The most important aspect of any marketing initiative is measurement. Measurement on Facebook ads takes several different forms, which tends to trip up advertisers as they begin to test paid media on Facebook. Below, we will cover everything you need to know about Facebook attribution to help your Facebook advertising efforts “measure up!”

Quick Note on Attribution


When we discuss attribution, we’re referring to the model in which the Facebook platform assigns conversions. The reporting you see in Facebook uses a “day-of-click” and “day-of-view” attribution model, which means every conversion is attributed to the day the user last clicked or viewed the Facebook ad that lead to each conversion.

This attribution model comes with a challenge that we at Metric Theory refer to as “backfill.” (Here’s more background on Facebook conversion backfill.) Briefly, backfill refers to a marketing data phenomenon of day-of-click/view attribution that a day that has already passed will continue to generate conversions as time goes on, due to users viewing or clicking on ads one day, but not converting until one or many days later. An example of this would be if a user viewed an ad on October 6th, then took some time to process their decision, and then ultimately converted on October 11th. If you looked at your results for October 6th on October 8th you would notice that you did not have any conversions. Yet, when you check your results for October 6th on October 12th you would see one conversion for October 6th.

Click-Through Attribution


Facebook allows you to track both click-through and view-through conversions. In order for a conversion to be attributed to your ad in a click attribution model, the user must have clicked an ad and then completed the conversion action in a defined time window set by you. You can choose between three different time windows for click attribution: 1 day, 7 days, or 28 days. Like most, Metric Theory views clicking an ad as having the strongest influence in driving a conversion. If a user takes the action to click an ad and then converts, we can say with certainty that the ad influenced them to complete the conversion action. Because of this, we typically recommend using the largest time period Facebook offers, 28 days, when analyzing click attribution.

View-Through Attribution


In order for a conversion to be attributed to an advertising effort in a view-through attribution model, the user must have viewed the ad, but not clicked the ad, and then completed the conversion action in a time window set by you. Just like click attribution, Facebook allows you to choose between three different time windows for view-through attribution: 1 day, 7 days, or 28 days.

Many find it hard to attribute conversion credit to an ad that was only viewed by a user, but not clicked. It is true that we are unable to know with the same level of confidence whether that ad truly influenced a conversion that we can for a click, but we also know that some ads do influence behavior without attracting a click. Simply put, it is challenging to understand the impact an ad view has on a conversion. Through testing and data analysis you can better understand that relationship, but even without that information, you can still use that data in attribution.

At Metric Theory, we’ll typically at least suggest a conservative attribution of view-through conversions at a 1-day time window, and find that this leads to a better understanding of ad performance for most clients.

Which Attribution Model Is Right For Me?


No attribution model will ever be perfect in capturing all conversions influenced by an advertising effort. Choosing the right attribution model for you is no easy task, and advanced marketers will even track multiple attribution models at once and use different attribution models in different contexts. For instance, you might use a 28-day click-through plus 1-day view-through attribution model specifically for budgeting in order to capture a more complete picture of performance, though you might need to present last-click attribution only when looking at overall marketing performance segmented by channel, since including the view-through conversions would create duplicate conversion reporting.

You might also select a different attribution model based on the funnel stage of a campaign. For prospecting campaigns, you might judge performance using a click-plus-view-through attribution model (or a longer view-through window), while using click-through attribution only (or a shorter view-through window) for lower-funnel remarketing campaigns. Why? With prospecting, it’s much less likely to drive clicks, let alone click-through conversions than with remarketing. Adding in more view-through conversions to prospecting campaign data gives more conversion data to optimize off of, and because one of the goals of prospecting campaigns is brand exposure, it makes perfect sense to value ad views more in that context. For remarketing, especially for purchase-driven campaigns like cart abandonment, ad views will likely contribute far less to the decision to buy.

Those are just two examples, of course. The most important thing is that you’re considering the full extent of the available data Facebook offers.

Facebook Attribution Tool


Facebook recently introduced a new tool called Facebook Attribution that helps advertisers evaluate conversion performance across multiple paid media channels. It allows you to toggle between various attribution models to view how each changes the point of view on performance data. Facebook Attribution also enables you to understand the path users take across paid media touchpoints and devices to conversion to provide insight on the customer journey. This tool offers one of the most comprehensive views of evaluating different attribution models available to digital marketers. Below is a screenshot of just some of the attribution options that the tool offers.

If you need help determining which type of attribution is best for you or you want to learn more about Facebook Attribution, please do not hesitate to contact us.