Differences between Facebook and Google Analytics data

Meta vs Google Analytics

Digital marketing is an integral part of modern business, enabling companies to reach target audiences at the right time, in the right place, with targeted messages, thus creating a stronger connection with the consumer and increasing the value of brand awareness. One of the key areas of digital marketing is advertising. Today, it is about it, or more specifically Meta advertising, and a common headache: data disparity. At the moment, I can say quite confidently that some of the most popular advertising analytics tools, at least in Lithuania, are Facebook Ads Manager and Google Analytics. These two platforms provide quite different data on user behaviour after interacting with advertising campaigns, on the actions taken by users on the website to which they were redirected after clicking on the ad or on the website they visited using other traffic channels. I want to start my first blog post on this topic because, personally, I am still dealing with people who are quite categorical and who tend to discredit the results of Facebook advertising on the basis of GA4 data alone. So, I will try to explain why these differences occur and how they can affect the results of digital advertising campaigns. I will also give practical tips that I think should help to reduce these differences.

How differences in data affect advertising campaigns

Can data differences between Facebook Ads Manager and Google Analytics (GA) have a significant impact on digital advertising campaigns? It can! Although these data discrepancies are quite common, it is very important to index and track the bias of the displayed data. If the difference in data is very large, such as for example: on a Facebook advertising account you see that a campaign generated sales of €10,000 and GA only shows a few hundred euros, it seems that we have a problem.

Here are some of the main reasons why these differences are important:

Budget management: Different measurement methods can lead to erroneous budget allocation decisions. For example, if Facebook shows a higher ROI than GA, it may seem worth investing more in Facebook ads. If these figures are inaccurate or misinterpreted, this will lead to inefficient use of the budget.

Attribution modelling: Facebook and GA use different attribution models. Facebook tends to give credit to any ad that was seen or clicked before conversion, while GA may use last point of contact attribution or more complex models. A misunderstanding of these differences can lead to erroneous conclusions about which advertising campaigns are most effective.

Conversion tracking: Different conversion tracking methods can lead to inconsistencies in the data, e.g. Facebook indexes "soft" conversions such as page views or time spent on site as signs of success, while GA focuses only on "hard" conversions such as sales. This can lead to a misjudgement of the effectiveness of a campaign.

Understanding the audience: Facebook provides detailed information about user demographics and interests based on its own data. If this data is not consistent with GA reports, it can cause problems in understanding your audience's true behaviour.

Optimisation strategies: Inaccurate or divergent data can lead to incorrect optimisation decisions, for example by wrongly modifying campaign content, targets or budgets based on faulty data analysis.

How Meta and Google attribution models affect data reporting

Facebook Ads Manager and Google Analytics use different attribution models, so it's important to look at how they work in order to see the bigger picture.

Facebook attribution models:

1) 7 Day Click - 1 Day View: this model attributes a conversion if the user has clicked on an ad in the last 7 days or seen it in the last 24 hours before the conversion action. This means that any conversion made by the user (purchase, add to cart, etc.) within this period will be attributed to the Facebook campaign.

2) 7 Day Click: this model only attributes a conversion to Facebook if the user clicked on the ad, was redirected to the website and performed a conversion action within 7 days.

3)1 Day Click: A conversion is attributed to Facebook if the user clicked on the ad and performed a conversion action within 24 hours after the click.

Attribution models used by Google:

1) Data Driven: this model uses artificial intelligence algorithms to determine how different marketing channels and touch points contributed to the final conversion. It analyses data on which actions were most likely to drive conversions and assigns value accordingly.

2) Last Click: The last click model assigns the total conversion value to the last traffic channel through which the user came to the website before converting. This means that if a user last used Google Search before making a purchase, the entire conversion value will be attributed to this traffic channel.

How to reduce the differences between Meta and GA data

Understand and compare the attribution models used: It is important to know which attribution models each platform uses and to take this into account when analysing the data.

Set up similar attribution windows on both platforms: As far as possible, try to align the attribution windows in Meta and Google Analytics to make the data comparison more accurate.

