The goal flow report on my google analytics account shows some strange sampling behavior. While I can usually select up to a month of data before sampling starts it seems to be different for the goal flow report.
As soon as I select more than one day of data the used data set is getting smaller very fast. At three days the report ist based on only 50% of the sessions, which, according to analytics, comes to only 35 sessions.
Has anyone experienced a similar behavior of sampling although only very small data-sets are used?
Sampling is induced when your request is calculation-intensive; there's no 'garunteed point at which it trips.
Goal flow complexity will increase exponentially as you add goals, so even a low number of goals might make this report demand a lot of processing.
Meanwhile you'll find that moast of the standard reports can cover large periods of time without sampling; they are preaggreated, so it's very cheap to load them.
If you want to know more about sampling, see here:
https://stackoverflow.com/a/37386181/5815149
Related
I am trying to automate the weekly report. Currently, I am using Google Analytics website to get the data for my report. Sampling level is higher precision.
I tried to get the same data by Google analytics API set samplingLevel as HIGHER_PRECISION. However, I am still getting the sampled data.
For FASTER, Precision Level is roughly 25% whereas for DEFAULT and HIGHER_PRECISION sampling level is roughly 50%.
On Google Analytics website, it says 'This report is based on 100% of sessions'. Can I get the same level of accuracy with Google API? I am using Google Apps script.Response for HIGHER_PRECISION is not matching.
Sumit, the API and the Google Analytics UI are certainly different and similarly the sampling's effect on things is a different beast which must be handled properly to get anything useful out of it.
As was mentioned in the comment, you can achieve high precision unsampled reports by (typically) shortening your date range that you're querying for and then "walking" the data.
To walk the data, you are essentially just gradually incrementing that small date range as you move through the desired data.
The "unsampled reports API" is... well, not the best. Considering that's what they are avoiding giving the end user in the first place, the offering available is not a very good long term or large project friendly solution. I would recommend small date ranges and then doing a data walk.
Happy Coding
There are several solutions to avoid sampling issue in Google Analytics by automating the process of data export for short date ranges.
I prefer this tool, it's pretty simple to use: MadStats.io
I'm having a tough time with Google Analytics, trying to understand why the value of metrics changes when segments are applied.
There is a standard audience overview report, which is based on 100% of sessions (no sampling) and the view is not filtered. The period is March of 2017.
Standard "All visitors" segment looks like this:
Then, there is another built-in segment called "Bounced Sessions". When I apply this segment, the "All visitors" values changes:
Amount of users increases, but the count of pageviews decreases.
Any ideas how to explain this?.. Thank you in advance!
Oki, there can be, multiple reasons. Let me explain first how these numbers are calculated, then we move on to your query.
There two types of data gathering and manipulation from google.
Pre-calculated data -- pre-aggregated tables
These are the precalculated data that Google uses to speed up the UI. Google does not specify when this is done but it can be at any point of the time. These are known as pre-aggregated tables
Data calculated on the fly
Some that you do which result in computation or manipulation falls under this category. Like using segments, creating custom reports etc.
Coming to your problem. When you apply segment, every metric that it effects will be calculated again. Thus it may result in numbers greater than you see in normal view.
Standard audience overview report is pre-aggregated at some point of the day. When you apply segment, the results will be calculated with the fresh data. Since latter is the latest, it will automatically give you increased number of the metrics. Even you can see a decrease as well, all depends on your data and user behavior.
Resolution: If you are a premium user, use Big Query. You must rely on big query for every metric as they are fresh and calculated on the fly
I'm trying to detect anomalies in google analytics events like page views or custom events.
I tested the custom alert feature from google itself. The period for those alerts are per day, week or month. What I'm looking for is a realtime detection. It would be useful to define rules for alerts like a maximum divergence between two points in time. For example [now, now - 15 minutes] or [now, now - 24 hours] or [now, now - 7 days]. Some solutions provide alerts when fixed threshold got passed (like observe.io). But thats not very helpful for highly fluctuating numbers that depend on weekday and daytime (like page views).
I would be thankful for any tips how to detect anomalies in GA in realtime.
I agree thereshold solutions is not a good idea for detect anomalies in time series. Because they generally are set by the user, rather than learned, which can be a time consuming and difficult process when monitoring many data streams.
Moreover, they need to be adjusted as the environment changes, so manual real-time maintenance is needed.
Besides, since they don’t take temporal sequences into account, simple thresholds cannot identify pattern changes that take place within the range. I recommend you use methods for anomaly detection in time series or change point detection.
You can googling about this topics and you'll find several algorithms. For realtime analisys, i also can recommend softwares like MOA (http://moa.cms.waikato.ac.nz/) and Numenta (https://numenta.com/).
Considering doing some relatively large scale event tracking on my website.
I estimate this would create up to 6 million new events per month in Google Analytics.
