Website traffic analysis: a step-by-step method in GA4

A website traffic analysis answers one question: what changed in the visits to your site, why, and what you should do about it. In GA4 the method takes seven steps: frame the question, read the long trend, split the change by channel, check the quality of each channel, find the pages behind it, segment the audience, and write one finding with one action. It takes about an hour once a month.

If you only need to know where the traffic numbers live in GA4, start with how to see your website traffic in Google Analytics. This article assumes you know the reports and shows how to turn them into a conclusion.

Step 1: Write down the question and the periods

Last month’s total only tells you that a number moved. Start with a sentence instead. “Did organic traffic grow after the site redesign?” leads to a very different analysis than “Why did enquiries fall in September?”

Then fix the periods before looking at any number:

• The main period: the last full month, or the full weeks since a change you want to evaluate.
• The comparison: in the date picker, turn on Compare. Previous period (match day of week) lines up the same weekdays, and Previous year shows the same dates a year earlier (Google’s documentation).

Use both comparisons when you can. Month over month shows what changed recently. Year over year shows whether the change is just the season. A site that sells garden furniture is not in trouble because October is below September.

Step 2: Read the long trend before the comparison

A two-period comparison hides the shape of what happened. Before you trust it, look at at least a year of traffic on one line.

The simplest way is an exploration. Go to Explore, start a Free form exploration, and switch the visualization to a line chart (Google’s documentation). Add the Sessions metric and set Granularity to week. Note that GA4 weeks start on Sunday in this chart.

Two things to check:

• Is the change a step or a slope? A sudden step on one date usually means a tracking change, a launch or a lost ranking. A slow slope over months usually means a channel is growing or fading.
• Is it unusual at all? The line chart has an anomaly detection option that marks outliers in the data (Google’s documentation).

While you are looking at the trend, mark what you already know. GA4 lets anyone with Analyst access or higher right-click a point on a report’s line graph and choose Add annotation, with a title, a date or date range and a color. The note then appears on every report with a line graph (Google’s documentation). A redesign, a price change or a newsletter launch marked once saves you from rediscovering it every month.

Step 3: Split the change by channel

Open Reports > Acquisition > Traffic acquisition with your comparison on. The default dimension is the session default channel group (Google’s documentation).

Do not read the percentages first. Read the change in sessions per channel, in absolute numbers, and find which rows account for most of the total change. Suppose total sessions fell by 1,000 and Organic Search fell by 800: the analysis is now about organic search, even if Paid Social shows a bigger percentage drop on a small base.

Two cautions:

• A channel can move because another one did. If Direct rises by about what Paid Search lost, check your campaign tagging before concluding that paid traffic fell.
• Small channels swing wildly. A channel going from 40 to 20 sessions is a 50% drop and almost certainly noise.

Step 4: Check the quality of each channel

Volume is half the story. In the same report, read two more columns for the channels that moved (Google’s documentation):

Column	What it measures	What a change tells you
Engagement rate	Share of sessions that were engaged	Whether the visits showed real interest
Session key event rate	Share of sessions with a key event	Whether the visits did what the site is for

Google defines an engaged session as one that lasted longer than 10 seconds, had a key event, or had two or more page or screen views. Session key event rate is the percentage of sessions that included a key event (Google’s documentation).

Read them against sessions:

• Sessions up, both rates stable: real growth. Find what drove it and do more of it.
• Sessions up, both rates down: the extra visits are low intent. Common causes are a post that reached the wrong audience, a broad ad campaign or bot traffic.
• Sessions flat, key event rate down: the traffic is fine and the site is the problem. Look at the pages and forms, not at marketing.

Step 5: Find the pages behind the change

Switch to Reports > Engagement > Landing page with the same comparison. To narrow it to the channel that moved, add a filter on Session default channel group, or add it as a secondary dimension if the filter is not offered (Google’s documentation). One page losing most of the gap points to that page: a lost ranking, a broken link, a redirect. Every page falling by a similar share points upstream, to the channel or the season.

If the change is in organic search, link Search Console to GA4. Its collection of reports is unpublished by default: publish it from Library in the left menu, then open Queries to see clicks, impressions, click-through rate and average position per search query (Google’s documentation). The data arrives in GA4 about 48 hours after Search Console collects it, and goes back 16 months at most.

Step 6: Segment the audience

Some changes only show up when you split visitors. Go back to your free-form exploration and switch to a table. Use Device category and Country as rows, and New users as a metric next to sessions. A free-form table takes up to 5 row dimensions, 10 metrics and 4 segments (Google’s documentation).

What to look for:

• One device behaves differently. Mobile engagement falling while desktop holds often points to a mobile layout or speed problem.
• One country appears from nowhere. A burst of sessions from a country you do not sell to, with very low engagement, is usually bots or spam.
• New users grow, returning users shrink. Acquisition is working, but people are not coming back. That is a content or email question, not a traffic one.

If a drop is sudden and none of this explains it, stop analyzing and start diagnosing: our traffic drop checklist rules out tracking, consent and filter problems first.

Step 7: Write one finding and one action

End with two sentences, written down where you will see them next month:

1. The finding, for example: “Organic sessions fell 18% month over month, almost entirely on the pricing page, which dropped from position 3 to 8 for our main query.”
2. The action, for the same example: “Update the pricing page content and internal links this week, and check its position in four weeks.”

The second sentence is what makes this an analysis. Next month, start step 1 by checking whether last month’s action worked. Add an annotation on the date you shipped it, so the trend chart in step 2 shows you the answer.

If you report these numbers to someone else, our guide to what a Google Analytics report should include covers the format, and the marketing KPIs worth checking weekly covers what to watch between two analyses.

The method on one page

Step	Where in GA4	The question it answers
1. Question and periods	Date picker, Compare	What exactly are we trying to explain?
2. Long trend	Explore, Free form, line chart	Is it a step, a slope or normal variation?
3. Channels	Acquisition, Traffic acquisition	Which channel accounts for the change?
4. Quality	Same report, engagement and key event rate	Were the visits that moved worth anything?
5. Pages	Engagement, Landing page; Search Console, Queries	Which pages or queries are behind it?
6. Audience	Free-form table	Is it one device, country or user type?
7. Finding and action	Your notes, an annotation	What do we do, and how will we know it worked?

Keeping the analysis going between months

The monthly hour works, but a broken page or a lost ranking can cost three weeks of traffic before you sit down.

That gap is what we built Dialytics for, so read this with that in mind. Dialytics is our product. It connects to your GA4 property and posts a short digest in Slack every Monday morning: what moved, the likely reason, and the one point worth your attention: a weekly version of step 3, with sitewide engagement and key events, plus the finding from step 7. It also checks GA4 every day against the same weekday over roughly the last 12 weeks and only alerts when yesterday is genuinely unusual. To dig into something, mention the bot in the thread and ask, and the answer takes earlier weeks into account.