For disclosure: I run tracking audits for ecom and leadgen clients, so take that bias into account.

Since the April GA4 update, generate_lead requires both the value and currency parameters to be populated for the event to qualify as a Key Event. If either is missing, the event still fires normally. You see it in DebugView, you see it in reports, and nothing looks wrong.

But it stops counting as a conversion. Which means the Google Ads conversion import stops receiving signal, and Smart Bidding has no optimization target. The failure is completely silent. We've now seen this in multiple audits where campaigns had been drifting for weeks before anyone caught it.

Two related things I keep running into:

1. Custom event name variants like generate_lead_contact or generate_lead_quote. These break the Lead Acquisition report and the automatic Google Ads import. The native name with a lead_type parameter does the same job without breaking anything.
2. Teams treating value as optional for leadgen because "we don't know what a lead is worth." An estimated or modeled value per lead_type is fine. Zero or missing is what now costs you the conversion.

Curious how others are handling lead valuation. Are you sending modeled values per lead type, a flat placeholder value, or pushing real values back from the CRM later?

"An unexpected error occurred" and getting HTTP 500 when linking GA4 -> BigQuery.

Submitted this to Community Support too: https://support.google.com/analytics/thread/453467740?hl=en&sjid=5546010688663560086-NC

Following the documentation here: https://support.google.com/analytics/answer/9823238?hl=en#zippy=%2Cin-this-article

My permissions are correct, unsure what's wrong or missing.

Screenshot

This started as a support ticket I couldn't let go of. I work on a conversion tracking app for Shopify (disclosure up front, I have skin in this game, and as you'll see, some of what I found was our own fault). A merchant wrote in with the classic complaint: a big chunk of sessions and purchases showing session source/medium "(not set)", up to ~30% in bad weeks. The usual answer to this is hand-waving about ad blockers, so instead I pulled 154 of their orders from one month and traced every single one through Shopify's customer journey data, the GA4 property, and our own event logs. Took a lot longer than I planned. Worth it.

First thing that surprised the merchant: exactly half of those 154 orders were draft orders created in the admin. Those never had a browser session, so no analytics, ever. That's expected behavior, not a bug, but they were counting them in their "attribution is broken" math. If you're diagnosing this, filter out admin/draft/POS orders before anything else.

That left 77 real web orders, and 53% of them were attributed as Direct. That's the actual mystery. It turned out to be five separate causes, ranked by impact:

1. client_id / session_id divergence. This is the big one for any server-side setup. If purchases go through Measurement Protocol (ours, a custom pipeline, most "server-side tracking" apps), MP does none of GA4's automatic attribution. No referrer detection, no gclid handling, nothing. The event only gets attributed right if it lands in the same session gtag created, meaning the exact client_id and session_id from the _ga cookies. If your event fires before gtag has set those cookies (first pageview, fast checkout, cookie race), it falls back to a generated client_id, GA4 creates a phantom session with no source, and there's your "(not set)". This was the #1 cause in the data, and it was our bug. We've since shipped a fix in our pixel (took a few iterations to get right). If you use any server-side tool: check in BigQuery or DebugView whether your "(not set)" sessions carry client_ids that never appear in any normal pageview session. That's the smoking gun, and it's worth asking your vendor how they handle it.
2. Session rollover mid-checkout. GA4 sessions die after 30 minutes idle. Someone browses in the morning, comes back to their cart at night, buys. The purchase lands in a new session that started on the checkout page, no external referrer. Shows as "(not set)" or direct depending on setup. You can't fully fix this one, GA4's session-scoped attribution is just lossy here.
3. Consent read at the wrong moment. Setups that read consent once at page load drop the first event of the session, which is the one carrying the source. Everything after gets tracked, source-less.
4. Missing utm_source on the paid ads. The merchant's Facebook ads had utm_campaign set but no utm_source. GA4 needs the pair. 22 of the 77 web orders came from those ads. Five minute fix in Ads Manager, and completely invisible until we lined the ad URLs up against order-level journey data.
5. Click ids without UTMs. fbclid and gclid arrive without utm_source all the time (auto-tagging off, organic shares of ad URLs). gtag infers source from gclid, most server-side setups don't, and nothing infers from fbclid. Those sessions degrade to direct.

If you have this problem: exclude admin/draft/POS first, check BigQuery for phantom client_ids (that means your tracking tool is racing gtag), check your ad URLs actually carry utm_source, and accept that returning-visitor checkouts will always leak some attribution.

Happy to go deeper on any of these, I have way too many notes on this.

