolidate underperforming content

3. Conversion Optimization

Add newsletter subscription CTA
Optimize project inquiry conversion path
Implement related post recommendation algorithm

6.3 Long-term Vision (3-6 months)

1. Data-driven Content Automation

Automatic topic detection using GA4 API
AI-based content recommendation system
Automatic performance report generation

2. Community Building

Comment system introduction (Giscus, etc.)
Guest post program
Technical seminar/webinar hosting

3. Monetization Strategy

Sponsored content (ethical disclosure principles)
Digital product sales (eBook, courses)
Consulting service integration

7. Next Analysis Cycle Plan

7.1 Analysis After 1 Week (October 13, 2025)

Purpose: Verify initial data collection

Checklist:

Confirm historical data collection complete
Identify daily traffic patterns
Determine main inflow paths
Analyze device/browser distribution
Top 5 pages for first week

Expected Insights:

Day-of-week traffic patterns
Total first-week visitors
Initial viral effect status

7.2 Analysis After 1 Month (November 6, 2025)

Purpose: Monthly performance evaluation and strategy adjustment

Analysis Items:

Monthly core metric achievement rate
Content performance ranking
Conversion rate by traffic channel
User journey mapping
SEO performance (Organic keywords)

Decision Points:

Content topic direction adjustment
Marketing channel reallocation
Technical improvement priorities

7.3 Analysis After 3 Months (January 6, 2026)

Purpose: Quarterly retrospective and 2026 strategy establishment

Strategic Questions:

Which content was most effective?
How does performance compare to targets?
What unexpected successes/failures occurred?
What’s the core strategy for 2026?

8. Transparency and Learning

8.1 Limitations of This Report

This analysis report has the following limitations:

Data Scarcity: Historical data not collected, trend analysis impossible
Sample Size: Only extremely limited real-time data used
Statistical Significance: Cannot draw statistical conclusions at this point
External Factors: Insufficient consideration of seasonality, events, etc.

8.2 Learning Points

What I learned through this experience:

1. Understanding GA4 Data Pipeline

Difference between real-time vs historical data
Data processing delay time
Data access methods via API

2. Importance of Early Stage

Correct tracking setup is the foundation of all analysis
Cannot measure improvement effects without baseline
Early design determines long-term strategy

3. Transparent Communication

Don’t hide data scarcity, disclose it
Acknowledge limitations and turn them into learning opportunities
Share the journey of growing together with readers

9. Practical Guide for Readers

9.1 Starting Your Blog Analysis

7-day Action Plan that you, the reader, can start right away:

Day 1: Baseline Assessment (30 min)

// 3 queries to run
1. Real-time status (Query 1)
2. 7-day traffic (Query 2)
3. Popular content (Query 3)

// What to record
- Current DAU (Daily Active Users)
- Most popular posts
- Main traffic sources

Day 2: Custom Dimension Setup (1-2 hours)

// In GA4 Admin
1. Create Custom Definitions
   - Content Language (ko/en/ja)
   - Content Type (blog_post/page)

2. Modify blog template
   gtag('event', 'page_view', {
     'content_language': 'en',
     'content_type': 'blog_post'
   });

Day 3-5: Enhanced Event Tracking

Scroll depth (75%, 100%)
External link clicks
Read completion (based on dwell time)

Day 6-7: First Weekly Report Writing

What to include:

Key metrics (users, sessions, pageviews)
Top 5 posts
Traffic source analysis
1-2 action items for next week

9.2 Frequently Asked Questions (FAQ)

Q1: GA4 data appears differently in MCP and UI A: Consider 24-48 hour data processing delay. Real-time reports are immediate, standard reports are delayed.

Q2: Which metrics should I focus on? A: For the first 3 months, focus on Monthly Active Readers (MAR) and Organic Search %. These two metrics best represent blog health.

Q3: I’m not meeting benchmark numbers - is it a failure? A: Growth trends are more important than absolute numbers. If you maintain 10% week-over-week growth, you can achieve targets within 3 months.

Q4: How much time should I invest in analysis? A:

Daily: 5 min (real-time check)
Weekly: 30 min (weekly report)
Monthly: 2 hours (strategy review)

Q5: What’s the key to multilingual blog analysis? A: Set independent benchmarks for each language. Korean and English content operate in different markets and competitive environments.

9.3 Additional Learning Resources

Official Documentation:

Recommended Tools:

Looker Studio: Custom dashboard creation
Google Search Console: SEO performance tracking
PageSpeed Insights: Core Web Vitals monitoring

Community:

Analytics Mania Blog (advanced techniques)
Measure School YouTube (video tutorials)

10. Conclusion

10.1 Early Launch Evaluation

jangwook.net blog has been successfully launched technically:

✅ Success Factors:

Astro-based high-performance static site (Core Web Vitals optimized)
GA4 + MCP analysis system working (automation ready)
Real-time user tracking and behavior observation available
Multilingual (ko/en/ja), multi-device access confirmed
Transparent data sharing culture established ← Most important

⏳ In Progress:

Historical data collection (24-48 hour wait)
Custom dimension implementation (language tracking)
Content library expansion (2-3 posts per week)
Traffic source diversification (SEO, social, community)

10.2 Future Roadmap

This blog will evolve into a data-driven learning platform, not just a static site:

After 1 week (2025-10-13):

✅ First historical data-based analysis report
✅ Daily traffic pattern identification
✅ Main inflow path identification

After 1 month (2025-11-06):

📊 Monthly core metric achievement evaluation
🎯 Content strategy optimization (performance-based)
🔄 SEO keyword analysis and adjustment

After 3 months (2026-01-06):

🤖 Automated weekly/monthly report system
📈 500 MAR target achievement verification
🧠 Data-driven content recommendation engine

After 6 months (2026-04-06):

🌍 2,000 MAR achievement and community activation
💰 Newsletter and monetization strategy launch
🔮 AI-based performance prediction model

10.3 Message to Readers

What makes this report special is that it shares a genuine journey, not perfect data.

Many analysis reports are full of impressive graphs and numbers, but the failures, trial and error, and learning process behind them are not shared.

jangwook.net is different. We:

❌ Don’t hide failures → Transparently disclose even data scarcity
📚 Share what we learned → Understanding GA4 pipeline, MCP usage
🤝 Grow together with readers → Insights applicable to your blog too

You can do it too:

GA4 setup (30 min)
Copy and run queries from this article (10 min)
Write first weekly report (1 hour)
Start data-driven improvements (ongoing)

In the next report, I’ll share deeper insights along with actual data.

📅 Next Report Preview

Title: “What a Week of Data Tells Us: jangwook.net First Weekly Analysis” Publication Date: October 13, 2025 (1 week later) Contents:

✅ Complete historical data analysis
📊 Daily/hourly traffic patterns
🎯 First week performance vs targets
🔧 Problems discovered and solutions
📈 Week 2 optimization strategy

Series Tags: #BlogAnalytics #DataDriven #Transparency #WeeklyReport

If this article was helpful:

🔗 Share: With fellow developers facing similar challenges
💭 Leave comments: Your blog analysis experience and tips
📧 Contact: 1-on-1 questions at Contact

Let’s learn and grow together. Looking forward to your first analysis report! 🚀

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