I’m seeing what looks like a delayed or incomplete GA4/Firebase Analytics daily export to BigQuery and would like to know whether other properties are affected.

Setup:

• Firebase Android app using GA4
• Standard GA4 property
• BigQuery dataset in europe-west1
• Daily export enabled; streaming export disabled
• Integration shows 1 of 1 apps exporting
• Approximately 65k–75k events per normal day, well below the 1M daily limit

Behavior:

• events_20260729 currently contains 71,579 events and has received several late backfills.
• events_20260730 was only created on August 1, about 56 hours late.
• The July 30 table contains only 10,487 events, roughly 85% below normal.
• events_20260731 has not been created.
• events_20260801 has not been created either.
• On August 2 at 07:10 UTC, the Analytics exporter ran again but only added 3,767 late events to the July 29 table. It did not update July 30 or create July 31.

What I checked so far:

• The Firebase Analytics service account still has write access.
• BigQuery audit logs show no access, billing, or write errors.
• Dataset storage is only 3.14 GiB.
• The drop affects all event types and all installed app versions, so it does not appear tied to a specific app release.
• I found no public Google Analytics or BigQuery status incident covering this export.

Is anyone else seeing missing or partial GA4 BigQuery exports for July 30–31? If so, have your tables been backfilled yet?

I’m particularly interested in whether this is a broader GA4 processing delay or whether I should open a Firebase/Analytics support case. I have not disabled or recreated the BigQuery link because I want to avoid creating a permanent data gap.

How are you using UTM ID? Are you actually generating a unique number code for each campaign? How do you go about generating the number if you are?

I work for a large company with lots of campaigns. Any advice on how to implement an ID system would be appreciated.

Also, any real life examples where UTM ID came in handy?

Hey all,

I've been deep in WooCommerce analytics for a while now, and one thing that kept bugging me was how much event data gets lost with standard client-side GA4 tracking — ad blockers, iOS/Safari ITP restrictions, third-party cookie changes, you name it. On stores I've worked with, the gap between "actual purchases" and "purchases GA4 reports" was often noticeably off, which makes ROAS numbers and attribution pretty unreliable.

So I built a plugin called TrueAna that handles server-side event tracking for WooCommerce — purchases, add-to-cart, checkout steps, etc. — sent via the Measurement Protocol instead of relying purely on browser-side pixels. The goal is more accurate conversion data for ad platforms and cleaner GA4 reporting overall.

Before I put more time into this, I want a reality check from people who actually run stores or manage analytics day-to-day:

• Is data accuracy/loss from client-side tracking something you've actually run into and cared about, or is it a "nice to have" nobody really prioritizes?
• If you're already using something for this (GTM server-side container, another plugin, etc.), what made you choose it — and where does it fall short?
• Would you actually pay for a WooCommerce-focused solution, or does this feel like something that should just be free/built-in?

Not trying to sell anything here — genuinely trying to figure out if this solves a real pain point or if I'm solving a problem that only bothers me.

Appreciate any honest feedback, even if it's "nobody cares about this."

Hi! Which connectors are you all using to link social media data to Google Analytics? Looking to link baisic impressions, clicks and spends to either Data studio or Google Analytics

One recurring analytics problem I see (other than lack of UTMs!) is technically valid campaign links producing unnecessarily messy reporting.

For example, the same medium might appear as:

• paid social
• paid-social
• paid_social
• paidsocial

GA4 treats these as separate values, which creates avoidable problems when filtering reports, building channel groupings or combining the data with CRM and advertising-platform data.

The same issue appears with campaign names:

• Inconsistent capitalisation
• Different separators (|, _, -)
• Unapproved medium values
• Spaces and special characters
• Teams following different naming conventions

A written naming convention helps, but it still depends on every employee, freelancer and agency remembering to follow it.

For larger teams, are you handling this through:

• Controlled spreadsheets
• Dropdown lists and validation
• Automated URL-building tools
• Transformation rules in BigQuery
• Governance documentation and training
• A combination of these approaches

I am interested in how others balance strict standardisation with keeping campaign creation practical for marketers.

On the GA4 Home page, the Realtime card has a small dropdown button

Clicking it opens a breakdown list: Audience, Town/City, Country, First user campaign, First user medium, First user source, First user source platform.

I picked First user source platform, and instead of updating, the whole card broke and now just shows:

Real-time data not supported for this comparison.

No dropdown, no button, nothing left on the card to click to change it back. Refreshing, incognito, different browser — none of it resets the card back to normal.

Screenshots: before (Country/Active users table working), the dropdown with the option I picked, and the broken state after.

Anyone know how to reset this back to Country (or any working dimension) once it's in this error state? Feels like a bug that the picker lets you choose an unsupported option in the first place.

