Experiencing problems with Funnel Explorations while user_id is implemented. (see screenshots, for the difference in user counts.)
I was told by Google support that the funnel exploration will not show any events where a user_id is assigned, due to the reports using "TVF" + "Gaia" events, whereas the funnel exploration does not include Gaia (user_id assigned) data. I could not find any documentation online related to this.
I am trying to find clarity on:
Is this really the correct functionality for funnel exploration, to exclude data associated with a user_id?
Does this affect all explorations or only certain types?
We want to continue to run our GA4 property with user_id implemented, but how can I do funnel exploration and generate visuals to share with my team?
view from engagement>events report
view from exploration>funnel
I verified the correct data is flowing into the GA4 property, I can see the correct number of purchase events and users in the Engagement>Events report, but when viewing in funnel exploration I am only shown a tiny fraction of users for the same date range.
Related
I'm tracking several events on my website using Google Analytics 4.
Some of those events are more important and I'm considering them as conversions. However, they happen at different steps of the user journey, so each of them would have a different cost and return, so I'd like to consider them as different conversions, in the same way they correspond to different events.
The problem is, when I want to report on them (e.g. on Data Studio, or Google Ads UI, or even on Google Analytics funnels), you can only see the aggregated sum of conversions, rater than being able to select a specific conversion.
Is there a way to make GA4 (an Data Studio) either:
Split reports by specific conversions (rather than the total number of conversions)?
Split reports by events rather than conversions?
I feel like GA4 has been a step back in terms of report customisation and data analysis.
Thanks!
I am using Google Bigquery to extract data on conversion paths from Google Analytics (GA).
When I analyze these conversion paths from the exported dataset, the last-click conversions match the Acquisition report in GA, but not to the Multi Channel Funnel (MCF) data. Apparently Bigquery doesn't really export raw data, but transforms it by deleting all last direct clicks. like described here: https://support.google.com/analytics/answer/1319312?hl=en.
Is it possible to get the Bigquery data to correspond to Multi Channel Funnel (MCF) conversion path data? To undo the deletion of last non-direct click and get proper 'raw' user level data?
All of the trafficSource fields in BigQuery Export for Google Analytics use campaign attribution as described in this processing flow, which will overwrite direct traffic with the most recent campaign (if there is one and it is within the specified timeout), as you mentioned.
If you are using Universal Analytics, you can adjust the campaign timeout to be shorter than the 6 month default. For example, if you set the campaign timeout to be one day, any direct visits that come in at least one day after a visit with a campaign will be attributed to direct instead of the previous campaign. This can be done with Classic Analytics as well using _setCampaignCookieTimeout. This technique will affect data collection from the time it is implemented going forward.
This thread is rather dated, so I thought I'd update just in case anyone else comes across this same question.
There is a field that was introduced (both in the Google Analytics interface and the BigQuery export) that allows you to match the numbers in the MCF reports. In BigQuery, look for the field trafficSource.isTrueDirect
BigQuery Export Schema
trafficSource.isTrueDirect
True if the source of the session was Direct (meaning the user typed
the name of your website URL into the browser or came to your site via
a bookmark), This field will also be true if 2 successive but distinct
sessions have exactly the same campaign details. Otherwise NULL.
In Google analytics how can i get a metric to put "number of active users on website at any one time". I want to put it against past date periods.
I.E I was scrolling through the metrics, trying to add a widget to my dashboard but, I can't find this metric!
It looks like it's possible because google analytics uses it on their standard report, real-time, "current active visitors on website"
How can I achieve this?
Simply, you can't out of the box. There are some limitations in real-time reports and the ways GA counts unique visitors.
However, if you do not need a detailed analysis, this custom report might do the work (just import it for your Analytics Profile/View and click Customization tab). It basically shows the generic numbers (you can change them to fit your needs) according to hour of a day.
Add any secondary metrics to find out how numbers change in days/weekends etc. Or you can slice the data with segments and see if some traffic is more active in the morning etc.
Hope hits helps.