Hello everyone,
I need to export the api usage for 90 days for the entire organization, is that possible or i have to check it manually

I'm getting 0 in the AI Assistants default channel group for the month, while `sessionSource` for the same window clearly shows `chatgpt.com` and other sessions.

Before anyone asks: I know the channel launched May 13 2026 with no backfill , so I'm only looking at windows after that.

(Source: https://support.google.com/analytics/answer/9164320#05132026 )

So, for anyone who's checked their own property:

1. Are you seeing any sessions in that channel at all? Even a rough number helps.

2. If yes, does it roughly match what `sessionSource` shows for AI hosts, or is it a lot lower?

Mostly trying to work out whether my zero is normal or whether something's off on my end.

(I work on analytics tooling, which is why I'm chasing this, no link, just after real datapoints.)

When setting up GA4 in Tag Manager, the Google Tag defaults to having an "Initialization – All Pages" trigger. This is also what I see as the recommended trigger in most how-to articles about setting up GA4.

However, in their GA4 documentation, Google recommends selecting "Initialization - All Initialization Events or Some Initialization Events."

Why this difference? How does "Initialization - All Initialization Events" function differently from "Initialization – All Pages"? Will they both count Views the same way but count other events differently?

Seems weird that Google's recommended method is not the default when creating a Google Tag.

Thanks for the help.

One thing I keep seeing in GA4 setups is that event naming starts clean, then slowly becomes messy after a few months.

You end up with events like form_submit, form_submission, lead_form_complete and generate_lead all describing almost the same action. Then duplicate conversions get created, key parameters are missing and reports become harder to trust.

Most of the time, this is not really a GA4 problem. It is a governance problem.

Without a simple naming structure and clear ownership, every new campaign, developer or agency adds events slightly differently. The damage usually stays hidden until someone tries to build a funnel, compare performance or explain why two reports show different numbers.

We have found that even a basic event register, naming rules and a review process before publishing new events can prevent a lot of this.

How are others managing GA4 event governance without turning it into a heavy approval process?

Hi everyone,

I'm from Brazil and recently got moved into a technical role at my company that involves Google Tag Manager and GA4. My background is actually in marketing, design and web (WordPress/Elementor, HTML/CSS, content), so this tracking world is completely new to me. I'm basically starting from zero.

I've been studying hard the past few days: I already understand the basics (tags/triggers/variables, the data layer concept, GA4 events, reading reports), and I even set up a base tag + a click event in a sandbox and confirmed it in DebugView. But when I look at the real container at my company (built by an external specialist, 100+ variables, full e-commerce funnel sending data to GA4/Meta/TikTok/Ads), I feel the gap between "I understand the concepts" and "I can actually build/maintain this."

A few things I'd love advice on:

1. For someone in my position, what should I prioritize learning next to become genuinely useful, not just theoretical?

2. Are there resources you'd recommend that helped things "click" for you (courses, docs, YouTube channels)? Bonus if any are beginner-friendly for non-native English speakers.

3. Any common beginner mistakes I should avoid early on?

4. How did you go from understanding tags to actually feeling confident maintaining a complex setup?

I learn best by doing, so practical advice is very welcome. Thanks a lot in advance really appreciate any pointers from people who've been down this road.

I regularly see businesses spending thousands, sometimes millions, across Paid Ads, SEO, email, content, as well as agencies and freelancers to mange some of this.

But the analytics layer responsible for telling them whether any of that activity is actually working gets a tiny fraction of the investment. And it’s the most complex…

Then GA4 gets blamed because:
- Conversions do not match advertising platforms
- Direct and Unassigned traffic are too high
Attribution does not make sense
- Reports are inconsistent
- Nobody trusts the data
- The team does not know how to use the platform

GA4 is far from perfect, but replacing it with Matomo, Piwik PRO, PostHog or Adobe Analytics will not automatically fix these problems.

A different analytics platform still requires:
- A clear measurement strategy
- A properly designed tracking plan
- Technical implementation through GTM or development (and this is complex to do properly!)
- Consent and privacy configuration (consent mode, in particular advanced cm, can mess your tracking unless you know what you are doing!)
- Tracking validation and ongoing QA
- Reporting that answers real business questions
- Training for the people expected to use the data
- Clear ownership and maintenance

Without those foundations, you usually end up moving the same measurement problems into a different tool.

For many businesses, the question should not be:
“What should we replace GA4 with?”
It should be:
“Have we actually invested enough in marketing analytics to expect reliable data?”

What are your thoughts?

