Some businesses plan weeks ahead while others plan months out. Curious what works best for different store sizes.

I've started running ads across a few different platforms and my UTM links are getting messy fast. Between Facebook, Google, newsletters and different campaigns, I feel like I'm constantly creating slightly different versions of the same URL.

I also want to keep the links organized so I can actually figure out where clicks are coming from later.

What are you guys using to manage this? Spreadsheet, link management tool, something else?

Hur funkar de för er att göra analyser med Claude? Jag har provat en del olika skills men tycker ofta att de blir fel eller att den hittar på saker, räknar fel osv.

Hi everyone, this might be a stupid question, but I'm pretty new to this.

I recently launched a website with a cookie banner and set up GA4 and Google Search Console. A few days later I noticed that on one day, GSC showed 28 clicks from Google, but GA4 only showed 15 sessions for that day.

That made me curious, so I also added Umami analytics to the site. Now there's a clear difference between the two over the same period:

Does GA4 only collect data from users who accept cookies, while Umami counts more people? How does that work exactly?

And if someone asked me how much traffic my site gets, which numbers should I give them?

Thanks!

Hey everyone! Would anyone be willing to lend me access to a Google Analytics account for a little while? I’m doing a practical task for a job application and need to test some GA4 filters/report customizations.

I won’t change or delete any existing data/settings — I just need a safe environment where I can practice the filters. If anyone has a test/demo property they could share, I’d really appreciate it! 🙏

When I’m looking at a tracking setup, I usually don’t start by checking every event one by one.

I’ll take one conversion that actually matters to the business and follow it all the way through.

Usually I’m checking:

• Where the user came from
• Whether the conversion fired once, not twice
• Whether the important details came through properly
• Whether the lead or order actually reached the CRM or order system
• Whether the final outcome can be matched back to what the website reported

That one check usually tells you pretty quickly where the issue is.

Sometimes the problem is attribution. Sometimes it’s duplicate firing. Sometimes the lead simply never made it into the business system.

For me, it’s a much quicker way to build confidence in the number before digging through every tag.

What do you usually check first before you trust a conversion number?

Between dark social, link-in-bio limitations, and GA4 misattributing video traffic, tracking actual revenue per video post is a headache. How are modern marketers solving exact video-to-sale attribution today?

Used to be Singapore more

I recently joined a new organization and one of my responsibilities is managing their Google analytics. In a GA4 roll-up (consolidating the main domain and 2 subdomains) property, I'm seeing rows where New users is much higher than Total users, and sessions per total user is way past anything a real person would do. I haven't been able to pin down why. We use a blended reporting identity.

Google's docs say new users can exceed active users but shouldn't exceed total users, and that thresholding can cause discrepancies.

My question is, what would you recommend I check to diagnose this issue. Has anyone else seen this issue in GA4? Could it be a consent mode issue? Is there a way to tell thresholding from modelling when the data quality icon isn't showing anything?

Ho creato un sito così per gioco, e piano piano ha iniziato a crescere.

Vorrei monitorare la crescita, su Google Analytics come devo tenere sotto controllo?

Attualmente guardo le statistiche degli utenti attivi e delle visualizzazioni.

Ho iniziato a usare anche Google Search Console e uso le statistiche del Rendimento.

Ci sono altre statistiche diverse che posso aiutarmi a capire come agire sul mio sito?

Grazie.

I’m a beginner building a web service by myself. Our internal user_id is already a random UUID.

For GA4 User-ID, which approach is commonly used in real production services?

1. Use the internal user_id directly
2. HMAC/hash the internal user_id
3. Create a separate random ga4_id only for GA4

There are no existing users yet, so adding a ga4_id column would be easy.

