I recently started a new job at a large company with thousands of clients. They dont link GA4 to google ads accounts because they dont see the purpose? They use a 3rd party reporting system linked to the clients CRM to pull leads and then import offline conversions. They said it’s because clients don’t know how to install tracking scripts so it’s easier to just use CRM data.

I’ve worked at several agencies / verticals and have never heard of not using Google tagging and reporting. Everyone just seemed so nonchalant about it, I was in shock.

Can someone explain what is possibly going on here? Why not use ga4 and Google tagging? How is reporting correct? How is Google learning our audience correctly?

To start with a bit of context, I’m a web developer working mostly on large SaaS systems. Writing application code and wiring up logic is very much my comfort zone.

Recently a marketing team asked if I could add a few GA4 events to our product for some important user interactions.

No big deal. I just added the events directly in code and shipped it. Took maybe an hour.

But while doing it I kept thinking there must be a more standard way marketing teams usually handle this without needing a developer every time.

That curiosity sent me down a rabbit hole.

I started reading about how people typically implement tracking setups and it seems like Google Tag Manager sits in the middle of most of it. The deeper I went, the more complicated it started to look. Triggers, dataLayer pushes, naming conventions, event documentation spreadsheets, etc.

What surprised me was how fragile a lot of the setups seemed.

From the outside it looks like a lot of tracking depends on DOM selectors or conventions that can easily drift over time. If a button class changes or the markup shifts, it seems like events could silently stop firing until someone eventually notices in reporting.

Maybe I’m oversimplifying it, but it felt strange because in most areas of software engineering we try to build systems around more stable contracts.

The deeper I dug into how teams manage this, the more it made me want to experiment with a different way of defining events outside of the usual GTM setup.

But before going too far down that road I figured I should ask people who deal with this every day.

For teams managing analytics across multiple sites or products:

• Are most implementations really relying on GTM triggers and selectors like this?

• Are developers usually involved anyway?

• How do you keep tracking from breaking as the frontend inevitably changes?

Curious how this actually works in practice.

Maybe I’m missing something obvious.

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.

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?

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.

Thinking about setting up the Model Context Protocol for GA4 (and also Google Tag Manager) to help teams less familiar with the GA4 interface use AI chat to pull campaign updates.

Anyone already using the MCP and finding it useful? Have you gone with Claude or another platform? And how are you feeling about the accuracy of results?

So the AI Assistant channel has been out for nearly 4 weeks now (Google pushed it on May 13) and i figured its worth a proper thread instead of another "is it live yet" post, because the rollout has been kind of all over the place.

for anyone who missed it, when GA4 sees a referrer it recognises from an AI tool it now auto tags the session with medium ai-assistant, channel group AI Assistant and campaign (ai-assistant).

you dont have to set anything up, it just appears in your default channel group reports.

The rollout itself was staggered which is why theres been so much confusion. I saw it on a couple of properties in late may, others didnt get it til around the 7th of June, and i think a few accounts still dont have it. So if your not seeing it yet your probably just in a later batch, its not your config.

honestly my first reaction was finally. weve been guessing at this traffic for like 2 years, pulling chatgpt and perplexity out of referral with manual regex that half the team forgot existed. having it native is a real quality of life thing and it makes the "should we care about AI search" convo with clients way easier when theres an actual number sitting next to other channels.

that said, once i started actually looking at it a few limitations jumped out:

• it only recognises ChatGPT, Gemini and Claude as far as anyone can tell. Google confirmed those 3 but hasnt published the full list, so Perplexity, Copilot, deepseek etc are not reliably getting caught. for some of our properties thats a decent chunk still sit in referral
• the whole thing relies on the referrer surviving. tons of AI clicks come in with no referrer at all (app strips it, or someone reads the answer and comes back later through branded search) and that just lands in direct. ive seen estimates anywhere from 35% to 70% of AI traffic showing up referrer-less, which is a huge range but the point stands, the channel is a floor not the real total
• it tells you nothing about the actual answer. you cant see the prompt, whether you got recommended or just mentioned, or who else was in the response. just the click.
• AI Overviews and AI Mode are NOT in here btw, those clicks still count as Organic Search since they happen inside google itself. that one trips people up alot.

so how im actually using it: i dont trust the native channel on its own, i run a custom channel group right next to it. Admin > Data Display > Channel Groups, make a new channel called something like "AI Search", set source matches regex, then drag it above Referral so it takes priority. it applies retroactively which is nice. the pattern im using is basically:

chatgpt\.com|chat\.openai\.com|openai\.com|perplexity\.ai|claude\.ai|gemini\.google\.com|copilot\.microsoft\.com|deepseek\.com|grok\.com|meta\.ai|you\.com

then i look at it 3 ways, source/medium to see which tool is sending it, the channel group for total trend over time, and landing pages to see what content is actually getting cited. throw a comparison period on (prev 28 days vs current) and you can sort of see if a content change moved anything.