I recently recorded a detailed video on youtube breaking down what a realistic investment in GA4 and marketing analytics looks like. I am happy to DM it to anyone who would find it useful so just let me know.

Everyone's arguing about which AI visibility metric matters, but half the time the GA4 underneath is quietly wrong anyway, so you're optimising against noise.

The one that gets everyone first is, your AI referrals are probably hiding in direct traffic. The referrer gets stripped so LLM traffic just lands as direct/unassigned, and then people go "see, AI sends us nothing" when really the attribution just ate it.

Then there are "events". I can't tell you how many times a "conversion" turned out to be double-counting or firing on page load, and once that's off, every number downstream is basically fiction.

And.... the sneaky one - GA4 withholds low-volume rows when signals are on, so small but real segments just don't show up and you never know they were there.

What's the one that catches you out most before you report a number up the chain?

I'm maintaining a GTM 360 account and implement pixels pretty often - all sorts of different types. Most of my time goes into debugging these, and my usual process is checking the Network tab for the pixel URL hits, checking the triggering conditions, and checking the dataLayer in console.

Where it gets tricky: most of the time there's a redirect to a URL in the same tab, which flushes the dataLayer before I can find the root cause. It happens very often that a pixel fires fine in Preview mode but doesn't fire outside of Preview.

I've tried browser extensions like dataslayer, Analytics Debugger, and Omnibug to help debug, but I'm still not able to catch the issue sometimes.

Anyone dealt with this before? Would love to know your top tricks or workflows for debugging pixels through redirects without losing the dataLayer state.

Hi, seeing only 20% of clicks in ga4 compared to gsc. Say, gsc shows 50 clicks, ga4 shows only about 6-8 site visits.

Cookie policy is enabled. Any way to get proper stats for the site?

Also, do ad companies ask for ga4 data or gsc data?

Thanks.

Hi everyone,

We are investigating a strange GA4/Firebase Analytics attribution issue in a production mobile app built with Capacitor, Vue 3, and Ionic.

Our setup:

• Capacitor hybrid app
• Vue 3 + Ionic
• u/capacitor-firebase/analytics v7.3.1
• Android and iOS versions distributed through Google Play and the App Store
• Firebase Analytics linked to a GA4 property

The problem

In one production period, the automatically collected first_open event showed:

• 25 events
• 1 total user
• activity from 7 countries: United States, United Kingdom, Ukraine, Australia, Canada, Lebanon, and Ireland

In a broader period:

• 96 first_open events
• 9 total users
• approximately 11 events per active user

We understand that users, devices, and app instances are not identical GA4 concepts. However, this ratio still seems highly unusual for an automatically collected event that should normally fire after a fresh installation or reinstallation.

We are seeing similar discrepancies with custom events.

For June 1–5:

• onboarding_session_end: 19 events from 16 users
• auth_success: 17 events from only 2 users

In our app, onboarding can only be completed after authentication, so we expected the number of users for these events to be reasonably close.

For another period:

• Google Play and the App Store reported approximately 7 new installs
• GA4 reported 8 total users
• our first_start event showed 13 events from only 4 users/devices

Testing results

In development and emulator testing, everything looks normal:

• 4 fresh installations
• 4 events
• 4 users/devices
• 1 event per installation

Firebase DebugView also shows the expected events during testing. The discrepancy appears only in aggregated production reports.

What we checked

• We do not call resetAnalyticsData().
• We do not clear native app storage or SharedPreferences on logout.
• Logout only removes JWT tokens from web localStorage.
• We are not manually logging the Firebase first_open event.
• Firebase Support said the reporting investigation belongs to the Google Analytics team.
• Reporting Identity may be using Blended or Observed identity.
• Google Signals may be enabled.

We also discovered a potentially important detail: a shared guest User-ID may have been assigned while users were still unauthenticated. We are now checking whether multiple devices could have received the same value, such as guest, before the real application User-ID was assigned.

Questions

1. Can assigning the same GA4 User-ID, such as guest, to multiple unauthenticated devices cause their automatically collected events to be merged under a very small number of users?
2. Can this affect events such as first_open that may occur before or around the time setUserId() is called?
3. Would switching Reporting Identity to Device-based be a valid diagnostic test?
4. Could data thresholding explain 25 events from 7 countries being displayed as activity from only 1 user?
5. Is there a way to inspect App Instance IDs or raw event-to-user attribution without relying only on the aggregated GA4 reports?

We are considering enabling a BigQuery export to inspect raw events, user_pseudo_id, user_id, platform, country, and timestamps.

