Four days after registering a new domain, I opened GA4 and saw 65 page views / 34 users / 9 countries.
For a brief, build-in-public moment, I almost cheered. Then I looked at the breakdown. The US had 17 sessions averaging 4.9 seconds of session duration. France, Poland, South Korea, India, Singapore: each between 0 and 1.4 seconds. Japan alone sat at 751 seconds (over 12 minutes): an outlier so loud it should be illegal.
The domain is kaoriq.com, registered on 2026-05-02, a personality-quiz × fragrance e-commerce site I'm building. As of today (May 5), it has fewer than 20 articles. Doing the back-of-the-envelope math, that page-view distribution is physically impossible to come from real humans.
This post walks through how I read the first week of GA4 data on a new domain as "me + a crawler army", with the actual numbers exposed. For anyone running GA4 on a new project, or anyone who registered a domain this weekend.
The raw data: past 14 days (4 days of real activity)
Numbers first, no spin.
Overall
| Metric | Value |
|---|---|
| Sessions | 37 |
| Page Views | 65 |
| Total Users | 34 |
| New Users | 34 |
| Avg Session Duration | 104.1 s |
| Bounce Rate | 80% |
By Country
| Country | Sessions | PV | Avg Duration (s) |
|---|---|---|---|
| Japan | 5 | 33 | 751.0 |
| United States | 17 | 17 | 4.9 |
| Canada | 4 | 4 | 1.3 |
| France | 4 | 4 | 1.4 |
| Poland | 2 | 2 | 0.0 |
| South Korea | 2 | 2 | 0.0 |
| (not set) | 1 | 1 | 0.1 |
| India | 1 | 1 | 0.0 |
| Singapore | 1 | 1 | 0.0 |
Daily
| Date | Sessions | PV | Users |
|---|---|---|---|
| 2026-05-02 (registration day) | 17 | 40 | 14 |
| 2026-05-03 | 6 | 11 | 6 |
| 2026-05-04 | 12 | 12 | 12 |
| 2026-05-05 | 2 | 2 | 2 |
At a glance, "not bad for week one" is a tempting read. But this dataset contains a 751-second Japanese reader living next door to 9 countries averaging zero seconds. The middle is missing. That gap is the whole tell.
Five signals, beaten in parallel
I never call bot traffic on a single signal. To avoid false positives, I always cross-check five axes at once.
| Signal | Bot pattern | Human pattern | kaoriq actual | Verdict |
|---|---|---|---|---|
| Session duration | 0–5 s | 30 s – several min | US 4.9s, FR 1.4s, KR 0s | Bot |
| Bounce rate | 90–100% | 40–70% | 80% | Bot |
| PV / Session | 1.0 (one page, gone) | 1.5–3.0 | US: 17/17 = 1.0 | Bot |
| Geographic anomaly | Random countries unrelated to content | Concentrated in target geo | EN/JA only, yet PL/IN/SG | Bot |
| Time-series spike | Massive day-one for new domains | Gradual ramp | 40 PV on day of registration | Bot |
Why a single signal lies
"80% bounce, must all be bots, right?" Not so fast.
- Duration alone: A reader who tabs your post and walks away for lunch racks up 30+ minutes. Indistinguishable from "deeply engaged" or "abandoned tab."
- Bounce rate alone: A landing page that perfectly answers the question gets a 100% bounce from satisfied humans. Excellence and bots both score the same.
- Geography alone: A viral overseas tweet legitimately produces multi-country traffic. Weak on its own.
You only get to call "bot" with confidence when all five signals lean the same direction simultaneously.
The bimodal distribution was the smoking gun
The real reason this verdict held in kaoriq's case is the shape of the duration distribution.
- Japan: 5 sessions / 751 s average
- Everywhere else: 0–5 s
If the traffic were genuinely human, session duration should spread more evenly across the 20–120 second band: "bounced after the title (10s)," "read the lede (40s)," "made it to the end (180s)" forming a gradient.
But kaoriq's distribution is bimodal with the middle scooped out. The honest reading: only "me (long sessions, testing the site)" and "crawlers (instant exits)" exist. Nothing in between.
Conversely, a healthy distribution would look like "Japan 100 sessions / 60s, US 50 sessions / 45s, Canada 20 sessions / 30s": durations spread normally. That'd be a real human traffic signature.
So how many real humans were there?
After all that beating, my estimate breaks down as:
| Category | Estimated sessions | Notes |
|---|---|---|
| Me, testing the site | 4–5 | Most of Japan's 5 sessions, source of the 751s average |
| Crawlers (Googlebot / Bingbot / GPTBot / ClaudeBot / AhrefsBot, etc.) | 27–30 | US 17, plus the zero-second Europe & Asia rows |
| Actual organic human traffic | 2–5 | The remainder of Japan + a couple of US sessions |
Of 37 sessions, at most 5 were real humans. That's the reality of week one for a new domain.
Why GA4 doesn't filter this for you
GA4 has a "known bots and spiders" auto-exclusion based on the IAB/ABC Spiders & Bots list. It catches classical crawlers but misses:
- JavaScript-executing crawlers: GPTBot, ClaudeBot, PerplexityBot. These new generative-AI crawlers run JS, so the GA4 tag fires.
- SEO-tool crawlers: AhrefsBot, SemrushBot, MozBot. High frequency, and they swarm new domains the moment they're discovered.
- Headless-browser scrapers: Custom Puppeteer or Playwright bots are indistinguishable from a real Chrome session.
The week after a new domain registration is when this crawler army discovers the new IP. It calms down within 7–10 days as DNS propagates. But if you take week-one GA4 at face value, you'll make bad decisions.
Three annotations every new-project dashboard needs
- Use "Engaged Sessions" as your primary metric. GA4 defines an engaged session as: ≥10s duration OR ≥2 PV OR a conversion event. Most of the bot army gets filtered here.
- Always view session duration split by country. Looking at any single metric (sessions, PV) without the geo filter lets the crawler army masquerade as success.
- Treat the first 30 days as a "noise phase." Real numbers only appear after social funnels, SEO, and content depth all line up.
Closing: look at your own GA4 with this lens
A new domain's GA4 lies for the first 1–2 weeks. If your country breakdown is full of zero-second sessions from the US, Eastern Europe, and Southeast Asia: that's the crawler parade, not humans falling in love with your content.
The procedure is simple: beat with five signals → suspect bimodal distributions → swap the primary metric to Engaged Sessions. Doing this saves you from being whipsawed by early data.
Doubting GA4 is, in the end, a discipline for not making expensive mistakes. Beat the data before the data beats you.
This post is based on real data
- Site: kaoriq.com (domain registered 2026-05-02, built with Astro v6 + Tailwind v4)
- Period analyzed: 2026-04-22 → 2026-05-05 (4 days of actual activity)
- Data source: GA4 Data API v1beta via Service Account
If you want the full LLMO playbook (how to think about AI crawlers, citations, and the measurement layer underneath the GA4 narrative):
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