How to avoid sampling in Google Analytics?

Option 1: Work with standard Google Analytics reports. They are never sampled Option 2: Use short date ranges Option 3: Upgrade from Google Analytics to Google Analytics 360 Option 4: Use Windsor.ai

Sampling in Google Analytics: How to avoid it

Advanced Google Analytics users will have experienced sampling before. Especially when it comes to working with custom dimensions.

What are the ways to avoid Google Analytics Sampling In Data Studios?

Sampling is one of the biggest obstructions that most data analysts and marketers have to deal with. Google Analytics, although a great tool for marketers, unreliable data is something that all marketers should avoid at any cost.

In this post, we will discuss how sampling affects your data. Besides, you will also see how Windsor.ai can ease your sampling pain in the Data Studio.

Sampling with Google Analytics

Defining samples in data investigation is simply the practice of analyzing a subset of available data to highlight the most meaningful information present in the projected data set. Google Analytics can apply session sampling to your data and provides you with accurate reports on time. Especially; when you are attracting a large number of visitors each month.

Default reports

There are few default reports catalogs in the left pane under behavior, audience, acquisition, and conversions. Sampling doesn’t affect these reports.

Custom reports

When you start modifying your default reports or build custom ones, sampling might affect your data. Hence; you should be more careful when you are

Applying table filters
Generating custom reports
Applying custom segments
Smearing secondary dimensions

In the mentioned cases above, sampling might affect your data set.

Sampling Thresholds

A free version of google analytics is used by most people. Below given are the sampling thresholds of analytics 360 and analytics standard:

Analytics 360: 100M sessions view level for the specified date range.

Analytics standard (free): 500k sessions at the property level for the selected date range.

In general, the segmented data for at least four weeks can be segmented; without dealing with sampling.

Native data connector for google analytics

A beautiful data visualization can be built in a Data Studio. The native data connector for Google Analytics works impeccably; when there is no need to worry about sampling. However, when you compare the Google Analytics reporting environment with API, the same data sampling challenges apply to Data Studio. In brief, analyzing google analytics data in a data studio is not an answer to your sampling challenges.

Sampling effects

For a better understanding of how sampling affects your data, you may set up a test to compare sampled vs unsampled data. A small sample and low values on specific metrics may lead to bigger inaccuracies in your data.

It Is recommended to use Windsor.ai to find out how the different data sets are affected by sampling.

Case study: e-commerce site

Ecommerce, lead generation, and also services websites have to deal with sampling. Some companies often suffer selecting multiple years of data. But it won’t be a great threat as others can analyze data sets for seven days or even less. It is a bigger issue when we want to achieve trend analysis for a longer period.

Now, I will discuss a short story on how I used Windsor.ai to deal with sampling for one of my big clients. The configuration was done even before the new connector was introduced, which we will explain later.

Background

E-commerce companies function internationally and have millions of visitors. Although it was a popular and profit-making online sale platform, it didn’t want to convert to the GA 360 package. The company wanted to create a data studio dashboard for an easy track of the e-commerce performance. Overall, it is not difficult to get unsegmented data in the data studio. All that’s needed is just connecting the native Google Analytics connector to receive all the metrics and dimensions required.

Besides, they also desired to get an in-depth view of the goings-on of their e-commerce business. It is the point where we get into sampling challenges. A few segments are like

Visitors who show specific interest in buying a product
Visitors who are showing specific interest in repairing a product
Visitors who are navigating to the store locator page (it is a sign that they are more interested in offline buying)

As you may have already guessed, these reporting needs and sections have led to data sampling challenges. Hence, we decided to extract a basic set of metrics daily on the channel level.

Solution

We together discussed the different options to tackle the sampling challenges of the company. Also, I leveraged various functionalities of Windsor.ai as well as Google Sheets to solve the challenge.

