Your GA4 Data Knows Where Your Leads Come From. You Just Can't See It.
Someone from your board just asked: "Which traffic sources are actually generating qualified leads?"
And you... opened GA4. Clicked through Acquisition. Then Engagement. Then Conversions. Switched to a custom exploration. Lost the thread. Started over. Pulled up that Looker Studio dashboard someone built six months ago. Squinted at a table with 47 rows of UTM parameters.
Thirty minutes later, you have a partial answer and a headache.
The data exists. GA4 is tracking every session, every source, every page view, every conversion event. But connecting "organic social" to "demo request" to "actual qualified lead" requires flipping through multiple reports, remembering the difference between sessions and engaged sessions (seriously, does anyone actually know?), and holding half the analysis in your head while you hunt for the other half.
Turns out, attribution isn't a data problem. It's a visualization problem.
The Metric Tree You Can't Build in GA4
Here's what you actually need to understand:
Which traffic sources drive low-intent actions (people just hitting your site), which push users toward mid-intent behaviors (newsletter signups, pricing page views), and which convert to high-intent outcomes (demo requests, premium trial starts).
It's a metric tree. Traffic sources branch into intent levels. Intent levels branch into conversion actions. Each split point tells you something about quality, not just quantity.
But GA4 shows you... tables. Lots of tables. Traffic sources in one report. Conversion events in another. Engaged sessions somewhere else entirely. You can build a custom exploration that gets close, but good luck sharing that with your CEO in a way that doesn't require a GA4 certification to interpret.
So marketing teams do what they always do—they export to spreadsheets. They manually map traffic sources to outcomes. They create static slide decks showing the attribution story. And by the time leadership sees it, the data's three weeks old and the conversation has moved on.
Meanwhile, your growth team is making budget decisions based on "sessions" because that's the easiest metric to extract. Never mind that the 10,000 sessions from that viral Reddit post converted zero users, while 200 sessions from your partner program generated fifteen demos.
Same data, completely different stories.
What If GA4 Data Worked Like Your Brain Does?
Count's GA4 metric tree template shows you the full attribution story in one canvas.
Traffic sources (organic search, paid social, email, referral, direct) split into intent levels. Low-intent website sessions. Mid-intent pricing views and newsletter signups. High-intent demo requests and premium trial starts. All with live conversion metrics from your BigQuery export of GA4 data.
Which is a fancy way of saying: you can finally see which traffic sources actually matter for business growth, not just vanity metrics.
Here's the thing—because Count connects directly to BigQuery (that GA4 export feature everyone enables but never uses), your metric tree updates automatically. And because it's a canvas, not a dashboard, your team can annotate it. Drop a sticky on that paid social drop-off. Start a threaded discussion about why organic search converts so well to mid-intent but stalls before demos. Comment on the surprising performance of that partner referral channel.
Your CMO, your demand gen lead, your content strategist, and your data analyst are all looking at the same attribution story. In real-time. With their questions and insights attached directly to the metrics.
No more screenshot archaeology. No more "wait, which date range is this?" No more explaining engaged sessions to executives who just want to know if the website is working.
Attribution That Actually Attributes
And because Count lets you join GA4 data to other sources—your CRM, your product analytics, your customer data platform—you're not stuck with "demo request form submitted" as your definition of success. Connect it to closed-won deals. Revenue. Actual customer LTV.
Build your semantic layer once using Count Metrics, and suddenly your marketing team can slice attribution by source without accidentally miscounting or double-joining data. Done.
The reality is: your website generates leads. GA4 tracks everything. But between "tracking" and "understanding" is a massive gap filled with confusing interfaces, isolated reports, and metrics nobody trusts.
Marketing lead attribution shouldn't require a data engineering degree.
Your traffic sources split into user intent. User intent splits into conversion actions. Conversion actions (ideally) split into revenue.
Build that metric tree once. Let the data update itself. Let your team collaborate on it. And finally answer "which traffic sources generate qualified leads?" in under two minutes instead of thirty.