The Hidden Reason Your Facebook Ads Are Broken (And How To Fix Them)

Every e-commerce brand knows that Facebook advertising is much less effective than it was a few years ago, but the reason why isn’t quite as well understood. Surprisingly, the answer often involves user data loss.
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This data loss is the direct outcome of updates made to Apple’s Safari web browser, which accounts for 34.5% of the overall browser market and 52.2% on mobile devices. In service of increased privacy, these updates shortened the lifespan of Safari browser cookies to a single day from ad channels and seven days for direct or organic traffic. (Domain owners alone are able to set a user identifier that persists beyond seven days.) But since the buyer journey often extends beyond a day, these updates suddenly made it much more difficult to understand the complete buying cycle.
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But don’t worry, the story doesn’t end there! This article will dive into the specific ways this user signal loss is impacting the effectiveness of Facebook advertising. It will also explain why server side user tracking has become the go-to solution for fixing Facebook ads and improving your e-commerce brand’s return on ad spend (ROAS).

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Anatomy of a 30 day buy-cycle

To illustrate the user data loss created by shortened user tracking timelines, let’s join Jo as she shops for an e-bike. She’s looking for a bike she can use to get groceries a couple times a week without having to use her car, and take her three-year-old along for the ride. She heard about a good e-bike brand from a friend, and pulls up her phone after her child’s bedtime to do a little research. 

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Day 1

Jo notices an ad on Instagram from the same bike brand her friend recommended. She taps on it, and browses through different models and feature options on the brand’s website. Each of these actions are “events” that include important signals about Jo’s preferences and intentions as a buyer. 

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Day 15

The weather’s been cold and rainy, so Jo hasn’t felt very motivated to continue her research. But there’s one e-bike model she’s been thinking about, so she returns to the website to compare color options, and decides on a cream colored bike with navy accents. These actions are additional events that further flesh out her journey as a potential buyer.

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Day 30

The nice weather has returned, and Jo is feeling ready to finally purchase the e-bike and spend some more time outside. She returns to the company website, adds the model and color she’s been dreaming about to the cart, and makes a purchase. Yep, that means a few more events are added to her customer journey.

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User tracking limitations spoil Facebook’s attribution 

Plot twist: Jo is actually three different people! Just kidding. But because the cookies in Jo’s browser expire so quickly, Facebook actually does see Day 1, Day 15, and Day 30 as three separate people! Even though each of Jo’s interactions with the e-bike brand triggered several different events, these disappeared before the next interaction. Each time Jo returned to the site, it was like starting from scratch. Rather than building on previous events and corresponding data, it was like a blank slate. This resulted in “Day 30 Jo” getting credit for the purchase, while “Day 1 Jo” and “Day 15 Jo” were completely disconnected from the equation altogether. They might as well have been different people.

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Signal loss is training Facebook’s algorithm to become worse  

Not being able to understand Jo’s complete customer journey isn’t ideal. But the long term implications for the e-bike’s company’s marketing efforts are actually much more concerning. Over the same 30 day window during which Jo made her purchase, they sold more than 2,500 e-bikes to other customers, each one with their own unique (and misattributed) journeys.

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Every customer further compounds the problems that result from user signal loss. It’s impossible to correctly attribute purchases to the ads that generated them, which impacts future ad bidding. And since Facebook only sees “Day 30 Jo” as the one who made the purchase, it avoids targeting future ads to someone similar to “Day 1 Jo” and “Day 15 Jo”. This results in smaller targeting audiences and/or less effective marketing efforts.

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What’s more, since the whole Facebook advertising platform relies on algorithms, all of the negative outcomes above are essentially training the algorithm to be less accurate and more expensive. It’s an unfortunate cycle, with ever-decreasing ROAS and higher CPMs at the center.

