Is anyone working on Google data studio?

I've used MMM services from some of the big DTC vendors out there. Some of them are bundled with multi-touch attribution, and/or geo testing, and/or frequent rebuilds.

And I've built a few of the open-source models with Claude: Google Meridian, Robyn, and PyMC.

There's always a part where you spend a lot of time getting the data into the MMM. And then there's the part where you spend a lot of time getting data back out in a way that's fine-tuned, calibrated, passes health checks, and in general just makes sense.

Most MMMs include a chance to optimize media budgets according to their findings.

But they all end in the same way: deciding whether to act on the results. Whether to treat its outputs as true or not.

In my experience there's a little bit of internal selling you have to do when you have the MMM. It's a little big magic and untrustworthy to a lot of people.

So here is my question:

When you shop for an MMM and/or build your own... how do you use it when it's done?

• What bells and whistles do you expect it to have?
• What questions about your media are you trying to answer?
• Do you decide ahead of time how you're going to use it?
• Do you have to convince others of the results, does everyone on your team agree ahead of time on how you'll be using an MMM?
• How often do you want to rerun it?

Every month I explain to a client that GA4 says one thing, Google Ads says another and Meta says a third, and every month it sounds like an excuse.

I understand they are attributed differently. What I’m missing is a sense of what gap is normal and what gap means something is broken.

Is there a rule of thumb, or is this just permanently fuzzy?

I was hired as a digital insights specialist and I use GA4 as my main reporting platform, combined with behavioural insights from a heatmapping tool, and native reporting from a landing page platform. Essentially, all the data is uncentralized, none of this drives into one spot where I can create self serve dashboards for stakeholders. Every time someone requests updates on how pages are performing, I have to go pull data from multiple places, analyze, and dump it into a power point deck. It's extremely time consuming and not sustainable since we landing pages are frequently launched.

I've expressed concern about this to my boss, but she doesn't really have a solution. If I'm being honest, this sort of thing is slightly outside her wheelhouse. Reporting isn't the only part of my job, I'm involved in other projects and defining tracking/measurement requirements for any initiative we launch (not just pages but digital features as well). The rest of the team produces the page and then move onto the next thing once it's launched. I'm essentially involved in the entire process from start and beyond post-launch. These pages can be in market for months to years as well. So everything is just compounding now that I've been in the role for a year.

Another manager in our dept has come to me asking what kind of reporting cadence I can commit to and I don't know what to say. Having to manually report on all these things on top of all my other responsibilities seems way beyond my capacity. I'm already stretched thin. But considering what my role is, the manager is not out of line asking the question or wanting to define a cadence. I can tell he's aiming to get reporting out of me for every page, every two weeks, but I just don't have the rounds.

Im not sure what to do here. Its crazy that someone hired to do insights is claiming they're too busy to do just that, but it's true. Our data set up is so inefficient/messy, the manual work of gathering everything is extremely time consuming. I know I probably need to chat with my boss, but I'm really not expecting her to have any solutions. What would you next move be here? And what kind of answer do I give to this manager?

Talking all Google products. If you have a large team, how do you look after this? At the moment people get access through like a info@agency email. But how does this get filtered down to individual users? Are you sharing logins? Adding the entire team individually? Coming into an era where passkeys seem to be enforced. How do you manage this? Everyone will need access to take a peek and to cover off if someone is sick or on holidays.

I tried to see GMP settings to see if there was an organisation level of hierarchy but doesn't apply to Search Console. Maybe I'm looking in the wrong spot. One of the reasons why I like the way Meta uses a business manager type of access. Add the org/partner ID, filter down and give other employees access.

Hey everyone!

I run a clothing brand focused on retro/90s-inspired styles, including fitted T-shirts, hoodies, and bootcut jeans. My products are priced at around €40 for T-shirts, €60 for hoodies, and €80 for jeans.

