I'm looking to use google analytics for its web interface only. A large dataset such as gasoline prices would be submitted to analytics via the api and viewed. Is this possible? Or is analytics purely tailored to viewing website statistics?
The Google Analytics data model is really geared toward datasets that can be thought of in terms of users, sessions, and hits (hits being things like pageviews and events).
If your data can be thought of in these terms, it will probably work. If, on the other hand, you're trying to do things like joins or calculate averages or other statistical operations, you're probably better of using something else.
While the others are correct, Google Analytics is geared towards users, sessions, and hits. It is none the less simply an application for data analysis. The question will be how to get the data into the system.
I think you need to give us a little more information about your data set. But let me assume a few things.
You have a dataset with gasoline prices over a period of days.
you have a dataset with gasoline prices for different gas stations.
It would be really nice if this wasn't old data that this is new gas prices coming in.
If I had this dataset I could insert it into Google Analytics. Directly using the measurement protocol.
The measurement protocol has a few required things, the first being hit type. 'pageview', 'screenview', 'event', 'transaction', 'item', 'social', 'exception', 'timing'. the second would cid or session id.
Now cid I think I would probably set to the different gas stations and probably add a custom dimension with the gas station name.
As for hit I think I would probably say screenview and make an application Google Analytics account. Mainly because well this isn't a website its a little different.
Then every time the price of Gas changes I would send a screenview, cid of the station with the custom dimension of the station, add a custom metric with the price.
The main problem you are going to have is that Google analytics doesn't handle old data well. If you are going to insert this data with a date associated the date and time cant be grater then 4 hours ago or the server wont process it.
Have you considered putting it in big Query instead?
This question really is to broad or opinion based, but it was fun to consider.
It is possible to send all kinds of hits with the Measurement Protocol. But Philip is correct in stating that the data model is largely geared towards users, sessions and hits. But you could probably get a good ways with custom dimensions and metrics.
Related
Our main challenge in Google Analytics at the time is to measure the success of our magazine articles.
The problem is that views grow over time so in any timeframe we always have the older articles overshadowing the newer ones. Sidenote: The same problem occurs for measuring social media post success.
My idea of a solution is to measure the rate by which views on articles grow. An article that has a higher growth of views is much more successful than an older article with more views, but with a lower growth rate.
Alternatively something like "views within the first week(s) of publishing this individual article" would also be a good metric.
Unfortunately to some extent also the growth rates rely on this publishing period of individual articles if we are interested in an eternal high score of articles. But since we are mainly interested in recent articles, growth rate would still give us the desired result of showing the most successful recent articles.
Has anyone dealt with the same challenges and found any solution to this, in best case with Google Analytics?
These examples may help, of which I have direct experience.
In the data layer we included a date of publication for the article and then used this to determine growth. This was taken from the CRM and was relatively straightforwards for the dev team. This was stored as a custom dimension in Google Analytics.
We had nothing in the data layer but instead a I just used the date on which page views started appearing as a proxy for date of publication. Not entirely reliable, and you may want to filter by views >5, or whatever is appropriate, to avoid any hits from editors or staff before a page is visible in the site navigation.
In both cases I was exporting data either to Google Sheets (using for example the Google Analytics API addon for sheets) or BigQuery, where it was relatively straight forwards to identify the first date and then calculate, for example, views per day. In your case it would be having a function which looks at the date of publication + 7 days. You may also be able to achieve this with Google Data Studio or similar dashboarding platform.
I work for a non-profit that needs to see how our fundraising efforts are going in 'real-time'.
We look at results in blocks of about a half hour - so we need to report on how we finished the last 24 hours or so and also where we're at in the current half-hour. We're accomplishing this through google analytics, as we have multiple fundraising streams all pointing to a common GA account.
I have tried using datastudio to report against the GA API, but that connector does not seem to refresh at a reliable rate - someitmes it'll pull fresh data within a minute, sometimes it can take twenty minutes to report on recent transactions. I believe the 'real-time' API could be used to get fresher GA data, but as far as I can tell, that will only report 'live' data, and not prior/historical data (say from four hours ago). Does anyone know what API I could use if any to pull all data historical through current datetime?
