Suddenly Singapore: When bots distort your GA4 numbers

At first, the dashboard looks encouraging. More visitors, more pageviews, a healthy climb in the chart. Perhaps a story is taking off?

A closer look makes things less clear. A large share of the additional traffic comes from Singapore. Visits are spread across old articles that barely feature in the current editorial day. And while GA4 reports a lot more activity, NowMetrix shows a much quieter picture.

That is usually when the question comes up: which number can we actually trust?

Other website owners are noticing it too

There are several reports along these lines in Reddit's r/GoogleAnalytics community. Website owners describe unexpected visits from Singapore and China, unusually high numbers of desktop users, and traffic that does not seem to match their usual audience.

In a discussion about Singapore and Lanzhou, the author reports that visits still appear in GA4 after blocking those countries in Cloudflare. They cannot find corresponding country IP addresses in their server logs.

Another user describes a spike spread across thousands of URLs. The unusual visits are concentrated on Windows and one particular Chrome version, with very little measured engagement.

These are accounts from website owners, not completed technical investigations. But they do show that anyone puzzled by this kind of traffic is not alone.

Doesn't Google Analytics filter bots automatically?

It does. Google's documentation on bot filtering says that known bots and spiders are automatically excluded. Google uses its own research alongside a list maintained by the industry body IAB.

The important word is “known”.

Automatic filtering does not mean that every recorded pageview comes from a person. The Reddit reports are therefore compatible with GA4 already removing bots. Unusual data can still remain. Google's documentation also says that users cannot see how much known bot traffic was excluded.

For a newsroom, that leaves a practical task: investigate an unusual spike before turning it into a story about audience growth.

Why top articles can match while the totals differ

When comparing GA4 and NowMetrix, people usually start with their successful stories. That makes sense. If the most important articles have similar pageview counts in both systems, tracking appears to be working overall.

But the total includes everything else as well.

Just one additional view on each of 20,000 different articles adds up to 20,000 extra pageviews. None of that might be visible in a top-50 list. In the daily report, the difference suddenly looks substantial.

That is why it helps to look at pages with low view counts. Is the discrepancy spread across the archive? Are there lots of isolated visits? Do they mostly come from one country or one browser group?

This pattern can point towards automated traffic. It still needs to be checked alongside whether both tools measure the same period and website, and whether both trackers are installed correctly.

Why a Cloudflare block does not always answer the question

The term “ghost traffic” comes up frequently in these discussions. It refers to apparent visits in an analytics report that may have no corresponding visit to the actual website.

The website and Google Analytics' data collection are separate destinations. For example, GA4 can receive events sent from servers through the Measurement Protocol. There are legitimate reasons to do this. It also illustrates why an analytics event does not automatically establish that a new page request reached the website's server.

If false events arrive directly at an analytics service, a firewall in front of the website cannot stop that transmission. Whether this explains a particular case needs investigation. Missing entries in the web server log alone are not enough: a CDN can serve pages from its cache, and different services may assign different countries to traffic.

There are several possible routes to an unusual number. Changing how your own tracker sends data will not automatically address all of them.

Where we would start investigating

A useful first comparison does not need to become a huge project. One carefully defined day and a few focused questions often achieve more than ten extra reports.

• Match the period and the website. Compare GA4 and NowMetrix for the same completed day in the same time zone. Check the hostname too: do the GA4 figures include subdomains or additional websites?
• Start with pageviews. User counts also depend on how people are recognised and how a tool defines a user. Pageviews are usually easier to trace as a starting point.
• Break down the unusual traffic. Country, browser, operating system, device category and screen resolution can help show whether the discrepancy is concentrated in one group. Look at the affected articles and engagement alongside them.
• Trace a few specific pages. Compare matching time windows in the website's CDN logs and the tracking logs. A page request and a message to an analytics endpoint are two separate things.

A country or screen resolution on its own is not enough reason to block visitors. The useful evidence comes when several signals line up and the pattern can also be found in the technical data.

Analyse the attached GA4 CSV export. Use code, such as Python, to calculate the result from all data rows.

I already selected the following in GA4:
- Day: [date]
- Property time zone: [time zone]
- Country: [country, e.g. Singapore]
- Hostname: [hostname or "no hostname filter"]

Find page paths with exactly one view in total within this selection, broken down by browser.

1. Inspect the file and any metadata. The required columns are Page path and screen class, Browser, and Views, or their translated equivalents. Country and hostname do not need separate columns if they were already filtered in the report. Flag conflicts with my details and ask if columns are missing or the selection is unclear.

2. Parse views as numbers, accounting for thousands separators. Do not count comment lines or overall summary rows as pages. First sum each page path's views across every browser and data row. Only then keep page paths with a total of exactly 1. An article with one Chrome view and one Safari view has 2 views in total and must be excluded. Do not use user or session counts for this.

3. For the selected page paths, show a table sorted by views: browser, views, and percentage of all selected views. Keep unknown browsers as a separate category. Check that the browser totals equal the number of selected page paths. Say explicitly if there are no matches.

4. Report the number of page paths analysed, their total views, and how many page paths have exactly one view. Also provide these selected page paths with their browser and views as a CSV. Briefly explain the calculation and show three example rows, if available.

Flag export limits, grouped rows such as "(other)", and missing page paths; do not treat them as individual articles. Limit the findings to this export, do not invent missing values, and do not infer proof of bot activity from this pattern alone.

How we approach this at NowMetrix

At NowMetrix, bot filtering is part of our ongoing product work. We filter recognised bot traffic out of collection and continue to maintain the rules. A newsroom needs to know which stories people are reading and where audience interest is growing.

That is why we investigate discrepancies with GA4. We compare articles, periods and traffic groups, then examine where the numbers diverge. A lower number can result from effective bot filtering. It can also indicate a tracking problem. Making that distinction is part of providing dependable analytics.

Our aim is for the numbers in your live dashboard to be useful throughout the editorial day. A spike should help you spot a story, prioritise an update or follow up on a topic. That means paying attention to what happens behind the chart as well.

If your GA4 numbers suddenly look different or diverge significantly from NowMetrix, get in touch. We will look at the specific case together and investigate the difference.

Explore the NowMetrix and Google Analytics comparison