I’m currently running Meta Ads campaigns optimized for purchases, mainly targeting Italy, since that’s where I’ve had the most sales in the past.

A while ago, I used an interest-testing strategy that worked reasonably well for me, although the results weren’t amazing. I created separate ad sets for different interests, gave each one time to collect data, paused the weaker performers, and then combined the better-performing interests into a new ad set with a larger budget. I also experimented with broader, AI-driven targeting.

Now I’m considering trying a similar approach again, but I’m unsure how best to structure it with the current Meta Ads algorithm and Advantage+ audience.

My initial idea is to start with around 6–7 separate interest-based ad sets, each with a daily budget of approximately $10–$15, using one interest per ad set. After collecting enough data, I’d evaluate their performance and decide what to do next.

However, I’m unsure which of these approaches makes the most sense:

Option 1: Keep the interest ad sets separate.
Let each one run as long as it continues generating conversions, and pause it when performance deteriorates. This would allow me to keep identifying which interests perform best individually.

Option 2: Combine the winning interests into one ad set.
After testing them separately, take the best-performing interests and combine them into a single ad set with a larger budget, hoping Meta can optimize delivery more efficiently with a bigger audience and more conversion data.

Option 3: Run both approaches in parallel.
Keep the individual interest ad sets running while creating an additional ad set containing all the promising interests combined. This would let me compare the results directly and see whether the combined audience performs better than the individual interests.

Option 4: Test broad/Advantage+ targeting alongside interest testing.
Run a broad or Advantage+ audience ad set at the same time and compare its performance against the interest-based approach.

I realize that running too many ad sets could fragment the budget and conversion data, so I’m wondering whether testing 6–7 interests separately is still worthwhile or whether I’d be better off consolidating things earlier.

My questions are:

• Does testing individual interests and then combining the winners still make sense in 2026?
• Is it better to keep successful interest ad sets running individually, combine the winning interests, or test both approaches in parallel?
• Would you run broad/Advantage+ targeting at the same time as interest testing?
• How much data or how many conversions would you want before deciding that an interest is a winner or should be paused?
• If an ad set stops generating conversions after performing well initially, would you pause it, reduce its budget, or let it continue?
• If you were launching a clothing brand campaign today, how would you structure the testing phase to get reliable results without spreading the budget too thin?

I’ve used separate interest testing in the past and had some decent results, which is why I’m considering it again. But Meta Ads has changed a lot, and I don’t want to stick with an old strategy if broad targeting and Advantage+ are now more effective.

I’d especially appreciate advice from people who have tested these approaches with fashion or e-commerce brands.

I’ve also tried running broad targeting on its own, but so far it hasn’t worked well for me. It brings traffic to my website at a relatively low cost per click, but I’m getting no add-to-carts, checkouts, or purchases. It feels like I’m spending money on visits without getting any meaningful results, which is why I’m considering going back to testing interests separately. I’m not sure whether the issue is the targeting itself, the creatives, or something else in the funnel.

Thanks!

Yes. Here’s a fictional example that makes it easier to see.

Month one:

Organic: 800 visits, 72 purchases = 9%
Paid: 200 visits, 2 purchases = 1%

Overall conversion: 74 purchases ÷ 1,000 visits = 7.4%.

Month two:

Organic: 200 visits, 18 purchases = 9%
Paid: 800 visits, 8 purchases = 1%

Overall conversion: 26 purchases ÷ 1,000 visits = 2.6%.

Neither channel’s conversion rate changed. The traffic mix did.

More visits came from the channel with the lower conversion rate, which pulled down the overall figure.

There’s still something worth investigating. But “the checkout got worse” would be a conclusion this example doesn’t support.

Do you check channel mix first when conversion drops, or start with device, landing page or customer type?

Over the past two years, server-side Tag Manager (sGTM) has become the gold standard for bypassing client-side ad blockers, browser tracking restrictions (ITP, ETP), and iOS Safari cookie lifetime caps.