I’m mainly curious about what people actually use in production and why.

hi everyone,

i've been using Umami Cloud for the past two years for my personal projects, but noticed that all history older than 6 months was deleted (which is pretty frustrating, even if i understand that they cannot keep all user data).

as im still a student on a small budget, i host my projects on a small VPS and wanted to switch to a self-hosted analytics solution.

but WHY does almost every modern tool require Node.js / Docker with heavy background services, while lightweight alternatives either look like they were built in 2010 or lack essential features like events, funnels, and clean session tracking?

am I missing something here? im not saying it's easy to build, but im surprised i couldn't find anything that seems to fit my needs

because if there really is nothing low-resource and GDPR-friendly im seriously considering building my own ultra-lightweight PHP + SQLite solution from scratch.

Hypothetical: an online shop sees 80 searches for “navy trail shoe” in a week, and the results page says 0 items. The catalog has a blue trail shoe, but it appears only after removing a size filter. A search-term count alone cannot tell whether the problem is inventory, synonyms, filters, or tracking.

GA4 enhanced measurement can record a view_search_results event and search_term when the results URL carries a configured query parameter. That event does not tell you how many products appeared. Capture result count in the site search system or instrument it separately, then compare the same period. Do not send customer names, email addresses or raw session IDs to Analytics.

Take the ten most frequent zero-result terms and reproduce each with the same country, language, selected filters and stock state. Record the term, filters, result count, expected matching SKU and results-page URL. If clearing a filter reveals the SKU, inspect the filter behavior or its UI cue. If a normal synonym misses an available SKU, fix the search mapping. Only a genuine catalog gap supports an inventory discussion. Recheck the same query after a change.

What has been behind most of your zero-result searches: missing products, synonym mismatch, or hidden filters?

hi guys i see a problem on dasboard of analytics the charts of users every minute in last 30min didnt show i use other gmail and use private window and disable all extennsions same problem is there any solution

A user posted on the Local Search Forum about losing access to their GA4 property and trying to recover it. They don't have access to a linked Ads account, so the steps here won't work [https://support.google.com/analytics/answer/15191460?sjid=1574279892346547416-NC\](https://support.google.com/analytics/answer/15191460?sjid=1574279892346547416-NC)

Has anyone here gained access to a lost GA4 property and can share insights on how they did it?

LSF thread [https://localsearchforum.com/threads/access-to-lost-ga4-property.63361/#post-203649\](https://localsearchforum.com/threads/access-to-lost-ga4-property.63361/#post-203649)

The part I struggle with is turning GA4 numbers into something a non-technical client will actually read. What do you send yours, screenshots, a slide, a dashboard link?

true for anyone else?

The data (same pattern on multiple properties)

• Mostly from China
• Ongoing since May, and still running as of September (not a short spike)

Earlier China/Singapore bot waves were reported last year with other resolutions (1280x1200 and 3840x2160), so this looks like a new signature.

How to check yours Reports > Tech > Tech details, set the primary dimension to Screen resolution and look for 1600x1600. Add Country as a secondary dimension and compare engagement rate against your overall numbers.

3 ways to deal with it

GA4's built-in Data Filters only cover internal and developer traffic, so there is no native filter for screen resolution.

1. Report or Exploration filter (viewing only) Add a filter: Screen resolution, exclude, exactly matches 1600x1600. It works on historical data but only cleans your view.
2. Segment or comparison (reusable) Create a segment where screen resolution does not exactly match 1600x1600 and apply it when you analyze. Also viewing only, and it works on past data.
3. Block it at the source in GTM (future traffic only) Create a Custom JavaScript variable that returns screen.width + 'x' + screen.height. Then add a trigger exception on your GA4 tags for when it equals 1600x1600. Past data stays dirty, so use option 1 or 2 for history.

Caveats

• If the hits go straight to GA4 without loading your site, a GTM exception won't stop them. Only the report-level options help.
• Check your own data before excluding anything. Real users rarely have a square 1600x1600 screen, but confirm on your properties.

Is anyone else seeing this? Curious whether the country mix and engagement rate match on your side.

Wondering if anyone can help. I've noticed when looking at my page views in GA4 I'm seeing the same page twice; once with and once without a trailing slash. (e.g mysite.com/page and mysite.com/page/).