Experiment with different attribution models in GA: By experimenting with the attribution models offered by Google Analytics, you can learn about how different touchpoints contribute to conversions, and perhaps find the model that will give you the most accurate representation of Facebook campaign data.

Frequent data monitoring and correction: Regularly review and compare data from both platforms to quickly spot and correct discrepancies, improving data accuracy and the effectiveness of your marketing efforts.

Practical example: differences between Facebook campaign and GA data

Imagine you are managing a Facebook advertising campaign aimed at selling goods/services on a website. The campaign was created using the 7 Day Click - 1 Day View attribution model. One week later, we can see in the Facebook advertising account that the advertisement has generated 50 sales, but when we compare the results of the same campaign using Google Analytics, we see that only 30 conversions are displayed there. Why is this happening?

Situation: a consumer sees an advertisement for product X on the Facebook app and clicks on it, but does not make a purchase during this visit to the website. Three days later, the same consumer returns to the website using Google organic search and makes a purchase during this visit. Facebook Ads Manager will attribute this conversion to the Facebook ad based on the 7 Day Click - 1 Day View attribution model, as the purchase action took place within 7 days after the ad was clicked. Google Analytics, meanwhile, will attribute this conversion to the organic search traffic channel, as this was the last channel through which the user came directly to the website before the purchase action.

Indexing the problem:

Different attribution models: the Facebook "7 Day Click - 1 Day View" model measures clicks and views. Google Analytics indexes the last traffic channel through which the .

Diversity of user behaviour: users may return to the website in different ways, which are interpreted differently by each platform in the case of conversions.

How does this situation affect the budget of an advertising campaign? Significant differences in data can make it challenging to assess the effectiveness of a campaign and the financial return generated. What's more, with each sale, Facebook Pixel learns to identify the potential user, potentially assign them to a specific audience, optimise the cost of the sale, and a whole host of other things, but more on that another time.

Solutions:

Integrate data from both platforms using third-party tools.

Identify and analyse different users' website journeys and their impact on conversions, based on data from both platforms.

Adjust advertising strategies based on detailed data, optimising budget allocation and campaign effectiveness.

Application of UTM parameters

In order to obtain more accurate data on the journey of Meta users and analyse it in GA4 , one of the most efficient ways is to use the UTM (Urchin Tracking Module) parameters. Including UTM parameters in the URL of the Facebook advertising campaign (the link to which the user is redirected after clicking on the ad). These parameters index the traffic source (source), medium (medium), campaign name (campaign), content (content) and keywords (term). Adding UTM parameters to the URL links of Facebook ads not only allows you to see the number of traffic counts, but also allows you to analyse the results of different Meta-campaigns, ad groups and specific visuals to determine which one was the most effective. Also, the use of UTM parameters helps to better understand user behaviour, their journey and the demographics of visitors.

Facebook Conversion API

The Facebook Conversion API (CAPI) is an additional Facebook tool that sends events directly from the website's server to Facebook, bypassing client-side tracking technologies such as coockies. This is particularly useful due to current privacy laws and individual browser technologies that restrict the use of third-party cookies. Server-side tracking helps to maintain data accuracy and reliability as the data transmitted is less dependent on the user's browser actions.

The benefits of the Facebook Conversion API are twofold: firstly, it improves the accuracy of the data, as the tracking takes place at the server level, so the data is less affected by changes in the user's browser environment. Secondly, it allows the creation of more accurate, personalised advertising campaigns by tracking more complex user behaviours and conversions that may not have been tracked before. In addition, CAPI helps to reduce data inconsistencies between Facebook and other analytics platforms, enabling more effective management of privacy policy challenges and ensuring that key events are captured and communicated.

So, by understanding the different principles of attribution and using these two methods - UTM parameters and Facebook Conversion API - you can significantly improve the quality of the data you receive and analyse data from two different data sources to optimise your digital marketing strategies.

My first pancake. Thank you for your time.