However, a lot of teams set up sGTM and assume their attribution problem is solved 100%. In practice, sGTM still misses a massive chunk of high-value conversions.

Here is a breakdown of why server-side tagging hits a wall, and how combining Offline Conversion Tracking (OCT) / Offline Data Imports with sGTM fixes backend data leakage.

1. Where Server-Side Tracking Fails

Even with a custom loader and proxy domain (e.g., metrics.yourdomain.com), sGTM relies heavily on initial client-side context:

• Cookie Expiration (ITP/Safari): Even server-set cookies (Set-Cookie header via response) are capped to 7 days or 24 hours if Safari detects IP/CNAME cloaking.
• Consent Banner Drops: If a user rejects analytics cookies on the frontend, standard GTM setup blocks browser tags from firing to your sGTM container unless you run Consent Mode Advanced -which still only sends anonymized pings.
• Post-Click Downstream Events: Standard sGTM tracks real-time web actions (form fills, online purchases). It cannot track what happens after the session ends - such as subscription renewals, phone sales, CRM pipeline shifts (Lead $\rightarrow$ Qualified $\rightarrow$ Closed Won), or offline retail purchases.

2. The Solution: Blending sGTM + Offline Conversion Imports

To achieve high-accuracy signal delivery to GA4 and ad algorithms (Meta CAPI, Google Ads, TikTok API), you need a two-tier approach: Real-Time sGTM + Delayed Backend Sync.

A. Storing First-Party Identifiers at Session Time

When a user visits your site, capture and persist first-party identifiers into your backend database or CRM along with the lead/transaction record:

• GCLID / GBRAID / WBRAID (Google Ads)
• fbclid & _fbp / _fbc cookies (Meta CAPI)
• ttclid / _ttp (TikTok)
• Hashed Customer Data (SHA-256): Email, phone, first/last name, address zip.

B. Feeding GA4 via Measurement Protocol v2

Instead of relying solely on the web stream, configure your backend or CRM to fire a GA4 Measurement Protocol event when an offline/delayed event occurs:

JSON```

{
  "client_id": "123456789.1600000000",
  "events": [{
    "name": "offline_purchase",
    "params": {
      "engagement_time_msec": "100",
      "session_id": "1600000000",
      "currency": "USD",
      "value": 250.00,
      "transaction_id": "T_12345"
    }
  }]
}

Note: Always ensure session_id and client_id match the original web session to ensure GA4 attributes the offline event back to the correct channel source/medium.

C. Feeding Ad Engines Direct (Offline Conversions / CAPI)

• Google Ads Offline Conversion Imports (OCI): Upload conversion events attached to gclid or Enhanced Conversions parameters (hashed email/phone) when a lead becomes a paying customer.
• Meta Conversions API (CAPI) via Server-to-Server: Send the offline purchase event back using event_name: "Purchase", matching original event_id or customer data.

3. Key Technical Pitfalls to Avoid

1. Event Deduplication: If an order triggers both on the web thank-you page (via sGTM) and later in your backend CRM sync, ensure both payloads pass the exact same event_id and transaction_id. Otherwise, platforms will double-count revenue.
2. Time Windows: Google Ads OCI requires conversions to be uploaded within 90 days of the click. Meta CAPI recommends uploading within 7 days for optimal real-time bidding optimization.
3. Data Freshness: Don't batch upload offline conversions once a month. Bidding algorithms (Smart Bidding / Performance Max / Meta Advantage+) rely on frequent signal updates. Batching daily or running real-time webhook triggers yields much better CPA performance.

Summary Checklist for a 95%+ Signal Setup:

1. Client-side: Capture gclid, fbclid, _fbp, _fbc, and user traits on initial form submit.
2. sGTM: Proxy client events through custom domain to handle real-time browser conversions.
3. CRM/Backend: Send real closed-won sales, phone conversions, and recurring payments back to GA4 (Measurement Protocol) and Ad APIs (Google OCI / Meta CAPI).