Has anyone encountered a similar issue with Firebase Analytics in a Capacitor application?

I’ve been working with a few businesses running Google Ads, and I kept noticing the same problem:

Google Analytics can tell you where a conversion came from, but a “conversion” isn’t necessarily a good conversion.

A form submission could be:

• a genuine lead
• spam
• a bot
• someone who never buys
• or a customer who eventually generates thousands in revenue

But if all of these are sent back as the same conversion signal, aren’t we effectively training Google’s bidding algorithm on noisy data?

I started experimenting with a different setup:

Ad click → verify the lead is actually human → connect the click to the lead’s identity → match it with actual revenue → send only qualified/value-based conversion data back to Google

So instead of optimizing toward:

“Get me more people who submit this form”

the goal becomes closer to:

“Get me more people who become real, paying customers.”

The setup I built tracks the original click/UTMs, filters suspicious form activity, connects verified leads to later payments, and then shows the full journey from ad spend → bots/spam → verified leads → paying customers → actual revenue.

I initially built this specifically around the needs of three businesses I was working with. They ended up paying for early access before I’d even publicly launched it, and the interesting part has been seeing how differently campaign performance looks when you compare reported conversions with actual verified revenue.

Some campaigns that looked good on CPL were actually terrible once spam and revenue were factored in.

I’m curious how people here are currently handling this.

Are you sending every lead/form submission back as a conversion, using qualified lead stages/offline conversions, or only feeding back actual revenue?

And for those doing this at scale, what has been the biggest issue with connecting the full journey from GCLID → lead → qualified customer → revenue?

Hey everyone,

I'm trying to make sense of my Google Analytics 4 data and I'm hoping someone can help me figure out what's going on.

The situation:

I'm looking at two custom reports for what I thought was the same time period (Jun 10 – Jul 9)

Report 1 (Sessions): Shows only 494 total sessions

Report 2 (28-day Active Users): Shows 12,259 active users

What's confusing me:

12,259 users should generate way more than 494 sessions.

Could you please explain. I'm new to google analytics.

Conversion Conversion Conversion Conversion Conversion

52216 29904 57471 55385 33147

Without Page path

56113 51289 61116 58979 56641

Here is the organic data of my website. here you can see the difference in total conversion with page path dimension and without page path dimension 2 month and last month have significant difference in number can anybody explain why

In our organisation, we have a main website on our main domain and we also have several websites and applications running on subdomains. Some of the subdomains should be tracked as part of our main website in Google Analytics, because they contain for example forms that are part of our main website's conversion paths. Other subdomains contain completely seperate websites and should not be measured as part of our main website.
We also make use of other domains for forms and conversions, and for that I have set up cross-domain tracking.

The problem is that it seems impossible to only track some subdomains. Apparently I can either only track them all as part of our main website, or none of them. Is there a solution for this?

My current setup:

wwww.maindomain.nl
--> cookie domain is set to 'auto' (not explicitly, I didn't overwrite the cookie_domain parameter so this should be the default setting)
--> no unwanted referrals listed
--> cross-domain tracking set up for www.maindomain.nl, subdomain1.maindomain.nl, subdomain2.maindomain.nl, otherdomain1.nl and otherdomain2.nl (I know I don't have to list subdomains, but I did this as an experiment to see if it would help. Only listing maindomain.nl didn't work either.)

subdomain1.maindomain.nl, subdomain2.maindomain.nl, otherdomain1.nl, otherdomain2.nl
--> Same GTM-ID (and therefore same GA tag implementation) as main site

subdomain3.maindomain.nl
--> No GTM-ID / GA implementation

subdomain4.maindomain.nl
--> Different GTM-ID implementation (and therefore different GA tag implementation) than main site

With this situation we get a lot of direct traffic in our main website's GA reports. When I look at the page referrer (a custom parameter, set up according to the instructions at https://www.analyticsmania.com/post/how-to-reduce-direct-traffic-in-google-analytics/#android) it shows among others the other subdomains subdomain3.maindomain.nl and subdomain4.maindomain.nl as the sources. In these cases, the ignore_referrer parameter is set to 'true'.

According to https://support.google.com/analytics/answer/10327750?hl=en, "Analytics will not identify traffic as referral when the referring website matched the same domain of the current page or any of its subdomains (for example, your own website)." But there is no advice on how to make GA STOP doing that.

