When running a Facebook Ads campaign and analyzing its results in both the Ads Manager dashboard and Google Analytics, you may notice varying degrees of discrepancy in the reported data. While small discrepancies generally don't cause major concern, larger ones can raise suspicions. Does this mean one of the tools isn't working properly? Most often, it isn't. Below, we'll try to explain the possible causes of these discrepancies.
Analyzing the discrepancy between clicks and user sessions
The first differences between Google Analytics and Facebook statistics are visible in the way clicks and user sessions are counted. So let's start by explaining these concepts.
- Click is a metric that aggregates all user interactions with a given ad creative. So, if you click an ad once and then click again a moment later, Analytics will count that as two ad interactions.
- A session, in turn, shows how many users clicked on an ad. The system considers two or more clicks by the same user to be a single session, provided they occur within a 30-minute time window. Only when the time interval between clicks is longer will the actions be considered two separate sessions.
Up to this point, everything is quite straightforward, and it might seem there's no room for discrepancies in the counts. But there is. At what point can you start counting users who actually interacted with the ad? Google Analytics claims that only when the user lands on the page the ad referred them to. They could have accidentally clicked the ad and stopped it loading, or closed the browser tab before the landing page fully loaded, and such an action is hardly considered a success. Therefore, such "incomplete" transitions are not counted in Google Analytics.
Facebook, on the other hand, will record every click—i.e., every user interaction with the ad—even if the viewer doesn't fully view the landing page or doesn't leave the Facebook platform at all. Ads Manager allows you to measure not only all clicks, but also clicks on links that lead off the platform to the advertiser's website. However, Facebook's system will still count clicks that didn't fully load the landing page. So, what's the conclusion? The number of clicks reported in Facebook Ads Manager may be higher than what's shown in Google Analytics.
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How does cross-device user tracking work?
Analytics is therefore significantly more accurate when it comes to counting sessions and clicks at the single device level. But what about situations where a user uses different devices and clicks on an ad, for example, first on a phone and then on a computer? Facebook handles this much better. This is because to use Facebook, and therefore click on any ad there, the user must be logged in to their account. Most users use the same account across all devices, so the Facebook algorithm has no problem distinguishing between the same user and the same user.
Google Analytics uses cookies as its default data collection method, and cookies are assigned to a specific device. Therefore, each time a user changes devices, they are classified as a new user.
If a viewer clicks on an ad first on a phone and then engages with it again on a computer, Google Analytics will record them as two separate users from different traffic sources. However, Facebook, if the viewer is logged in with the same account on both devices, will recognize them as the same person.
Differences in attribution models
A conversion can be preceded by a variety of user actions. This means that before they complete a goal, such as purchasing an item from an online store, they may interact with your website in various ways. Let's see this with an example. A user sees an ad for shoes from your store on Facebook. They click on it, look at the shoes, but decide not to buy. A few days later, they decide they need shoes and enter a query into Google, receiving a search ad with a link to your website. They click the link, look at the same shoes again, but decide to ask their wife for her opinion before buying. In the evening, they enter the query again, but instead of clicking the sponsored link, they choose the organic result, and this time they buy the shoes.
So which interaction with the website can this purchase be attributed to? Was the user's initial exposure to the offer, i.e., the Facebook ad , the decisive factor in achieving the intended conversion ? Or perhaps the user forgot about it when searching for shoes in the search engine? In that case, the sponsored link would have led them to your store and made the purchase. But they didn't buy the shoes by accessing the website through the ad link, but through an organic search result, so perhaps they should be "responsible" for the conversion? On the other hand, it's possible that the user initially had no intention of buying the shoes, and it was the Facebook ad that ignited this need. In this situation, the first ad would have played a role in convincing them to make the purchase.
Questions are constantly multiplying, and it's difficult to find a clear answer. Therefore, different analytical tools may adopt different methods for measuring ad effectiveness, meaning they have different attribution models. We'll now show how this works in the case of Facebook Ads Manager and Google Analytics.
Facebook Ads – how to interpret the attribution model in campaigns?
If a user converts on the website within seven days of clicking on an ad, Facebook Ads will attribute the conversion to their ad (assuming the Facebook pixel is properly configured). The same will happen if the recipient completes the goal within one day of being shown the ad. While the user may have subsequently encountered ads on other channels, Facebook will still attribute the success. This is currently the default attribution model for this channel, which may raise some concerns, but it has its merits. Facebook isn't strictly a sales channel, so the purpose of ads on this platform is to stimulate the user's desire to buy. So, even if the ad didn't lead to a purchase, it planted a seed in the user's mind that later germinated and bore fruit, then this is also partly due to the Facebook ad.
For example, the recipient might have wanted to compare competitor prices before making a purchase, or search for reviews of a featured product, ultimately landing directly on the company's website. However, they found the company through a Facebook ad, so the system correctly recognizes their contribution to the conversion. Furthermore, Facebook's system cannot monitor whether the user subsequently encountered ads created by other systems, so assuming that the Facebook Ads campaign influenced the recipient's decision is the best way to calculate conversions.