Curious to hear how others here are structuring their offline conversion pipelines - are you using direct webhooks from your CRM, custom database scripts, or third-party ETls?

Hey everyone!

I'm looking for honest advice from people who have experience running Meta Ads for clothing brands, especially in Europe.

I run a small clothing brand with a retro/90s-inspired aesthetic mixed with modern elements. My products are priced as follows:

• T-shirts: €40
• Hoodies: €60
• Bootcut jeans: €80

I'm currently testing Meta Ads in Romania, but I'm also considering Italy because most of my previous sales came from there.

Here's my situation:

• I've spent around €52 on my current campaign.
• I've received approximately 100 website visits.
• I've had zero Add to Carts and zero Initiate Checkouts.
• About a year ago, I spent approximately €3,000 on ads in total but generated only around 10 sales at most.
• I haven't run ads through this account for about a year.
• Based on my calculations, I need a cost per purchase of around €20 to break even.

Previously, I tested different things without following a consistent strategy. I don't want to repeat that mistake or spend another few thousand euros without understanding what's wrong.

If you were in my position, what exact strategy would you follow to get sales as quickly as possible while minimizing the risk of losing money?

I'd especially appreciate advice on these questions:

1. Campaign structure: Would you start with one Sales campaign optimized for Purchases, or use a different strategy for a relatively unknown clothing brand?
2. Budget: Would you start with €20–€30 per day, or invest more to collect data faster? How would you decide when to increase or reduce spending?
3. Target market: Would you focus on Romania first, test Italy because I've had previous sales there, or consider another European country? Would you test one country at a time?
4. Products and creatives: Would you focus on one hero product, such as my bootcut jeans, or advertise all three categories? What types of videos or creatives would you test first?
5. My current results: Is spending around €52 for 100 website visits with zero Add to Carts a reason to change something immediately? What would you investigate first: the creatives, product pricing, website, tracking, or the offer itself?
6. Time to first sale: With the right strategy, how many days would you realistically expect it to take to get the first sale? Is 1–3 days possible, or should I expect 1–2 weeks or potentially longer?
7. When to stop: What specific metrics or spending thresholds would you use to decide that a campaign isn't working and needs to be changed or stopped?

I'm not expecting anyone to guarantee results, but I'd love to hear about your actual experiences. If you've launched a clothing brand in Europe, how much did you spend before getting your first sale, and what made the biggest difference?

If you were starting from scratch in my exact situation, what would your step-by-step plan for the first 7 days look like?

My priority is to get my first sales as soon as reasonably possible without burning through my budget. I'm open to honest criticism about my pricing, products, website, or strategy.

Thanks in advance!

Hi there,

I’m getting back into GA4 and was working on a client who wanted me to set up conversion actions in GTM, Ads, and now in GA4 for button clicks and forms to better see paid actions.

Then Google made all their UI changes. I’m going to set up charts in GA4 so they can see the data.

However when I make a custom Ads tag in GTM you no longer have a conversion label to make the tag. And in Ads making a conversion now is much less custom. Ie how do you track button clicks?

I’m all for automation but irritated that Google is yet again making things harder than they need to be in their UI and lack of customization.

Thanks

Hey folks,

My co-founder (21M) and I (22F) are currently building an algorithmic tool that automatically maps and detects the marketing measurement setups already present on websites and stores.

The tool scans the front-end to identify exactly what is deployed, such as recognizing whether the endpoint is GA4, Meta Pixel, both, or something else entirely. It also picks up active cookie banners and identifies front-end events like Add-to-Cart as they happen. These are just some examples to name a few, for the sake of simplicity in this post.

We are in the early R&D and training phase. To stress-test and train our algorithm's detection accuracy, we need beta testers with live websites so we can run our tool against a variety of live setups.