My questions are, would all of this extra data that I'm now hanging onto:
a) Slow down GA UI performance
and
b) Increase the amount of data sampling
Notes:
I have noticed that GA seems to be taking longer to retrieve results for longer timelines for my website lately, but I don't know if it has to do with the increased amount of event tracking I've been doing lately or not – it may be that GA is fighting for resources as it matures and as more and more people collect more and more data...
Finally, one might guess that adding events may only slow down reporting on events, but this isn't necessarily so is it?
Drewdavid,
The amount of data being loaded will influence the speed of GA performance, but nothing really dramatic I would say. I am running a website/app with 15+ million events per month and even though all the reporting is automated via API, every now and then we need to find something specific and use the regular GA UI.
More than speed I would be worried about sampling. That's the reason we automated the reporting in the first place as there are some ways how you can eliminate it (with some limitations. See this post for instance that describes using Analytics Canvas, one my of favorite tools (am not affiliated in any way :-).
Also, let me ask what would be the purpose of your events? Think twice if you would actually use them later on...
Slow down GA UI performance
Standard Reports are precompiled and will display as usual. Reports that are generated ad hoc (because you apply filters, segments etc.) will take a little longer, but not so much that it hurts.
Increase the amount of data sampling
If by "sampling" you mean throwing away raw data, Google does not do that (I actually have that in writing from a Google representative). However the reports might not be able to resolve all data points (e.g. you get Top 10 Keywords and everything else is lumped under "other").
However those events will count towards you data limit which is ten million interaction hits (pageviews, events, transactions, any single product in a transaction, user timings and possibly others). Google will not drop data or close your account without warning (again, I have that in writing from a Google Sales Manager) but they reserve to right to either force you to collect less interaction hits or to close your account some time after they issued a warning (actually they will ask you to upgrade to Premium first, but chances are you don't want to spend that much money).
Google is pretty lenient when it comes to violations of the data limit but other peoples leniency is not a good basis for a reliable service, so you want to make sure that you stay withing the limits.
I try to pull out the (unique) visitor count for a certain directory using three different methods:
* with a profile
* using an dynamic advanced segment
* using custom report filter
On a smaller site the three methods give the same result. But on the large site (> 5M visits/month) I get a big discrepancy between the profile on one hand and the advanced segment and filter on the other. This might be because of sampling - but the difference is smaller when it comes to pageviews. Is the estimation of visitors worse and the discrepancy bigger when using sampled data? Also when extracting data from the API (using filters or profiles) I still get DIFFERENT data even if GA doesn't indicate that the data is sampled - ie I'm looking at unsampled data.
Another strange thing is that the pageviews are higher in the profile than the filter, while the visitor count is higher for the filter vs the profile. I also applied a filter at the profile to force it to use sample data - and I again get quite similar results to the filter and segment-data.
profile filter segment filter#profile
unique 25550 37778 36433 37971
pageviews 202761 184130 n/a 202761
What I am trying to achieve is to find a way to get somewhat accurat data on unique visitors when I've run out of profiles to use.
More data with discrepancies can be found in this google docs: https://docs.google.com/spreadsheet/ccc?key=0Aqzq0UJQNY0XdG1DRFpaeWJveWhhdXZRemRlZ3pFb0E
Google Analytics (free version) tracks only 10 mio page interactions [0] (pageviews and events, any tracker method that start with "track" is an interaction) per month [1], so presumably the data for your larger site is already heavily sampled (I guess each of you 5 Million visitors has more than two interactions) [2]. Ad hoc reports use only 1 mio datapoints at max, so you have a sample of a sample. Naturally aggregated values suffer more from smaller sample sizes.
And I'm pretty sure the data limits apply to api access too (Google says that there is "no assurance that the excess hits will be processed"), so for the large site the api returns sampled (or incomplete) data, too - so you cannot really be looking at unsampled data.
As for the differences, I'd say that different ad hoc report use different samples so you end up with different results. With GA you shouldn't rely too much an absolute numbers anyway and look more for general trends.
[1] Analytics Premium tracks 50 mio interactions per month (and has support from Google) but comes at 150 000 USD per year
[2] Google suggests to use "_setSampleRate()" on large sites to make sure you have actually sampled data for each day of the month instead of random hit or miss after you exceed the data limits.
Data limits:
http://support.google.com/analytics/bin/answer.py?hl=en&answer=1070983).
setSampleRate:
https://developers.google.com/analytics/devguides/collection/gajs/methods/gaJSApiBasicConfiguration#_gat.GA_Tracker_._setSampleRate
Yes, the sampled data is less accurate, especially with visitor counts.
I've also seen them miss 500k pageviews over two days, only to see them appear in their reporting a few days later. It also doesn't surprise me to see different results from different interfaces. The quality of Google Analytics has diminished, even as they have tried to become more real-time. It appears that their codebase is inconsistent across API's, and their algorithms are all over the map.
I usually stick with the same metrics and reporting methods, so that my results remain comparable to one another. I also run GA in tandem with Gaug.es, as a validation and sanity check. With that extra data, I choose the reporting method in GA that I am most confident with and I rely on that exclusively.