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2026-04-18 | 📊 Hello, Google Analytics 🤖

🎬 The Mission

📈 Today we brought Google Analytics into the daily reflection workflow. 🎯 The goal: fetch yesterday’s GA4 site metrics and embed them in the corresponding reflection note, complete with wikilinks to the most-viewed pages. 🔬 This post covers the full technical journey, including two bug fixes, a metric redesign, and a deep dive into the GA4 Data API.

🔑 How It Works End to End

🔐 Authentication

🧩 Google’s GA4 Data API requires an OAuth2 bearer token. 🔑 We obtain one using a GCP service account JSON key file, which contains a PEM-encoded RSA private key. 📜 The flow: parse the RSA key from the service account JSON, build a JWT with RS256 signing and the analytics.readonly scope, POST the signed JWT to Google’s OAuth2 token endpoint at oauth2.googleapis.com/token, and receive an access token valid for one hour.

📡 The GA4 Data API

🌐 The API we call is the Google Analytics Data API v1beta. 📖 Official documentation lives at developers.google.com/analytics/devguides/reporting/data/v1. 🔗 The endpoint is a POST to analyticsdata.googleapis.com/v1beta/properties/PROPERTY_ID:runReport, where PROPERTY_ID is the numeric GA4 property identifier found in Google Analytics Admin under Property Settings.

📨 Request Format

🔧 We make two API calls per run, each a POST with a JSON body and the access token in the Authorization header.

📊 The summary request asks for five metrics: screenPageViews, activeUsers, bounceRate, screenPageViewsPerSession, and averageSessionDuration. 📅 The dateRanges array contains a single entry where startDate and endDate are both yesterday’s date in YYYY-MM-DD format. 🎯 Setting both dates equal restricts the query to exactly one day of data.

🏆 The top pages request adds a pagePath dimension to break results down by page URL, an orderBy clause sorting by screenPageViews descending, and a limit of 5 to get only the most-viewed pages.

📬 Response Format

✅ A successful HTTP 200 response returns a JSON object with a rows array. 📦 Each row contains a metricValues array (and optionally a dimensionValues array for dimension queries). 🔢 Each metric value is an object with a single value field containing the number as a string, for example “42” for an integer metric or “0.65” for a ratio like bounce rate.

🚫 When there is no data for the queried date, the API returns HTTP 200 with no rows field at all, not an empty rows array. 🛡️ Our code treats missing rows as an error and surfaces a clear message rather than silently producing zeros.

❌ When the service account lacks property access, the API returns an HTTP 403 with a JSON error body containing an error object with message and status fields (typically PERMISSION_DENIED). 🔍 We check the HTTP status code before parsing, and also inspect the response JSON for an error field as a second line of defense.

📅 Which Reflection Gets the Data

🕐 The task runs at or after 1 AM Pacific time. 📅 It fetches yesterday’s analytics data and writes it to yesterday’s reflection note. 🎯 April 17’s traffic data belongs in the April 17 reflection.

📊 Choosing the Right Metrics

🤔 The first version displayed five metrics: active users, sessions, page views, new users, and average session duration. 💭 After seeing the first real data, the question arose: are sessions and new users really telling us anything interesting?

🔍 The Analysis

📄 Page Views is the core consumption metric and stays. 👥 Active Users (renamed to Visitors) tells you reach, how many unique people visited. 🔄 Sessions largely duplicates visitors for a daily view since most visitors have one session per day. 🆕 New Users is interesting at the macro level but not very actionable daily. ⏱️ Average Session Duration tells engagement depth but averages can mislead.

✅ The New Set

📄 Page Views stays as the lead metric, the most fundamental measure of content consumed. 👥 Visitors (GA4 activeUsers) stays because knowing your unique reach is always valuable. 📊 Bounce Rate (GA4 bounceRate) replaces sessions: it tells you what percentage of visits were not engaged, defined as less than 10 seconds, single page view, and no conversion events. 📖 Pages per Session (GA4 screenPageViewsPerSession) replaces new users: it measures content depth and how well internal linking is working. ⏱️ Avg Session Duration stays for engagement depth.

🚫 Why Not Percentiles

🤷 The GA4 Data API does not expose session duration percentiles. 📊 Getting percentiles would require exporting raw event data to BigQuery, which adds significant complexity. 📈 Bounce Rate effectively gives us a binary distribution: engaged versus not engaged, which is more actionable than an average anyway.