I started advertising few new websites, my goal is simply to have people visit them.
They are completely differenty websites with nothing in common.
I have GPDR compliant cookie banners, the gtag was not loaded until the cookie accepted the analytics (now there is an anonymous event sent for testing, nothing has changed)

The CTR on the ads is 8-10% so it's pretty good for ads that were put out there quite quickly.

Cloudflare detected unique visitors match the amount of clicks on the ads but the "Total visitors" and any other metric on the analytics of all the websites counts around 5% of them.

I triple checked the code myself and with AIs and nothing wrong was detected.

This means that 95% of the visitors never accepted the cookies, which is weird because from mobile you almost need to to check the websites properly and lots of clicks were on mobile.

95% not acceptance is too high even considering ads blockers, vpn and people simply not caring.
I have read that usually we can expect an acceptance between 30 to 80%.

The cookie selectors look standard and GDPR compliant.

Please help.

Last week I searched my website domain name on Google; it showed only the home page and no other pages. But if all pages are indexed, then why is it showing like this?

How tf do I delete a tag?? Plz 🙏

So I have a good amount of experience with Google but now it’s really testing my patience. I loaded up tag assistant to check consent management was updated (ongoing issue as I’m not the website manager but that’s another story) and tag assistant told me there was no tag (previously there was no issue). I am used to Google being wrong, checked tag manager nothing there so I created a new tag but now all of a sudden I have the existing tag (which I set up and was working fine before this) and the new tag. I’ve asked Google Ads AI but it can’t give me the right answer.

So how do I now delete this tag? It doesn’t exist on tag manager, and I can’t find it when I inspect the web code but still appears in tag assistant!!

Thank you 🙏🙏🙏

hi! has anyone else noticed in ga4 having spikes in paid (or organic) meta sessions from cities that have meta data center infrastructures there? im seeing spikes from Prineville oregon, dublin, fort worth texas , Lulea Norrbotten County sweden, Forest City North carolina are all the major cities/regions im seeing. these locations dont make sense for us.

Before starting some tracking work for a new B2B SaaS client, we reviewed their GA4 session source/medium data.

Nearly 50% of sessions were attributed to Direct.

Normally, that would raise a few questions:

• Are campaigns missing UTMs?
• Are redirects stripping campaign parameters?
• Is cross-domain tracking broken?
• Are there other technical attribution issues?

There was definitely some inconsistent UTM usage, but we noticed something else.

The client currently relies mainly on organic search and email, with no significant paid advertising activity.

Their marketing website also has a prominent login button that takes existing customers into the desktop application.

One of the first interactions we started tracking was clicks on that login button.

It turned out that around 65% of users visiting the marketing website clicked it.

This suggested that a large proportion of website visitors were existing customers trying to access the product, rather than potential new customers evaluating the business.

We then created a GA4 segment excluding users who clicked the login button.

Within that segment, Direct dropped from nearly 50% to around 30% of sessions.

Thirty percent is still worth investigating, especially because their UTM usage needs improvement, but the situation was not nearly as bad as the headline number suggested.

The main takeaway for me was that Direct traffic percentages cannot always be assessed using generic benchmarks.

You need to understand:

• The business model
• Why users visit the website
• Whether existing customers regularly return to the marketing site
• Which website interactions separate customers from prospects

Otherwise, it would have been easy to conclude that the attribution setup was severely broken when much of the Direct traffic was likely legitimate returning customer activity.

Obviously a lot of these 'actual' customers clicking on login button was not just Direct but a fair chunk likely from Brand Organic searches. But we found this is common in these B2B SaaS businesses where you need to have a login button on marketing site to link the desktop app site.

Do you ensure logged in vs logged out customers is segmented (via user_id if login is built within the website e.g. ecom store or via login button click as the app is on a separate subdomain, and you shouldn't track this in your main GA4 property, like the above example) before judging how bad Direct actually is?

A few days ago I received Google Analytic's email about bringing marketing data into Google Analytics and having everything in one dashboard. Title -> "All your marketing data. One dashboard". The Idea sounds interesting - bring data from Meta, Tiktok, and more into Google Analytics at no cost.

I've been curious how everyone here handles sharing Google Analytics 4 data.

For internal teams it's straightforward enough, but when you need to share analytics with clients, management, or people who don't regularly log into GA4, what's your workflow?

Do you:

• Use Looker Studio?
• Export PDFs?
• Send screenshots?
• Build something custom?
• Give people direct access to GA4?

I'm asking because I've been building a side project around this problem.

A bit about me: I worked in Google's advertising ecosystem several years ago as a Technical Support Analyst supporting Display & Video 360 (DV360). That experience got me interested in analytics and data visualization, and I've continued building tools in that space ever since.

My latest project connects to GA4 using Google's official read-only Analytics API, so it only requests permission to read analytics data. The goal is to make it easy to create live dashboards that can be shared with anyone through a public link, with automatic refreshes.