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Client side vs. server side user tracking

There are two types of user tracking: server side and client side. Server side tracking is managed by individual brands, while client side tracking is managed by third parties like Facebook or Google. Client side tracking has the advantage of being extremely simple to configure. (The process can be as easy as pasting a small snippet of code into your website or downloading an affiliated app.) Indeed, it’s their ease of use that has made client side user tracking so ubiquitous. But due to Safari Intelligent Tracking Protection (ITP), client side tracking expires after just one day when coming through a paid ad. Luckily, there’s another “flavor” of cookie: server side! How is server side user tracking different from their client side cousins?

• Server side cookies are “stickier,” because they aren’t constantly at risk of being erased like someone shaking an Etch a Sketch. They stick around throughout the buying cycle, and in doing so they assist in building out a more detailed picture of the prospective customer.
  

• The reason that server side user tracking isn't subject to shorter expiration windows is because brands remain in control of the data they do—and don’t—share with Facebook. This complies with Apple’s objective of increasing user privacy and regulating third party data access.
  

• Server side user tracking offers more privacy, because each company gets to decide what data gets shared with Facebook, vs. client side which defaults to sharing everything.
  

• Server side user tracking is a lot more complicated. They require configuration, and also that the client passes them to third parties. When APIs and integrations change, clients must react quickly to stay up to date. This work requires internal talent who can move fast and embrace flexibility.
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Because of the resources required to configure and maintain server side user tracking, only large organizations have been able to use them. Meanwhile, smaller e-commerce companies were unable to enjoy the benefits. Because smaller companies were unable to dedicate internal teams to configuration and maintenance, they were stuck with lesser, client side tracking. This put their paid advertising efforts at a tremendous disadvantage when trying to compete for customers.

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How does server side user tracking compare to CAPI?

Perhaps you’re already familiar with Facebook’s Conversion API. This solution was created by Facebook to solve many of the problems addressed in this article. In short, the Facebook Conversion API allows businesses to provide a direct data connection between e-commerce websites and Facebook’s ad platform. And while this solution is helpful, Black Crow’s technology offers noticeable improvements beyond what CAPI offers. Because of this, many leading e-commerce brands employ a two-fold strategy: they use server side user tracking as a way to “supercharge” the capabilities of Facebook’s Conversion API by improving event match quality.

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Wait, what is Facebook’s Event Match Quality?‍

Event match quality (EMQ) is Facebook’s scoring system that signals its ability to connect customer events with Facebook’s ID graph. Certain events that include customer info (like an email address or phone number) generally have a higher EMQ score because they are more likely to connect to a Facebook account. Most events don’t include user info (page view, or view content, for example), so they are more challenging to tie back to a Facebook account.

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When an e-commerce company makes use of both server side tracking and CAPI, it increases the resulting EMQ scores (especially for upper funnel actions) by as much as 31%. These improved scores result in larger audiences to target, better attribution, more accurate data, and improved ROAS by as much as 50%!

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Using server side user tracking to retrain Facebook’s algorithm

Remember how the user signal loss detailed above is training Facebook’s algorithms to be less efficient? Well the opposite effect can also be true. Let’s briefly revisit Jo’s story, but with server side user tracking enabled.

Day 1

Jo searches Instagram, taps on an ad, browses the e-bike company’s website, and searches for different models and feature options. Each of these events are linked together with a constant identifier.

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Day 15

Jo returns to the website to compare models. As she browses, these events are added to the profile that includes her previous engagement on day 1. As a result, the picture of Jo becomes more detailed.

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Day 30

Jo returns to the company website, adds the bike to cart, and makes a purchase. These events are added to the rest, and Jo’s profile becomes even clearer.

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Now that Facebook has visibility into each of the events that led to Jo’s purchase across all thirty days, its algorithm gains a better understanding of the signals that led to a conversion.
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In other words, server side user tracking literally helps retrain Facebook’s targeting and bidding algorithms to be more accurate in the future.

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Getting a taste of server side user tracking

This is where Black Crow AI enters the story. While server side tracking has historically only been available to large companies, Black Crow simplifies the process and makes it accessible for e-commerce brands of any size. This turns the tides of e-commerce Facebook advertising. After years of decreasing effectiveness and increasing costs, Black Crow restores much of what has made Facebook such a powerful advertising platform in the past.

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