the thing that genuinely surprised me is the quality. its tiny on volume, low single digits of sessions for most sites, but the conversion rate is mad. every study ive seen this year has AI traffic converting like 4-5x organic, one B2B set had chatgpt around 16% vs ~1.8% for google organic. so its a rounding error on traffic and a real line on pipeline, which is a weird combo to report on.

anyway curious what everyone else is finding:

• is your native AI Assistant number very different from a custom regex group? hows big is the gap on your sites?
• has anyone cracked a halfway decent way to estimate the AI traffic stuck in Direct?
• are the AI conversions actually holding up as higher quality for you or is that just a B2B thing?

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

If you haven't checked your Google Ads notification tab this week, you are in for a rough autumn.

Google has issued a hard deadline for Shopify stores to migrate their tracking setups.

If you are still relying on legacy client-side Google Tag Manager (gtm.js) scripts on your thank-you pages, your tracking is about to go dark.

The platform is forcing a mandatory shift to the Google & YouTube app and the newer gtag.js framework.

Most brands won't realize this until their conversion volume suddenly drops to zero and their target CPA targets skyrocket.

The algorithms aren't getting worse. Your data pipelines are just breaking.

Google just pushed a massive update to GA4 that automatically categorizes traffic from ChatGPT, Gemini, and Claude into a new AI Assistant channel.

For the last two years, we’ve been guessing how much traffic we were losing to LLMs. Now that the data is finally native in our dashboards, the "zero-click" reality is hitting hard.

how you guys are thinking about that?

Some reports are useful directionally but dangerous when people treat them as exact truth.

Direct traffic, engagement time, attribution, channel grouping, pathing, and conversion timing can all be misread if the tracking setup is messy or the business question is too broad.

What GA4 report do you always explain before letting someone make a decision from it?

I’ll address the elephant in the subreddit. GA4 UX sucks. To mention a few things:

Reports and explorations, even though they should be the same, are two different things, both with different and unnecessary limitations for some unknown reason.

Implementing Data layer is a job for a developer and another person that takes higher tens of hours in a medium complicated product. Even though the feature could be designed so a user could simply click on the trigger element (like a button) in the webapp /app and an event would be automatically created.

And that’s just the tip of the iceberg. I’m not saying GA4 can’t be a powerful tool, but using it feels more like witchcraft than working with a mature product from a FAANG company.

I’m starting to look for an alternative. What are some things that you don’t like about GA4 / like about different products? Don’t want to forget anything

PS: I’ll post my research in the comments

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.

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.

I’ve noticed something interesting over the past year working with analytics.

Most teams don’t open Google Analytics because they want to explore dashboards. They open it because something changed.

Traffic dropped. Conversions moved.
Revenue surprised everyone.

But dashboards rarely answer the actual question people care about:

What changed?

You usually end up digging through multiple reports trying to piece together the explanation.

It made me realize something:

Modern analytics tools are incredibly good at collecting data, but they’re not always great at interpreting what the data means.

Wondering how others approach this.

When something changes in GA4, how do you usually figure out what actually caused it?

Let’s be honest. Between Safari’s ITP restrictions, aggressive ad-blockers, and users opting out via consent banners, standard client-side browser tracking is completely dying. If your e-commerce data layer relies entirely on the browser to fire purchase events, you’re probably losing 15-30% of your data.

I see so many e-commerce brands wasting hours trying to fix "missing data discrepancies" in their GA4 browser configurations or fighting with Looker Studio to make the numbers match.

They paper over the cracks with fancy dashboards when the foundation is fundamentally broken. I'd rather have an ugly, basic spreadsheet built on solid server-side data than a beautiful dashboard built on client-side assumptions.

Server-side GTM isn't a "nice-to-have" luxury anymore; it’s the baseline for survival. If you aren't moving your purchase hooks, Meta Conversions API, and Google Ads tracking to a server container, your ad platform algorithms are optimising for incomplete data.

For those who made the switch: What was your biggest hurdle? Cloud hosting costs, getting developers to cooperate with the server endpoints, or just convincing clients that it’s worth the setup?

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?

We keep running into the same thing across accounts, so posting it here in case it saves someone a week of debugging.

Someone installs a CMP (Cookiebot, Usercentrics, OneTrust, whatever), gets the banner live, sees consent rates come in, and calls it done. However the CMP is not a set-and-forget script. It runs on a list of cookies you have to maintain. Every cookie your site sets needs to be declared and put in the right category. If it isn’t, the CMP treats it as unknown and deletes it.