What we are looking for:

If you run an e-commerce store, D2C or any website for that matter with marketing analytics set up, and are open to being a beta tester for our training set, we would love to connect. We have a working proof of concept and process documents ready, and we'd be happy to walk you through how the tool operates.

If you're willing to let us run our scanner against your site to help train our model, please drop a comment below or send me a DM!

I came across a report about a possible ChatGPT referral tracking issue that could affect website analytics.

According to the report dated October 20, some ChatGPT visits temporarily lost UTM tracking information, potentially causing them to appear as Direct traffic instead of ChatGPT referrals. A fix reportedly started rolling out on October 15, but some visits may still be missing referral information.

For digital marketers and SEO professionals, this could make it harder to measure traffic and conversions coming from ChatGPT accurately.

A few questions for the community:

• Have you noticed unusual increases in Direct traffic recently?
• Are your ChatGPT referrals showing correctly in GA4?
• How are you tracking AI-driven traffic and conversions?

It would be useful to compare experiences and understand how this affects attribution reporting.

Note: This is based on reported information, not an independently verified issue.

Hey everyone,

Has anyone else noticed that yesterday’s metrics still haven’t updated in Google Analytics? (See attached screenshot.)

I know I had significantly more visits according to Microsoft Clarity, but the data in Google Analytics hasn’t caught up yet. I’ve already checked my tracking tag, and everything seems to be set up correctly.

Is anyone else experiencing the same issue? Could this be a reporting delay or a problem with Google Analytics?

Thanks in advance!

We've been seeing a huge spike in direct traffic to our website from the US, particularly Colorado, Virginia, and New Jersey, where we have relatively limited operations. The spike started around mid-September 2026 and is still ongoing.

The engagement metrics make us suspect this could be bot traffic. For example, for Colorado alone (September 20–October 19), GA4 shows:

Direct sessions: 10,362

Total users: 10,199

New users: 10,193

Active users: 10,188

Average engagement time: 2 seconds

I also came across a recent post from someone experiencing something similar, wondering if this is part of a broader trend.

Could it be AI crawlers, bots, automated scanners, or something else?

How would you identify and filter this traffic in GA4?

We have checked the Tech details reports, but haven't found any obvious distinguishing characteristics, such as screen resolution or browser/device information, that would help isolate it. Is there a reliable way to exclude this traffic from reporting without accidentally filtering out legitimate users?

Appreciate any insights or suggestions. Thanks!

Hey everyone,

I’m trying to wrap my head around a specific cross-channel attribution scenario between Google Ads and GA4 and would love some input on how the data is credited.

Here is the exact timeline:

• Day 1: A user clicks on one of our Google Ads (Search campaign).
• Day 3: The user makes a purchase. (As expected, this counts as a conversion in Google Ads against the campaign, and GA4 attributes it to the Paid channel).
• Day 4: The same user returns to the site, but this time they come through an Organic Search link and make a second purchase.

My questions are about that second purchase on Day 4:

1. In Google Ads: Will this second conversion still be credited to the original campaign since it happened within the click-through conversion window (e.g., 30 days)? Or does Google Ads ignore it because the immediately preceding session was organic?
2. In GA4: How will this second purchase show up in the standard reporting? Will the conversion fall on Paid Search, or under Organic Search?

Any insights on how these two platforms will report this specific sequence would be hugely appreciated. Thanks!

Which plugin is the best ? I use with chat gpt I saw few there but I wanna ask what u guys recommended too help 🫡

Since October 15, I've been experiencing a significant delay in GA4 standard reports. Here's the situation:

• Real-time report works (I can see active users in real time).
• However, in standard reports, "Today" data is missing/delayed. The number of users who came in during the day, and how many of them were new users, only shows up the next day.
• This has been going on for about 3 days now and started on October 15.

Has anyone else experienced the same issue?

I was working on a Google Ads account where I set up separate purchase conversion actions for client-side tracking, server-side tracking, and offline conversion tracking.