Try Windsor.ai – 30 days free trial

Google explains sampling in the following way

In data analysis, sampling is the practice of analyzing a subset of all data in order to uncover the meaningful information in the larger data set. For example, if you wanted to estimate the number of trees in a 100-acre area where the distribution of trees was fairly uniform, you could count the number of trees in 1 acre and multiply by 100, or count the trees in a half acre and multiply by 200 to get an accurate representation of the entire 100 acres.

In terms of thresholds for sampling as of July 2021, Google states the following

Google Analytics Standard: 500k sessions at the property level for the date range you are using
In some circumstances, you may see fewer than 500k sessions sampled. This can result from the complexity of your Analytics implementation, the use of view filters, query complexity for segmentation, or some combination of those factors. Although we make a best effort to sample up to 500k sessions, it’s normal to sometimes see slightly fewer than 500k sessions returned for an ad-hoc query.

Google Analytics 360: 100M sessions at the view level for the date range you are using
360 thresholds vary according to how queries are configured. For detailed information, contact your 360 support team.

Now let’s have a look at what issues it causes and why relying on sampled data is such a problem when doing data analysis.

How to identify it and why is it a problem?

When looking at reports in Google Analytics you will usually see a green tick mark in the report similar. This means no sampling is applied.

google analytics unsampled

In case the tick mark appears in yellow, it means that you are looking at a sampled report.

google analytis sampled

Now why exactly is this a problem?

7.15% of the data (as shown in the screenshot above) will in no way tell you the true story of what is happening and will almost certainly be too small for even looking at trends.

If you are looking at a sample rate of 50%> it may help you to analyze demographics of your audience or similar high-level insights, but definitely will not help you if you want to do any kind of comparative analysis. If you make decisions based on sampled data you basically work with inaccurate data. These decisions can lead to:

A loss of trust in the data and risk of the reputation in the data/marketing team
A financial loss for the company as you make budgeting decisions based on incomplete data

So let’s explore the options you have to avoid sampled data.

Sampling: How to avoid it when working with data

Option 1: Work with standard Google Analytics reports

google analytis standard report

Google does not sample the standard reports in Google Analytics. This means you are safe from sampling when you look at any standard report. If you look at top level metrics this will be the way to go. Chances are however, that this will not suffice for you. Especially when you made it as far that you have a sampled view, I doubt that looking at these top level reports will bring you one step further ;-).

Option 2: Use short date ranges

google analytics date range

Another way to avoid sampling is to use a short date range. If you reduce monthly to weekly or even daily, the sampling will at one point disappear. This approach might work to look at very short date ranges but makes analysis of longer date ranges hard as you would need to export the reports into Google Sheet documents or CSV files and then somehow patch it together (which is a time waster you should probably avoid).

Option 3: Buy Google Analytics 360

As you can see in the opening paragraph of this article, the sampling threshold for Google Analytics 360 (aka Google Analytics Premium) is much higher (500k sessions on Google Analytics vs. 100m sessions on Google Analytics 360). The issue we see here is that it comes with a hefty price tag starting around $150k per year. Of course it not only comes with reduced sampling but also with other features but sampling is clearly the most important feature.

Option 4: Use Windsor.ai

Another option is to use Windsor.ai to extract all your data upsampled. For those looking at media KPI’s it also connects the upsampled data to the costs from your various sources (Google Ads, Facebook, Bing, LinkedIn, DCM, …) and makes it available for you to work with in raw format via API, Google Data Studio, Microsoft PowerBI or our own dashboard.

The steps to get started for free are:

Connect your Google Analytics and your costs data here
Load data for a date range of 20 – 30 days to get your upsampled insights insights
Setup your dashboard in the platform of your choice (links above) and analyse data
(Optional) Customize the setup to connect your Google Analytics data with your CRM or e-Commerce data or enable pulling and visualization of custom dimensions from your Google Analytics setup

Conclusion

It depends greatly on your technical abilities and your wallet to what option you choose. The most important takeaway which I’m sure you understood by now is that making decisions based on sampled data leads to many problems.

If you have another way of tackling this problem feel free to share it with us.