#LifeHack: Why and how to avoid sampling in your campaign data

We can observe that the message next to the orange badge in the report with the  Ad Content  shows that the report is sampled from 30.39% of the sessions. The report with  Source / Medium  is not sampled, because the shield remained green.

We can also see that the sampling has the effect of altering the number of users: the real number is 288,531 in the report on the right, while this number is overestimated in the report on the left at 292,757. this difference does not seem major at first glance (4,226 users), the sampling is harmful on several levels:

• The sampling process is not constant: it may vary if you generate your analysis this morning, tonight or tomorrow. Thus, the analysis is not reliable because the same query varies over time.
• The difference can be quite small on the totals, on the other hand it can be much larger on the smaller sets such as small campaigns or sub-campaigns.
• The different indicators do not vary to the same extent: sampling lowers your sessions by 15% but your income by 30%.

If you are lucky enough to have access to a Google Analytics Premium account, then you can download an unsampled report. But if you are among the vast majority of Google Analytics users, you have no choice but to reduce or break up your date ranges to lower the  sampling rate  to a reasonable percentage. So, the result is a loss of time, more manual manipulations, not to mention non-additionable metrics, such as users for example, which complicate the analysis.

The answer is by using the full potential of the three mandatory parameters that each session has in Google Analytics. I emphasize  all  sessions here, because even sessions that are not part of a campaign have a  utm_campaign parameter  : the value is then assigned to ( not set ).

Let’s review these three parameters:

1 –  utm_medium  : it is not recommended to customize this parameter  given its crucial importance in the definition of  channel grouping  (unless you customize your  channel grouping .)

2 –  utm_source  : customization of this parameter is possible but not recommended given the importance of the name of the site / network (Facebook, Google, Adwords) in the analysis of a campaign. Not to mention that this parameter is already formatted in a particular way in the default script of other  source utms  in your Google Analytics system. Ideally, as with all other sources of your  analytics , it is best to limit yourself to the site your user was on before going to yours. An example: you want to compare the performance of your sponsored posts on Facebook with that of organic posts. If you keep an utm of type:  utm_source=facebook , utm_medium=cpc  and  utm_campaign=mysupercampaign , you can then easily do this analysis by selecting filter the source in your report. If, on the other hand, the value of the  utm_source  is personalized ( facebook_campagneXcreatifY ), your analysis may be more complicated because the number of variations of your source parameter for your traffic identified by a utm may be significant.

3 –  utm_campaign  : it is strongly recommended to personalize it, it exists and was created for this purpose. Many combinations for the same campaign don't look  pretty  in your report by campaign because the results for one campaign are split across multiple rows. But on the other hand, thanks to a rigorous nomenclature, you can easily filter your results both in Google Analytics and in a visualization platform like Google Data Studio, Datorama or Microsoft Power BI,  without any sampling .

In conclusion, it is important to insist on two points:

• Another major advantage of concentrating as much information as possible in the utm campaign / source / medium trio is that as this data is located in the pre-aggregated reports, they will not be affected in the event of a management error on the retention of data. In other words, you won't lose any data if some of your account is erased by the implementation of the new data retention settings . 

• Adopting a clear nomenclature and limiting the sampling as much as possible in your campaign structures is one of the central pillars of effective reporting, especially since the sampling can sometimes not be seen directly, especially when the data is processed. in viewing platforms.

Good analysis!