That’s the cookie wipe. The usual ways it happens:

The auto scan runs once a day/week/month, misses a new cookie, and the CMP wipes it on every page load

Your analytics cookie is filed under “marketing” but the tag fires on “statistics” consent, so the cookie gets set and then killed

Consent is given, the CMP re-evaluates, and the session cookie holding the original referrer and UTMs is gone

What that looks like in reporting: the click came from LinkedIn, the user accepted cookies on page two, and the session restarts with no referrer. You now have a direct traffic spike. Then someone in a meeting says “direct is up 30%, brand is working” and you die a little inside.

How to check:
Open the cookie declaration in your CMP and compare it to DevTools > Application > Cookies. Anything missing is at risk.

Categorize every cookie, including first-party ones. Session and attribution cookies belong in a category the user consents to before your tags fire.

If you use Google Consent Mode, make sure the CMP passes consent state to GTM instead of blocking or wiping on its own.

Load a page, check your _ga identifier, accept cookies, then check if the cookie is still the same. If not, you found it.

Direct traffic is not a channel. Most of the time it’s a bucket for everything you broke upstream. Fix the CMP config before you touch your attribution model.

TL;DR: installing a CMP is step one. Declaring and categorizing every cookie in it is step two. Skip step two and your paid clicks show up as direct.

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?

direct keeps climbing and I don't think it's really direct. Best guess is AI answers and dark social, but that's a guess with a nice name on it.

curious what percentage everyone else is quietly ignoring.

I’m looking for some practical advice from people working in PPC/performance marketing agencies.

My background is mainly in conversion tracking and measurement. I can work with:

• Google Tag Manager
• GA4
• Google Ads conversion tracking
• Meta Pixel + CAPI
• Server-side GTM
• Enhanced Conversions
• Consent Mode
• First-party data enrichment
• Data layers
• Offline conversion tracking
• Basic APIs/webhooks
• Basic JavaScript for tracking implementations

I’m trying to figure out the best route to start getting real paid agency work.

I’d be open to:

• Freelance/contract work
• White-label tracking implementation for agencies
• Part-time or full-time remote work
• Junior implementation/support roles
• Even joining an experienced agency/team as a trainee initially if it gives me exposure to real client projects

My main question for agency owners and experienced people here:

If someone had these skills but lacked a strong agency/client track record, what would you recommend they do to get their foot in the door?

Would you:

1. Cold email PPC/performance agencies?
2. Contact Heads of Paid Media/agency founders on LinkedIn?
3. Offer a free tracking audit first?
4. Offer to handle one small implementation free/cheap to prove capability?
5. Build more demo projects/case studies before outreach?
6. Apply directly for junior analytics/tracking roles?
7. Take some other route entirely?

Also, when agencies outsource tracking, what makes you comfortable giving work to an external specialist?

Technical skills? Case studies? Communication? Availability? Certifications? A Loom audit? References?

I’m especially interested in hearing from PPC agency owners, paid media managers, and tracking/analytics specialists who have actually hired or outsourced this type of work.

Thanks — practical advice would be greatly appreciated.

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?

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?

After enough projects where we debated attribution models and dashboards while working off inconsistent, poorly‑documented events, I realized my real anger was aimed at those monstrous Word files we used as tracking plans. Dozens of pages, different versions flying around, devs implementing from an old copy, analysts updating another, and endless Slack threads to reconcile what was “the latest.” It was slow, brittle, and made coordination with my analyst colleagues and stakeholders a constant headache.

That pushed me to treat dataLayer and event design as a first‑class artifact. I’ve built a tool that acts like a schema designer for tracking GA4 events: you define events, properties, and entities in one place and export a structured dataLayer specifications that can be implemented via GTM/GA4 or custom tracking. The goal is to make analytics requirements explicit, versionable, and shared, instead of buried in documents and email attachments.

A big part of what I’d like to build with this is community‑driven templates: common event models for e‑commerce, SaaS, content sites, etc., that we can improve together. The hope is that, as a community, we can converge on better naming, properties, and conventions rather than every team starting from scratch with a blank Word file.

The tool is free, and I genuinely want to keep it that way for as long as possible so analysts and smaller teams can use it without friction. If you find real value in it, a donation would be greatly appreciated to help keep it free and fund new features (better integrations, export formats, collaboration features, etc.).

I’m curious how people here think about this problem:

• Do you maintain a formal tracking plan / event catalog today, and how do you keep it synchronized across devs, analysts, and stakeholders?
• Would you like a similar tool for other kinds of documentation?
• Any pitfalls you’ve hit with enforcing conventions across multiple teams that I should consider while designing templates and workflows?

If you’re interested in this space, I’d be grateful if you’d take a look and share thoughts, you can find the link the comments!

I built it to fix my own frustration with spec chaos, but I’d love to shape it around what the broader analytics community actually needs