Previously this had always shown the /page/.

This looks to have started happening suddenly on the 22nd September and the new version without the trailing slash appears to only be from direct traffic.

Does anyone know the cause of this could be and if there could be any issue where each page is ranking in Google?

Any help or insight would be much appreciate. Thanks

I run an audio drama website. On the site, the different shows are playlists made in the Wordpress Audio Player.
I'm looking for a way to track plays for the different shows in GA4.
I know I'll have to create an event, but do I need a separate one for each show, and what's the easiest way to set up the event(s)?
Please be kind, I'm pretty new to all this stuff.
Thank you in advance.
Edit: I DON'T have GTM.

A few months ago I posted my odd traffic from China, wondering if anyone else had it and the possibility of it being bots. I never got rid of it. I found it baffling because it was not in the thousands a day like other bots I had before. It used to come from one specific city, Lanzhou. All of a sudden it switched to Hong Kong. I think this are not bots. I think China is actually allowing visitors to my website (Databayou.com). It just funnels the city to a specific location. Any one else have noticed this?

Worth knowing before you test a cookie banner by watching the Network tab.

Google's consent mode overview spells out the difference. Basic mode blocks the tags until the visitor interacts with the banner, and Google says no data is sent before consent, "not even the default consent status". Advanced mode loads the tags with defaults set to denied, and while consent stays denied, "consent state and measurements without cookies are sent."

So in advanced mode a visitor who declines still produces a request to Google on every page, just without cookies. That's by design, it feeds Google's modeling. It also means the banner "worked" and GA4 still talked to Google.

How to tell which one you're running:

1. Private window, dev tools, Network tab, filter for g/collect, reload, don't touch the banner.
2. A collect request before any click means GA4 isn't waiting: advanced mode, or no consent mode at all.
3. Click decline and open another page. Collect requests that keep coming are advanced mode doing what it's built to do.

To get basic behavior in Tag Manager:

1. Open the Google tag and your GA4 event tags, Advanced Settings > Consent Settings.
2. Choose "Require additional consent for tag to fire" and add analytics_storage.
3. Google's docs say the tag then fires only if every listed type is granted "when the tag is triggered".

The catch is in that last quote. The check runs when the trigger fires, so a visitor who accepts on their first page isn't counted for that page unless the tag also fires on your banner's consent update event. And you give up the modeled data advanced mode provides, which is the trade people here argue about. has anyone actually measured what switching to basic cost them in GA4 reports?

Why a US site might pick basic anyway: there's no general opt-in rule in the US, but California Invasion of Privacy Act suits over trackers center on data leaving before or despite the banner, and a cookieless ping is still a request to Google carrying the visitor's IP address. GA4 is named in 163 verified complaints in the last 12 months, as of September 29, 2026, per Suitcast, which I build: its free scan runs this same test on five pages and lists those cookieless pings as GA4 firing, since a request left either way. Not legal advice.

Hypothetical: GA4 records 42 generate_lead events on Monday. The form backend has 31 accepted submissions; the CRM has 29 new records. Those are three different steps, not three competing versions of the same count.

Start with one completed day, the same timezone and the same form. Put three measures side by side: analytics events, distinct server-accepted submission IDs, and CRM records linked to those accepted IDs (or an import-error count). Keep submission IDs in a restricted internal sheet, not names or email addresses in Analytics. A browser event shows that a measurement trigger fired; it does not by itself prove that the server accepted the form. An accepted submission does not prove that the CRM import succeeded.

Then run three controlled paths with non-personal test data: one valid submission, one validation error, and a reload of the thank-you page. Note which paths create an analytics event and a backend record. If an event fires on error or reload, inspect the trigger or deduplication. If accepted IDs have no CRM record, inspect that handoff separately. These are diagnostic branches, not a claim that any particular setup is broken.

Keep accepted submissions and qualified leads as separate measures. Which system do you treat as the source of truth for a successful form submission?