It feels silly to ask a question like this but I really can't find a view by myself after clicking anywhere in the interface at least twice.
I switched my app from analytics to firebase and those events do reach the firebase backend.
I also can see the events from the last 30 minutes.
They are stored under select_content -> Parametername = item_name
But I do not find a view of way to select a wider period of time. Like show that same statistics over a longer period of time.
Do I have to set up a specific view? Or how can I get those results?
I had this exact problem and finally found a way to do it (though using the Google Analytics site instead of Firebase- but still using the select_content event).
In Google Analytics you can create a custom dimension from item_name (I did this in All events->Manage Custom Definitions->Create custom dimensions) then create a custom analysis in Analysis hub: I started from Blank, then used the Exploration Technique, selected Bar Chart for Visualization, added item_name as the Breakdown and Event Count as the Value- this gives the total event count for each item_name over the selected time period.
If you change the Visualization to Line Chart, you can also see how the values change over larger time periods (Granularities: Hour, Day, Week, Month).
Screenshot
Related
I have a website where the customer can take a quiz to find the right products. I want to track the customer's journey on the quiz, to see what they choose and where people fall off.
The quiz starts at example.com/start/ and for every step they take, the URL "expand" to e.g. example.com/start/first_step/, next step example.com/start/first_step/second_step etc.
I think I can do it with tag manager, by creating events for each step / URL. But my first issue is, that the events get to long. The other issue is, I cant figure out how to visualize the journey in either Google analytics 4 or Google data studio.
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Does any of you have a great idea for how I can do it?
what exactly to you mean saying "the events get to long"? Do you mean the requests payload to the google analytics server?
Generally speaking, I'd recommend the following:
Create a generic event for all quiz steps
Add two parameter for the progress, one containing the steps name an one containing the index (e.g. "quiz_step_str" & "quiz_step_index")
Send the events to GA4
Visualize them using a bar chart. (https://analyticsdemystified.com/google-analytics/step-step-guide-creating-funnels-googles-data-studio/)
Additional information regarding step 2:
The parameter containing the step name can be generated using js and getting the value from the url. Just scrape the corrsponding part from it (e.g. "example.com/start/first_step/" -> "first_step").
For the parameter containing the steps' index, I recommend creating a lookup table.
I'm logging some custom metrics in Application insights using the TelemetryClient.TrackMetric method in .NET, and I've noticed that occasionally some of the events are duplicated when I view them in the Azure portal.
I've drilled into the data, and the duplicate events have the same itemId and timestamp, but if I show the ingestion time by adding | extend ingestionTime = ingestion_time() to the query then I can see that the ingestion times are different.
This GitHub issue indicates that this behavior is expected, as AI uses at-least-once delivery.
I plot these metrics in charts in the Azure portal using a sum aggregation, however these duplicates are creating trust issues with the charts as the duplicates are simply treated as two separate events.
Is there a way to de-dupe the events based on itemId before plotting the data in the Azure portal?
Update
A more specific example:
I'm running an algorithm, triggered by an event, which results in a reward. The algorithm may be triggered several dozen times a day, and the reward is a positive or negative floating point value. It logs the reward each time to Application Insights as a custom metric (called say custom-reward), along with some additional properties for data splitting.
In the Azure portal I'm creating a simple chart by going to Application Insights -> Metrics and customising the chart. I select my custom-reward metric in the Metric dropdown, and select Sum as the aggregation. I may or may not apply splitting. I save the chart to my dashboard.
This simple chart gives me a nice way of monitoring the system to make sure nothing unexpected is happening, and the Sum value in the bottom left of the chart allows me to quickly see whether the sum of the rewards is positive or negative over the chart's range, and by how much.
However, on occasion I've been surprised by the result (say over the last 12 hours the sum of the rewards was surprisingly negative), and on closer inspection I discovered that a few large negative results have been duplicated. Further investigation shows this has been happening with other events, but with smaller results I tend not to notice.