I apologize if this request is vague, but I'm just looking for a conceptual approach at this point to get the freshest data - preferably in one fell swoop (API call). There is more complexity post-data intake (I have to then compare it to goals we've set for each half-hour, amongst other nuances to the transacitons themselves), so i wanted to start with this fundamental piece/question.
Thanks!
Given the context provided, I believe that the API solution would not be feasible. Among other reasons:
The real time API only offers a limited amount of dimensions and metrics. For example, e-commerce data is not available.
https://ga-dev-tools.appspot.com/dimensions-metrics-explorer/
https://developers.google.com/analytics/devguides/reporting/realtime/dimsmets
The Standard intraday processing SLA for the Core Reporting API is < 24 hours for standard properties. The processing occurs on a best effort basis. Meaning that an hourly availability can occur from time to time but can not be guaranteed.
https://support.google.com/analytics/answer/7084038?hl=en
As an alternative approach to the API solution, you could consider the use of an App + Web property which would allow you to stream event data in real time to BigQuery. However, this solution has some cost implications and would introduce you to a new tracking paradigm.
https://developers.google.com/analytics/devguides/collection/app-web/tag-guide
https://support.google.com/firebase/answer/6318765?hl=en
https://www.simoahava.com/analytics/getting-started-with-google-analytics-app-web/
This seems really basic but i am struggling with it
We have a client who runs a travel website.
They have a few different search bars eg Flights, Hotels, Carhire.
I am trying to track the performance of each... "What % of people completed a sale that ran a Flight search." Same for Hotel, and for Car hire
Any ideas for the best way to get this info in GA?
Many thanks
There are a few ways to get this information, each with their pros and cons. The options that I see immediately available are segments and goals.
Segments are great because they are retrospective and generally more flexible, with the ability to be changed if you find your criteria isn't quite right. You create here, and specify sessions that go through search results pages etc:
Then you can create another segment for booking confirmation page, and any other intermediary steps that you'd like to report on. The main con of segments is that you can only pull in 4 at a time, but if you have more you can pull them 4 at a time and copy+paste the data into an excel sheet or google sheet. Segments can also be pulled via the Core Reporting Api and DataStudio which makes them great for automating into dashboards.
Goals are cool because they pull into the default reports, and basically track sessions through a particular page, event or sequence. The main con I see and the reason is that I don't use them is that they only start tracking fro mthe time you create them , and if you change the configuration it does not impact historical data, so your data can get messed up quickly if you don't have sandbox GA views or sandbox goals for your testing before putting it into a dedicated goal slot. You can also only have 10 or 20 goals depending on your plan, so once data is tracked against that goal you can't remove or clear it.
I've been using a SSIS Integration component to download data from Google Analytics in order to keep an historical view of some websites and track the evolution of them. Basically the metrics we track are Visits (now Sessions) and Visitros (now Users), and the dimensions are Year and Month. However, today I noticed that the data I downloaded for july had a variation on the Users metric. I heard that google analytics uses an estimation method to "calculate" some (if not all) of their metrics, could it be that after that they "adjust" the data with more acurate information? If so, is this mentioned in the documentation? (a link would be highly appreciated) Since the users are complaining that we are not delivering the real GA Data. I tried looked on the Google analytics documentation page with no luck.
Thanks for your time.
PS: Sorry for my english, it isn´t my native language
If you are using the standard version of Google Analytics (you'll know if you are paying $150k for premium), data is sampled depending on volume. Have a read of this article can-you-trust-your-google-analytics-data
I have seen very slightly differing results being returned if you repeatedly call the api with the same historical parameters repeatedly. In my case the figures only differed by 1-2 over a daily set of several thousand, but nevertheless it differed.
If you want to guarantee your results, consider upgrading to premium
Sampling could be an issue if what you are requesting is over 50,000 rows for the time period you are requesting. To avoid it you can download more often, such as daily.
But I think your issue is that there is a processing time for Google Analytics - if you are downloading at 3 am on the 1st it is probable that the processing for the previous day has not finished.
Google Analytics Premium SLA is for 4 hour data freshness, so even that would have trouble. Pragmatically you should allow 24 hours before you download data for the previous day, 48 hours for e-commerce data.
Thirdly make sure it is not Unique Visitors you are requesting, as this is dependent on the time period you are requesting.
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