Avoid Discrepancy of Facebook Ads & Google Analytics Data

Today, Artyom Pervukhin from Kinetica shared his experience with Facebook Ads Manager and Google Analytics Data Discrepancy.

The number of users and conversions in these systems might differ because their counting algorithms are different. Let's find out what’s the difference and what should we do with it.

Number of users

As a rule, every person has several devices to see the ads: laptop, smartphone, tablet. If cross-platform tracking is not configured in Google Analytics, then the person who logged in from these devices will be counted three times.

In our experience, there is one real person behind two users in Google Analytics

Facebook counts people by their ID, so in Ads Manager, we see the real number of people who clicked on the link. Each user counts as one.

Let's compare the following reports. Google Analytics Report:

We are interested in the "Users" tab

And Ads Manager:

Let's look at "Unique clicks on the link"

In the Facebook report, the data is 2 times lower than in GA for similar campaigns.

Furthermore, Facebook only works with sites with HTTPS. If you have HTTP installed on your site, the social network will not transmit click data.

Number of conversions

This indicator differs due to the principle of user tracking: web analytics counts conversion only by the last indirect click but does not see users viewing ads. Facebook counts conversions taking into account attribution and views in the feed.

For example, the client saw an ad 4 days ago on an Instagram feed and did not click on it. Yesterday the client made a purchase on the website by going to it through a search from a computer at home.

Facebook will count this conversion, but web analytics services will not, because they do not have access to Facebook analytics. Knowing this, it is possible to draw conclusions about how many users make purchases from Facebook ads and from which platforms they come to the site.

How to avoid data discrepancies?

Discrepant metrics can ruin the results of analysis and lead to ineffective campaigns. Without a granular picture of marketing metrics, analysts can’t track advertising performance properly and optimize campaigns to target the desired audience.

To avoid data discrepancies, marketers should normalize insights before getting to the actual analysis. The data harmonization process involves the unification of naming conventions, cleaning data sets from duplicates, merging metrics from disparate sources, and so on.

Manual data normalization is often a time-consuming process, especially if marketers have to deal with tens of sources. Besides, it may lead to human mistakes and data integrity issues. Automated solutions help to overcome this issue.

Solutions like Fivetran or Improvado’s Marketing Common Data Model fully automate the process of data normalization. With automated data mapping, custom naming conventions, and autonomous metrics unification, marketers don’t waste time on repetitive tasks. Instead, they can focus on the analysis of insights provided by algorithms and spend more time on campaign optimization.

Learn how Improvado helps to deal with data discrepancies