🏆 Top Pages as Wikilinks

🔗 The top pages section now displays as a markdown table with view counts right-aligned in the first column and wikilinks in the second. 📝 Each GA URL path is resolved against the vault to find the corresponding note file and extract its title from frontmatter. 🏡 The root path ”/” maps to “index” as the wikilink target. ⚡ When a note file does not exist for a path, the raw URL path is used as a fallback alias. 📋 Pipe characters in titles are escaped for table compatibility since both wikilinks and markdown tables use the pipe character as a delimiter.

🐛 Bug Fix History

📅 Wrong Reflection Target (First Run)

💡 The code was writing to today’s reflection file but fetching yesterday’s data. 🛠️ Fix: compute yesterday’s date and use it for both the API query and the reflection file path.

🔢 All-Zero Metrics (First Run)

🔍 The API returned either no rows or an error response, and our code silently defaulted everything to zero. 🔬 Three layers of zero-coercion were hiding the real problem:

• 📭 When the API response contained no rows, parseSummaryResponse returned a success with all zeros instead of an error
• 🔢 parseIntMetric and parseDoubleMetric silently returned 0 for any unparseable string instead of failing
• 📡 fetchAnalytics did not check the HTTP status code, so a 403 error response with valid JSON was treated as a successful data fetch

🛡️ All three layers have been fixed. The parsers now return explicit errors. 📊 The logs now show HTTP status code, response size in bytes, row count, service account email, API endpoint, and the date being queried.

📖 Quick Reference

🌐 GA4 Data API documentation: developers.google.com/analytics/devguides/reporting/data/v1

🔗 API endpoint: POST analyticsdata.googleapis.com/v1beta/properties/PROPERTY_ID:runReport

🔑 Required scope: googleapis.com/auth/analytics.readonly

📅 Date format: YYYY-MM-DD (both startDate and endDate set to the same day for single-day queries)

📊 Metrics used: screenPageViews, activeUsers, bounceRate, screenPageViewsPerSession, averageSessionDuration

🗂️ Dimension used: pagePath (for top pages breakdown)

💡 Twenty Ideas for the Future

🧠 With the analytics pipeline in place, here are the most exciting directions:

1. 🔥 Trending content detection to surface posts gaining momentum
2. 📈 Week-over-week comparisons right in the daily reflection
3. 🌍 Geographic visitor distribution summaries
4. 📱 Device breakdown analysis for content optimization
5. 🔍 Search query analysis to understand what terms bring visitors
6. 🚪 Landing page analysis to see which content attracts new visitors
7. 📉 Bounce rate trends to identify content that needs improvement
8. ⏰ Engagement patterns by time of day for optimal posting
9. 🔗 Referral source tracking to see where visitors come from
10. 📚 Performance comparison across blog series
11. 🏆 Monthly top posts digest in a dedicated note
12. 🤖 AI-generated content recommendations based on popular topics
13. 📈 Engagement scoring combining views, duration, and return visits
14. 🔄 Social media ROI by correlating posts with traffic spikes
15. 📊 Content freshness indexing to flag popular but outdated posts
16. 🎯 New versus returning visitor content preferences
17. 📈 SEO performance tracking for organic search trends
18. 🔍 Four oh four error monitoring from analytics data
19. 📊 Reading depth estimation based on session duration
20. 🤖 Auto-generated weekly analytics blog post summarizing the week

📚 Book Recommendations

📖 Similar

• Lean Analytics by Alistair Croll and Benjamin Yoskovitz is relevant because it teaches how to use data to build a better startup, and the same principles apply to building a better blog with metrics-driven feedback
• 📈🎯✅📏 Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs by John Doerr is relevant because it shows how tracking the right metrics transforms outcomes, just as adding analytics to daily reflections provides actionable feedback loops

↔️ Contrasting

• The Art of Not Giving a Frick by Mark Manson offers a contrasting view where ignoring metrics and focusing on intrinsic motivation matters more than data-driven optimization

🔗 Related

• Real World Haskell by Bryan O’Sullivan, John Goerzen, and Don Stewart is relevant because the entire analytics integration was built in Haskell with pure functional patterns
• Web Analytics 2.0 by Avinash Kaushik is relevant because it covers the philosophy of web analytics and how to extract meaningful insights from visitor data