Lucas
Forum Replies Created
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The simplest way to start tracking drag and drop actions on your web app using Google Tag Manager (GTM) would be to incorporate JavaScript event listeners, which would pick up on “dragstart” and “drop” events.
Firstly, create two new Custom HTML tags in GTM. The tag for the “dragstart” event would leverage a JavaScript event listener set to trigger on this specific event. Similarly, the tag for the “drop” event will be set up in the same manner, but to trigger on the “drop” event. With this setup, every time a user starts to drag or drops an element on your site, an event will fire.
Next, you’ll want to push these events into the Data Layer. You can accomplish this directly within your new Custom HTML tags, using the dataLayer.push method. This will allow you to push key information about the event (like the ID of the element being dragged, and its new position when dropped) into GTM’s Data Layer.
Once your new events are being pushed into the Data Layer, you can proceed to set up corresponding triggers in GTM. These triggers will be set to fire when GTM detects the newly defined “dragstart” or “drop” event in the Data Layer.
Finally, you can connect these triggers to appropriate tags for GA4, effectively sending each drag and drop action as an event to GA4 for you to monitor and analyze.
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From the sound of it, it seems like you’ve encountered a common issue in reporting where adding another dimension, in this case, ‘usergender’, to your report can cause the return of an empty response especially if the data source for that dimension doesn’t have usable data. ‘SetIncludeEmptyRows’ should ideally work, but there could be a number of reasons why it’s not. It might be how your database is structured, it might be how you’re calling up the report, or it could even be an error somewhere in your PHP code. Without more specific information, it’s hard to give a more specific answer. However, if you double-check the mentioned areas, you might find the culprit.
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It’s possible that the discrepancy you’re seeing between GA4 and UA could be due to the fundamental difference in how both tracking systems work. The GA4 works based on events, whereas UA is mainly about sessions. So, a page view in GA4 might not necessarily reflect the same thing as a session in UA. Different aspects such as custom parameters and the way each system interprets user interaction can also affect this difference. Additionally, filters, sampling, and other settings can cause variances. It’s recommended you review the specific settings, interpretation of hits, timing, and potential data discrepancies for each system to identify any specific issues.
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It’s really great to hear that you’ve managed to link your application to GA4 and see user counts! But running into an issue where custom dimensions aren’t reflected in the GA4 reports can be frustrating. Generally, the delay can be because GA4 may take up to 24-48 hours to fully reflect new data, especially for custom segments or dimensions. However, as you’ve waited more than that, it’s more likely the issue is somewhere else.
One common mistake is mismatched names between your GA4 setup and your application code. Make sure that the custom dimensions in your GA4 match exactly what’s specified in your code, considering any caps or special characters. Remember dimensions are case sensitive.
Additionally, ensure that your application’s code is correctly sending dimension values – not just static values – to Google Analytics and there’s no issue with data types.
If everything checks out with the dimensions and code, consider looking into your GA4 property and view settings. It’s also worth rechecking the permissions of the user profile you’re using. In some cases, data might not appear due to incorrect settings or if the user lacks necessary permissions.
To troubleshoot further, you could use Google Tag Assistant or GA Debugger chrome extensions to check that data is being sent correctly. These tools can provide a detailed event-by-event breakdown to uncover the issue.
Please note it’s recommended to consult with someone who has technical knowledge as the issue might be more complex and could require detailed debugging.
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Hey there!
Just to clear up the situation a bit – it’s totally normal that your custom parameters are appearing as ‘(not set)’ for the ‘session_start’ and ‘first_visit’ events. These are auto-collected events and unfortunately, they’re just not designed to capture custom parameters. It’s kind of like they’ve got their own thing going on, y’know?
And hey, before you ask – yes, this could have some impact on your reports. Here’s how it works. If you base your reports on the ‘views’ metric solely, and you’re always setting your custom parameter, you’ll never lay eyes on a ‘(not set)’ value in your report. But if you’re a ‘users’ metric kind of person, be prepared to see a bit more ‘(not set)’ action. This is simply because ‘users’ includes those fun-lovers, the ‘session_start’ events, which as we know, don’t play ball with your custom parameters.
So, don’t fret. You’re not missing anything, and you’re not messing up. It’s just the way things flow in the GA4 world! Hope we’ve managed to illuminate the situation for ya. Let us know if you need more info. Keep up the good work with your data and happy analyzing!