PS: Beware of data sampling...
Suppose I have 65 people that register on January 1, 2012.
I want to find out how many of those 65 people returned to the site that same week. (More generally, if n people signup on date A, I want to be able to find out how many of those n people return in a given date range.)
Is there a way to do this using Google Analytics? If so, how? I am currently getting the user's username for each page hit.
If you only need to track people who sign in then you don't need to get very fancy. You can copy the relevant user attributes, such as sign up date, from your DB to GA using events or session level custom variables.
But if you want to track everyone, including those who don't sign up, then you'll need to use visitor level custom variables (GA cookies).
I explain how to set this up in detail in this post so I'll just highlight the key points here:
First, decide how to layout the data in Google Analytic's custom variables based on your requirements. For example, are you storing retention dates for daily, weekly or monthly tracking? Do you also want to track cohort goals? Partition this data into the available custom variable slots.
Write the cohort data to these custom variables when visitors arrive or achieve goals using Google Analytic's _setCustomVar function. Setting the fourth parameter of that function to 1 indicates you want to do visitor-level (cookie) tracking.
For each cohort you wish to analyze, create an advanced segment in Google Analytics. Using a regex expression in the condition will give you the flexibility to segment for interesting cohorts. ex: "All users whose first visit was the week before Christmas".
Analyze the results with reports by specifying a date range and the corresponding cohort-sliced advanced segments. Another option is to extract the data using the Google Analytics Data Feed Query Explorer or their API.
Once you've put in the work your new visitors will be stamped by their first visit date and nicely fall into each daily or weekly retention bucket. This is what it might look like if you were tracking weekly retention, for example:
This is not a full solution, but here are some points on how I would approach this problem with the help of Google Analytics:
You have to make sure that you somehow store the registration date of each user, either in your database or in a cookie. Then have a look at Google Analytics Event Tracking. You could for example set up a new category based on the registration date. On every page load in your page, you then have to set up this event tracking call, for example like:
_trackEvent("returns", "2012-01-01", "UserId:123123123")
This way you will receive all page views for users that registered on that particular date. To add a date range in this, you have to make sure that these events only get fired for the number of dates after the signup (e.g. 7 days).
After your date range, you will be able to see how many page views and how many users returned - you even know which users came back.
Tools like Mixpanel, KISSmetrics and others support cohort analysis out of the box but I've heard that you can do this with a bit of effort in Google Analytics as well. How do you set this up if you want to track, say, the daily and weekly retention of your visitors?
Google Analytics can do a lot but retention analysis is one of it's weak points. Since it tends to focus on visits (as opposed to visitors) you'll need to configure the cookie tracking yourself using Google Analytic's custom variables. Having said that, it's not too hard to get a simple solution running quickly.
First, decide how to layout the data in Google Analytic's custom variables based on your requirements. For example, are you storing retention dates for daily, weekly or monthly tracking? Do you also want to track cohort goals? Partition this data into the available custom variable slots.
Write the cohort data to these custom variables when visitors arrive or achieve goals using Google Analytic's _setCustomVar function. Setting the fourth parameter of that function to 1 indicates you want to do visitor-level (cookie) tracking.
For each cohort you wish to analyze, create an advanced segment in Google Analytics. Using a regex expression in the condition will give you the flexibility to segment for interesting cohorts. ex: "All users whose first visit was the week before Christmas".
Analyze the results with reports by specifying a date range and the corresponding cohort-sliced advanced segments. Another option is to extract the data using the Google Analytics Data Feed Query Explorer or their API.
Once you've put in the work your new visitors will be stamped by their first visit date and nicely fall into each daily or weekly retention bucket. If you need more detail there's a full walk through on my blog:
How to do Cohort Analysis in Google Analytics.
This really interested me so I did a little research and basically you have to customize the GA javascript in the pages to upload custom variables into google.
Once you have done that you need to go to "Advance Segments in Google Analytics" and select your custom variables. Here is a detailed description on how to accomplish this:
Hacking a Cohort Analysis with Google Analytics