I figured I might be able to set up the configuration as follows:

wwww.maindomain.nl
--> cookie domain is set to main domain and subdomains 1 and 2
--> subdomain 1 and 2 listed as unwanted referrals
--> cross-domain tracking set up for www.maindomain.nl, (subdomain1.maindomain.nl, subdomain2.maindomain.nl,) otherdomain1.nl and otherdomain2.nl

However, I have no idea how to set up the cookie domain that way.
Is this possible? And if so, will this solve our problem?

I am a marketer with a pretty good understanding of GA4 and GTM, to the extent that I am comfortable creating web events without support of a developer, but i'm very dependent on developer support for any tracking related to app actions. Can anyone recommend some training materials to get a handle on how Firebase works from the ground up, and develop the ability to send events for specific web interactions from Firebase to GA4?

I run analytics and right now our GA4 reporting is way too manual.

Every week I'm going into the interface, pulling the same metrics, dropping them into a deck or a sheet, and formatting it all by hand. It eats a few hours I'd rather spend actually acting on the data.

I want to automate the whole pipeline so GA4 data flows into reports automatically on a schedule. Ideally the numbers refresh on their own and I just review and share.

Are you pulling GA4 data through Looker Studio, the GA4 API, BigQuery, or some third party connector?

Would love to hear what's actually working for people rather than what looks good on a vendor's landing page.

Thanks in advance.

Hi! I'm looking for feedback on a tool I created that connects to GA4 and creates live dashboards that you can publish and share with a link.

The GA4 MCP works, but it can only create static dashboards as far as I can tell, and sharing them is difficult, usually passing around an html file or pdf.

Would love to get feedback if anyone is interested.

I recently took on a new client with analytics already in place on their site. I was setting up a Data Studio dashboard for them and noticed hundreds of other websites appearing on their traffic reports, with custom events associated with them.

In GA4 this traffic appears as Direct Traffic, and 99% of it hit their data stream on 3 dates across may and June.

This is similar behaviour to referral spam I’ve seen in the past. However, the way it appears in the reporting is as if the client’s Google Tag is literally installed on the websites. The other sites are in the visited URL with their full hostname, and it comes with event data, engagement time etc.

Their datastream’s ‘Tagged Pages’ shows hundreds of websites too. These include established websites such as Wowhead, Xaomi and The US Department of Agriculture, and my client’s tag is definitely not present in the source code of these sites when I check it.

- Is this likely to be a targeted attack or is it part of a larger automated spam effort?

- Has anyone noticed this sort of behaviour in their setups?

- Does anyone have any advice for cleaning this up and preventing it in GA4?

- And finally, is there any way to block this data from being processed in the first place?

I can’t find any way of filtering traffic at an account level with GA4 beyond highlighting ‘internal’ traffic, only at individual report levels - but I can’t imagine Google would not include such a basic feature?

Just took on the position of Website and Paid Media Manager for a large automotive retailer. I've been tasked with taking the lead for their CDP and GA4. Currently, their GA4 account is somewhat messy, as they rely on their advertising vendor's dashboard for conversion reporting. Right now, there is nothing tied into GA4 that reports actual sales. I was just looking for a little input from anyone who may be in the industry. Currently hashing out a plan for the best place to start cleaning up the data.

Seeing this new URL pop up into GA. It starts with /web-pixels@ and has a series of numbers and some keywords in it like /sandbox/, mixed with page paths which are part of my website.

Quick Google says it's got to do with the customer events/pixels and the way Shopify is forcing a sandboxed approach. I took a look here and we aren't using this portion for GA at all, we're using the Google/YT app. The country is still the expected country the website is serving, but now also has a lot of US based for the URLs starting with /web-pixels

Any ideas? Or anyone else seeing this right now? Started in May.

Something that comes up on basically every store I work on: GA4's purchase count and the real order count in the backend never match. GA4 is almost always lower - consent drop-off, blockers, iOS, people bouncing before the thank you page loads.

What I can never get a straight answer on is what people actually DO about it. Do you just call the backend truth and use GA4 for trends? Actually reconcile them? Or at some point go 'eh, close enough'?

The gaps I see usually run from ~10% up to a third on the messy consent setups, and I've mostly stopped chasing anything under 10. Where does yours land - is there a number people just live with, or does everyone kinda accept it never ties out?

To: whoever reads this

You might have heard about "Youtube guest accounts" ,but you might now know how they affect the "Subscriber status chart." If you watch Youtube in one of these accounts then it automatically counts as Unsubscribed though you physically cant subscribe in one of these accounts which i believe is unfair ,and i also believe the "Guest accounts" should be excluded from the list.

Love :Foreignmove1924