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Google analytics users vs sessions: What is the difference?

When you are analyzing your campaigns in Google Analytics you will sometimes notice that you have to choose between using users or sessions. It’s important to know why you are using either one of those metrics over the other because there are differences between them. By looking at the definition and differences between these two metrics you will be able to make a better analysis because you will know which one fits your current situation. Let’s compare google analytics users vs sessions!

This article will contain what users and sessions are in Google Analytics and how they differ from each other. It will also discuss when you should use either one and where to find these metrics in Google Analytics.

What are sessions in Google Analytics?

Google defines a session in the following way:

Definition of a session

“The period of time a user is active on your site or app. By default, if a user is inactive for 30 minutes or more, any future activity is attributed to a new session. Users that leave your site and return within 30 minutes are counted as part of the original session.” – Google

This means every time a person visits your website he will trigger a new session if he or she hasn’t already been on the website for longer than 30 minutes. Then the already triggered session will continue. The 30-minute timer will have reset by then so the user needs to not visit the website for another 30 minutes for a new session to trigger.

How is a session defined in Google Analytics?

A session is a box filled with different kinds of metrics that are tied to a user. The user visits the website and the session will contain page views and events that are connected to the sessions. Google has a great illustration of this concept.

Google analytics session box

The user can have multiple sessions and every session can contain multiple page views. A new session starts when the previous one has ended. A session ends because of 3 reasons.

The first reason is when the user enters the website through a different campaign. Google Analytics analyzes that traffic comes from a specific campaign using UTM tags. When the user clicks on one of these links he will get a session tight to him with that specific UTM tag name. When a user clicks on a link with a different UTM tag to get back on the website he will start a new session.

Imagine someone getting an email with a Black Friday theme for example. They open the mail and click the link with the UTM source named BF. In google analytics, you will see a session with the connected campaign BF. The same day the receive a birthday mail with a UTM source BD. This will show up as a new session named BD.

The second reason is at midnight. I am not sure why exactly but when a user is browsing a website at 23:55 for example. He will begin a new session at 0:00. Meaning that without leaving the website he will have two sessions tied to the user. Probably because a session cannot be connected to two days!

The third and final reason is the 30-minute rule. When a user goes on the website and leaves it for 30 minutes he will always start a new session upon returning to the website. If he returns within 30 minutes he will continue the same session if the above two rules don’t apply.

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What are users in Google Analytics?

Definition of a user

Google Analytics tries to define users to one specific person. This is not perfect because of the way that users are defined, but it comes close. Just remember that a user can have multiple sessions and sessions can have multiple page views. You could consider it a box containing a bunch of sessions boxes.

How is a user-defined in Google Analytics?

Google defines a user in the following way:

“In order for Google Analytics to determine which traffic belongs to which user, a unique identifier associated with each user is sent with each hit. This identifier can be a single, first-party cookie named _ga that stores a Google Analytics client ID, or you can use the User-ID feature in conjunction with the client ID to more accurately identify users across all the devices they use to access your site or app.” – Google

An identifier is a combination of letters and numbers that identify the user. This is sent with each hit. A hit is when data is being sent to Google Analytics. 

So this means that a user is an identifier on one or multiple devices. This identifier is connected to specific sessions and send with each data hit. An identifier can have multiple forms but always tries to represent a user using their devices.

New versus returning users

This user identifier is used to create two sub metrics that are used frequently in Google Analytics analysis. When a user is identified for the first time he or she will be a new user.

When this same user with the same identifier comes back within 2 years he will be a returning user. When this person clears their cookies and comes back he will be a new user again.

Google analytics users vs sessions

So let’s break it down to a real-life situation a connect what is a user and what is a session.

Bob likes to buy hardware at a webshop called Stevehardware.com. When Bob decides he needs a new hammer he will go into the website. He finishes the transaction and leaves again. If he does not return within 30 minutes, or through a campaign, we will have a session connected to bob containing the metrics while buying a hammer. Sessions represent the visits of a user.

Bob bought the hammer on his desktop device and this device has a unique identifier. This device will then be the user because it’s tied to Bob. If userID functionality  is not activated he will not be recognized as the same user on a different device when he decides to buy a saw on a mobile device. This means that Bob’s devices are users and the visits he does with them are sessions.

When should you use users or sessions for ab testing?

It’s actually better to focus on users instead of sessions when evaluating tests because sessions are not independent like users are. This is because a user can have multiple sessions that all influence each other.

A study by towardsdatascience confirms this by showing that the number of sessions a user has will increase the conversion rate.

Graph showing the effect of sessions on conversion rate

This means that you are basically adding an extra variant within your experiment and making your analysis less reliable. When running an AA test for example. This is used to test your metrics and see if nothing is skewed. The same study from towardsdatascience.com shows that looking at sessions gives you skewed p-levels:

Skewed p-value because of sessions

While looking at user doesn’t give you a skewed result:

Stable p-level because of user evaluation

What this means is that you are increasing your false-positive rate the longer you run your test when evaluating with sessions. If you want to use sessions you will need to AA test to see what the effect of the session has on your data and apply the correction you need. But I recommend sticking to users when it’s possible!

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Google analytics users vs sessions in report

You can find the user and sessions in a lot of reports in Google Analytics but the one I always use is under Acquisition > All Traffic > Channels. This gives you both of them and also the transactions and conversion rate. Remember that conversion rate is based on sessions and you should calculate the conversion rate on users/transactions if you are looking at e-commerce transactions to minimize the session fallacy.

Google analytics acquisition report

Conclusion

So remember that using users over sessions will give you a smaller chance of getting a false positive so it can be in your best interest to evaluate both metrics and see if the results match. 

Good luck with your future ab testing endeavors! If you liked this post you can share with social media using any of the buttons below the article. Any questions or comments about this post can be left in the comment section below. Have a great day!

Want to learn how to analyze your ab test? Check out my guide on how to do this with Google Optimize and Google Analytics.

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