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There could be multiple reasons for the misalignment between Shopify and Google Analytics revenue data. First, check if the Google Analytics tracking code has been properly installed on all pages of your Shopify store. Also, ensure that you have enabled eCommerce tracking in your Google Analytics account. It’s crucial that your redirect script doesn’t interfere with the Google Analytics code. If you’re using any script blocking plugins, it might also prevent Google Analytics from tracking properly. Adding a post-purchase script could help, as Google Analytics eCommerce tracking works by sending an event once a purchase is successful. Finally, remember to filter out your IP address from Google Analytics to avoid skewed data. If all these are set correctly and the issue persists, consider seeking help from a Shopify or Google Analytics expert.
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It’s likely you’re seeing an error because the syntax of your query is incorrect. In the Google Analytics 4 (GA4) SQL system, custom dimensions are not queried in the same way as regular dimensions. The ‘Dimensions’ in your query is likely being interpreted as a column name, resulting in an error because there’s no such column present in the “GoogleAnalytics4″.”Engagement” table.
Instead of including ‘customUser:dimension1’ within the WHERE clause of the SQL statement, you should include it as one of the fields being selected, just like ‘Date’ and ‘conversions’. This is because ‘customUser:dimension1’ is a dimension, not a specific value within a column. Your query should look somewhat like this: ‘SELECT Date, conversions, customUser:dimension1 FROM “GoogleAnalytics4″.”Engagement” where Date=’20230510”. Performing the query in this manner should solve your problem, assuming ‘customUser:dimension1’ is a valid dimension within your GA4 dataset.
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Unfortunately, as of now, entrances data is not directly available via Google Analytics 4 Reporting API. The GA4 Reporting API has its own set of metrics and dimensions that it supports, and currently, it does not include entrances. It’s important to remember that the API and the Google Analytics interface don’t always offer the same data. The Google Analytics team is constantly updating and expanding the functionality of Google Analytics 4, so it’s possible that this feature could be added in the future. I recommend checking their documentation regularly for updates.
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Hey! Just so you know, you actually can create a
client_id. GA4 generates it in a similar way to UA – you don’t need anything from Google to make it.You could also try going straight for the Measurement Protocol: https://developers.google.com/analytics/devguides/collection/protocol/ga4. The links given by
@dockeryZmight work better if you open them in an incognito window or a different browser.Plus, there’s no need to build everything from scratch. Node already has some packages for sending events to GA4. Here’s a good one as an example: https://www.npmjs.com/package/ga4-mp?activeTab=readme.
And lastly, if you’re worried about Google’s bot traffic detection, don’t be! It’s not exactly sophisticated. As long as your user agent doesn’t blatantly say “bot”, you should be fine. Just be creative with it. Hope this helps!
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Yes, it’s possible to add your Google Analytics code in a different spot within your Angular project. One common method is to inject the Google Analytics script dynamically using TypeScript. You can create a service for it.
In the service, you can use the Renderer2 from ‘@angular/core’ to create a script element, set the ‘src’ attribute to Google Analytics URL with your tracking ID, and append this script to the ‘head’. This method keeps your code clean and does not show the script tag in the HTML file when the source code is viewed via the browser, as it will be injected into the DOM at runtime.
This method not only helps keep your code cleaner but also provides more control over when and how the Google Analytics script is loaded. Please remember that altering the placement of the tracking code can sometimes affect data collection, so you need to monitor your data to ensure everything is working as expected.
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It’s not just you! Google Analytics 4 and Universal Analytics do have different ways of collecting and processing data, so there can definitely be discrepancies between them. Sometimes it’s due to the way sessions are counted, how users are tracked, or the types of interactions that count as engagement. That could explain why your page views are so different!
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Hey there! So, in a nutshell, what you’re asking for isn’t really something that can be done with Google Analytics alone. Google takes user privacy very seriously, so they don’t allow any personally identifiable information (like email, phone numbers or social security numbers) to be passed through their system.
But, if you’re tracking user details in your own system and assigning each user an ID, that’s another story. You’d then use these IDs as a custom dimension in Google Analytics, allowing you to tag the data you’re interested in without shuffling through any personal info.
Keep in mind though, it’s super important to be cautious when handling user data, always making sure not to cross any lines into personal identifiable info territory. When in doubt, always best to check with a professional! Happy analyzing!