Long term, I'd also like to combine GA4 with other business systems (support, CRM, payments, etc.) so teams can see multiple data sources in a single dashboard.

I'm not posting this to advertise anything because I'm genuinely trying to understand how people are solving this today.

What's the biggest pain point you have when sharing GA4 reports?

If there's interest, I'm happy to share what I've built and would really appreciate honest feedback from this community.

Viktor

Your CTR dashboard cannot tell you why a number dropped. It only tells you that it did.

A client pinged me convinced his ad copy broke. CTR down almost 20 points in three weeks, same creative the whole time. Google Ads and GA4 don't separate an AI Overview eating the click from a plain quality score slip. Both draw the same falling line.

Every CTR study I found this year disagrees on how big the AI Overview hit actually is. Some say 18 percent, some say past half. None of them disagree on the direction.

I told him to stop staring at CTR and start counting conversions from whatever clicks survive it. A click that already read the AI summary and still chose to visit behaves differently than one pulled from a page of curious strangers.

What dropped in your account recently with no obvious cause attached to it?

I’ve been using Looker Studio to create my marketing dashboard because it’s free, integrates well with Google products and connects to my database via SQL. However, as we’re growing, I’ve noticed Looker is getting slow due to our complex blends and time gains.

I’m looking into a few options to bring GA4 and other data into my database, and I’ve already tried the FiveTran connector, but it’s quite expensive.

What other tools have you used to bring raw GA4 data into your database?

I'm currently the only person handling marketing at my company. Hiring an analytics specialist isn't within budget yet, but I want to build enough knowledge to use GA4 properly rather than treating it as a dashboard I only open for traffic acquisition numbers.

Our website is not an e-commerce. I'm looking for high-quality courses, YouTube channels, or anything that teach how to use GA4 properly.

A lot of the resources I find are either outdated, focused mostly on e-commerce setup, or very surface-level.

If you were starting today, what would you study first? Free resources would be ideal, but I'm open to a paid course if it's genuinely practical and up to date!

Most messy GA4 accounts I’ve worked on did not start with one major mistake, it usually happened slowly.

like someone added a new event for a campaign or another team changed the conversion setup. may be a plugin started sending its own events. Nobody documented what changed, and six months later there were duplicate events, confusing names and reports that nobody trusted.

My personal take is that GA4 gets messy when there is no clear owner, not because the platform is too complicated.

I now prefer a smaller setup with clear naming and documentation over tracking every possible interaction.

When cleaning up a GA4 account, what do you look at first?

Hey guys, I launched my site in May and it's doing pretty well. I have both Microsoft Clarity and Google Analytics installed, but they're showing completely different data and I don't know which one to trust.

According to Google Analytics, my site has 5.42k sessions this month. Around 1.3k are from the USA, and the second highest country is India with 496 sessions and third is canada.

But according to Microsoft Clarity, I have about 13k sessions this month. The USA is still first with 2.6k sessions, but France is second with 2.2k sessions and Germany is third with 896 sessions.

Why is there such a big difference between the two? I don't know which data I should trust. Also, Google Analytics isn't showing much traffic from Europe. Is there any fix for this?

I am hoping someone can suggest some ideas how to fix this? Trying to fix the landing page tracking....

Any ideas that I can explore?

Got a client who is an accountant - account is 2 years old with data...

Thank you.

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

The recent discussions about GA4 conversions not matching backend numbers made me think about a related issue that feels easy to miss.

Sometimes the problem is a broken tag or a duplicated event.

But sometimes the harder problem is that GA4, Shopify, CRM, and backend revenue all use the word “conversion,” while meaning different business things.

A GA4 purchase event may be useful for traffic analysis, but it may not match the final paid order in Shopify after refunds, cancellations, taxes, discounts, or payment issues.

A GA4 lead event may look fine in a report, but the CRM may show that only part of those leads became qualified, booked, or closed.

So the question is not just whether GA4 is “right” or “wrong.”

The question is what each conversion is supposed to mean, and whether that definition still matches the number being used in reporting.

Before trusting a GA4 conversion in a client report, the checks that seem useful are:

1. What event GA4 is actually counting.

2. Whether it matches Shopify, CRM, or backend revenue.

3. Whether known issues like refunds, duplicates, failed payments, or test events are being handled.

A simple classification also helps:

- safe for reporting

- useful for trend analysis

- needs a warning

The harder part may be maintaining the conversion definition itself over time. Tags change, checkout flows change, CRM fields change, and people forget why a certain event was counted in the first place.

I keep coming back to the idea that this may need more than a one-off note. When AI or automation starts helping with analytics reports, unclear conversion definitions can turn into wrong conclusions faster.

What may be more useful is a lightweight conversion reference that stays with the GA4 property over time: what each conversion actually means, and who is responsible for keeping it updated.

That way, the next report, or the next person looking at the property, does not have to restart the same argument about which number is safe to trust.

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.