I compared the reported results over the same three days: 26 to 28 August 2026. Here are the numbers I recorded:

→ Server-side tracking: 160.76 conversions
→ Offline conversion imports: 153.00 conversions
→ Server-side + offline: 223.36 conversions

The combined conversion action reported 38.9% more conversions than server-side tracking alone. The interesting part was deduplication.

Adding the two individual figures gives 313.76 conversions, but the combined action reported 223.36 after accounting for overlapping purchases using transaction IDs.

So the two sources were not contributing entirely unique conversions. This is why I don't think comparing conversion counts alone is enough to evaluate a tracking setup.

I'd want to validate the results against actual backend orders, check transaction IDs, compare conversion values, and confirm that the conversion actions are configured appropriately.

One thing I'm still interested in discussing is how others approach this comparison.

When comparing client-side, server-side, and offline purchase tracking in Google Ads, how do you validate which conversions are genuinely additional versus already captured by another source?

I documented the account comparison in CMnT for anyone interested in the setup

Hi

I have a new website that uses Stripe Checkout Session for the payments. In case you aren't familiar with the flow: the user places an order on our website, gets redirected to Stripe's secure payment page, makes the payment and then gets redirected back to the success page on our website.

Under "Traffic Acquisition", this is what I see:

1. (direct) / (none)
2. l.instagram.com / referral
3. tagassistant.google.com / referral
4. google / organic
5. chatgpt.com / ai-assistant
6. checkout.stripe.com / referral
7. instagram.com / referral
8. ig / social
9. (not set)
10. m.facebook.com / referral

Should I somehow exclude checkout.stripe.com / referral?

What do you recommend? Thanks

Hi,

My Amazon affiliate links confirm that there are visits, but GA since yesterday has been showing 0 for all properties, anyone experiencing the same?

Hey everyone,

I'm trying to set up GA MCP with Claude however I can't install homebrew because I have an Intel-based Macbook and it's no longer supported. Are there any alternatives to homebrew out there that I can use?

I'm using Macbook pro 2019 MacOs Sequoia

Thanks in advance!

UPDATE: Thank you everyone for all your responses! I'm honestly not sure how I did it but I managed to set it up with Python? not sure what I did lol but it's up and running now

Hi everyone, me and my client cant reach daily reports snapshot for 20 hours, it says there is no data. everthing seems zero.

when the real-time data (last 30 mins) is flowing without problems. i can see that there is traffic, actions and conversions.

I have checked filters, gtm and pixels and everything seems okay. can you help?

Hi all. I'm trying to add a couple of filters to some of my reports in GA4, but I keep getting a "this report cannot be saved" message. It's happening on multiple properties as well. Any ideas what could be causing this?

I recently launched an app last April, and it’s going well, but there’s an issue with the mau and total users. The thing is that my Mau is saying 7k while the total users are 280. Can someone explain what is happening?

The goal is to have GA data into BigQuery. Using Data Studio to visualise from data in BigQuery. Don't really have a background in this area, but rather a front end marketing person who plays around in GA.

I understand GA can import data directly to BigQuery. But we don't have the resource or knowledge to turn these into proper tables or know what to look for in these.

Did a demo with with Windsor and it looks good for what we need. Though it runs into the 10 dimension limit, which I believe the native BigQuery/GA does not have.

• How are others using Windsor or alternatives, structuring their requests and managing the metric/dimension fields?
• Are you basically re-creating and choosing the fields that you already see in the GA interface reports?
• If I re-created those separately as requests (eg. Acquisition and Events), how would managing the overlapping fields be like to create one report? Because in GA you can add a secondary dimension for example to the traffic report and overlay an event name
• If so, is there a resource I can find online?

I tried to search, but no success in finding what I was looking for, maybe the question I'm asking isn't the right one. The fear here is, we can look at nearly everything but in 2 months time, a question might come up with the data we haven't included in the initial fields.

Any direction would be helpful.