But is it reliable? Unfortunately, no. Clicking on an ad doesn't automatically load the landing page for this system, as we've already discussed. It's even more difficult when counting impressions. Facebook's algorithms take into account whether an ad was displayed to the user, but they don't measure the time it was visible on the user's screen. It might turn out that, yes, it appeared on the page, but scrolled so quickly that the user didn't even have a chance to notice it, let alone read its content. Nevertheless, if the user later converts within the appropriate timeframe, Facebook will count it as a conversion from their ad.
Attribution models in Google Analytics
Google Analytics uses a different attribution model by default – the last indirect visit is counted as the one that resulted in a conversion. What is an indirect visit? Simply put, it can be any way to access a website except directly entering its address in the browser. So, for example, if a user clicks a Google Ads ad but doesn't purchase anything, then visits the website by entering its address in their browser and converts on the website, Google Ads will take credit for that conversion. Comparing this accounting system to Facebook's system, it's easy to see that Facebook Ads campaign results in Analytics may be weaker than in Facebook's statistics.
However, Google Analytics also offers other attribution models, and changing the default settings can significantly alter the results presented by the tool. Models to choose from include:
- Last interaction – then the system assigns full credit for the conversion to the channel the user used immediately before the conversion. This setting makes sense when the ad is targeted at people who are already committed to making a purchase, not those at earlier stages of the purchase funnel.
- Google Ads Last Click – assigns 100% of the "responsibility" for the conversion to the last ad displayed by the Google Ads system, which the recipient clicked on before converting. This model is only worth using when you want to compare the effectiveness Google Ads ads. Does not apply to statistics for Facebook ads.
- First interaction – In this model, Analytics assigns full credit for a conversion to the channel through which the user first encountered our website. This can be used when advertising is used to build brand awareness.
- Linear – considers that one conversion equals 100%, and gives equal credit to all channels through which the user encountered the website. So, if the user first saw a Facebook ad, then clicked a Google Ads sponsored link and made a purchase, it will attribute 50% of the value of that one conversion to the Facebook Ads ad, and the other 50% Google Ads advertisingIt's best to use this model when every user interaction with the website is important to us.
- Timetable – assigns different conversion contributions to channels based on how many days prior to conversion the user interacted with them. The closer in time the interaction with a given channel is, the more important that channel is.
- Taking into account the position – This model also divides credit among channels included in the purchase journey, but it doesn't take time into account. Typically, this model assigns 40% of the conversion value to the first interaction, 40% to the last interaction, and the remaining 20% is distributed equally among the remaining channels in the purchase journey.
Google Analytics or Facebook Ads – which statistics should you consider?
Advertisers often strive to standardize the results they receive from both tools as much as possible. This can be achieved by, for example, changing the attribution in Facebook Ads so that conversions are only counted within one day of an ad click. However, before taking such steps, it's worth analyzing your business and industry and tracking the average conversion path length of your audience. Sometimes, it's not necessarily about fixing discrepancies, but rather about accepting that both Google Analytics and Facebook are complementary tools that can be used in parallel to better understand your business through web analytics and make better marketing decisions.
Frequently asked questions about data discrepancies
No. While larger discrepancies may be concerning, they generally don't mean that any of the tools are malfunctioning. These platforms simply measure performance differently, adopt different metric definitions, and rely on different sales credit models (attribution models).
Facebook relies on clicks—it counts each interaction with an ad, even one that happens in rapid succession, as a separate event. Google Analytics analyzes traffic using sessions. If a user clicks your ad twice (or more) within a short period of time (a 30-minute window), Google considers it a single session.
This is due to the difference in the timing of the event registration. Facebook records an interaction the very second the ad is clicked. Analytics, on the other hand, will only count a visit once the user has landed on the website and the landing page has loaded correctly. If a user accidentally clicks on an ad and quickly closes the tab (before the page loads), Facebook will count it as a click, while Google will not consider this "incomplete" transition to have occurred.
Facebook uses an aggressive attribution model. Thanks to the pixel, it will attribute a completed goal to its ad if the user makes a purchase within seven days of clicking the banner or within one day of viewing it—regardless of what other ads the customer has used in the meantime.
Google Analytics has a completely different approach by default – it typically assigns 100% of the credit for a conversion to the last indirect visit (the so-called last interaction). If a user clicks on a Facebook ad on the first day but makes a purchase the next day by clicking on a link in Google, Analytics will credit the search engine with "success." Facebook, in turn, will consider the user to have brought in the customer. Both platforms are telling the truth, but they look at the purchase path from different perspectives.
Summary
The above article covers the following topics:
- Facebook counts every ad click even without the page fully loaded, while Google Analytics only counts sessions with a fully loaded landing page.
- Facebook tracks users across devices through account logins, while Google Analytics relies on device cookies, treating the same person as different users.
- Attribution models differ in their approach to attributing conversions – Facebook and Google Analytics give credit to different interactions in the user's purchase journey.
- Facebook Ads attributes a conversion to an ad if it occurs within 7 days of a click or 1 day of a view, even if the user has been exposed to other ads.
- Google Analytics uses the "last non-direct entry" model by default, but offers 6 other models.
- Both tools are complementary analytical systems – instead of unifying the results, it is worth treating them as different perspectives.