I'm not that familiar with the advanced querying bit of Application Insights, I actually just used it for the first time today to dig into the events. But it does sound like there might be something I can do there to create a query that I can then plot, with the results deduped?
Update 2
I've managed to make progress with this thanks to the tips by #JohnGardner, so I'll mark that as the answer. I've deduped and plotted the results by adding the following line to the query:
| summarize timestamp=any(timestamp), value=any(value), name=any(name), customDimensions=any(customDimensions) by itemId
Update 3
Adding the following line to the query allowed me to split on custom data (in this case splitting by algorithm ID):
| extend algorithmId = tostring(customDimensions.["algorithm-id"])
With that line added, when you select "Chart" in the query results, algorithmId now shows up as an option in the split dropdown. After that you can click "Pin to dashboard". You lose the handy "sum over the time period" indicator in the bottom left of the chart which you get via the simple "Metrics" chart, however I'm sure I'll be able to recreate that in other ways.
if you are doing your own queries, you would generally be using something like summarize or makeseries to do this deduping for a chart. you wouldn't generally plot individual items unless you are looking at a very small time range?
so instead of something like
summarize count() ...
you could do
summarize dcount(itemId) ...
or you might add a "fake" summarize to a query that didn't need it before with by itemId to coalesce multiple rows into just one, using any(x) to grab any individual row's value for each column for each itemId.
but it really depends on what you are doing in your specific query. if you were using something like sum(itemCount) to also deal with sampling, you have other odd cases now, where the at-least-once delivery might have duplicated sampled items? (updating your question to add a specific query and hypothetical result would possibly lead to a more specific answer).
I've got a strange problem.
I'm trying to pull out data from GA API.
metrics: ga:users
dimensions: ga:date,ga:source,ga:medium,ga:transactionId
After reviewing the data I can see that I have multiple transaction Id's.
Usually 5 to 7 duplicates per month - the same transaction ID is in two dates.
In Google Analytics there are no duplicates.
There are in the exported data + Query Explorer also shows duplicates.
Does anybody know why?
Thanks,
Krzysztof
First of all, do you make sure you use unique transactionIDs for each transaction? I've seen cases where the ERP makes certain transaction or orderIDs available again after an order was cancelled.
If you look at the transactionID in GA (click in on the ID itself to drill down into it) and change to Quantity or look at the product revenue for the graph line, do they occur on two different dates?
This behaviour is often seen if you forget to prevent the transaction pixel again on things like a page refresh. Another example is if they perhaps receive an email with "Click here to view your order/transaction" and it fires again on the receipt page.
After how many clicks will the Core Reporting API start to sample the click data, when using samplingLevel=LARGE?
I'm trying to retrieve data from a large account (i.e. more than 30,000 clicks/day on average) and the number of clicks doesn't always match what I can see on Google Analytics. This, however, seems to happen only on this large account, and not every day. Strangely, on those days where the click count doesn't match, transactions and revenue match what I can see in Google Analytics.
In my query, I'm only trying to retrieve the data for a given account, without applying any filter.
EDIT: If I don't retrieve data aggregated at the account level ― thus not including ga:adwordsCampaignID, ga:adwordsAdGroupID and ga:adwordsCriteriaID in the dimensions ― I can retrieve all the clicks.
EDIT2: If add the ga:deviceCategory dimension, along with ga:adwordsCampaignID, ga:adwordsAdGroupID and ga:adwordsCriteriaID I can retrieve all clicks. I'm not sure if this can help narrow down the issue.
Google Analytics has a cardinality of 50K after that you ll receive (others)
Based on the "EDIT" i can safely assume the reason is that when a click doesn't have an 'ga:adwordsCampaignID' associated with it, it ll not retrieve that click. This happened to me with custom dimensions.
EDIT: Try using the 'include-empty-rows' parameter on your query. https://developers.google.com/analytics/devguides/reporting/core/v3/reference#includeEmptyRows
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.