Why TripleWhale and Meta Attribution Don’t Match: Understanding the Key Differences

If you’re among the many brands using TripleWhale’s (TW) multi-touch attribution (MTA) solution while also optimizing Meta ad campaigns on 7-day click attribution, you've likely noticed a significant difference between the two. It can be frustrating to see TripleWhale’s reported return on ad spend (ROAS) consistently lower than Meta’s 7-day click ROAS, sometimes by a substantial margin. 

But why is this the case?

To truly understand, we need to explore the foundational differences in how these systems approach attribution.

Reason#1: Deprecation of Browser URL Parameters

One of the key reasons for this discrepancy lies in how TripleWhale’s MTA relies on UTM parameters like:

tw_source={{site_source_name}}&tw_adid={{ad.id}}

These UTM parameters help track which ad led a user to the advertiser’s website. However, with increasing consumer privacy concerns, browsers are deprecating those URL parameters, reducing the visibility TW has to accurately track the origin of clicks. While Facebook Click IDs (fbclid) may still be available, without UTM parameters, TW can’t always determine which specific ad drove the click.

Facebook, by contrast, doesn’t depend on UTM parameters. Its ad delivery system automatically logs every ad impression and click, tracking which users interacted with which ads and when. Since all users on Facebook and Instagram are logged in, the platform can also link ad interactions to individual users through other identifiers, such as email addresses, phone,  or Facebook Click IDs. This enables Facebook to track the entire user journey without the need for UTM parameters.

Reason #2: Cross-Browser and Cross-Device User Journeys

Consumer behavior is rarely linear. Users might click on a Facebook ad, then switch devices or browsers to complete their purchase. In these instances, TW MTA struggles to maintain visibility. Why? Because when a user moves from one session or device to another, new session cookies are created, and TW treats these sessions as organic, disconnected from the original Facebook ad interaction.

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Facebook, however, maintains a significant advantage. By leveraging its consumer identity graph, Facebook tracks users across devices and browsers. Since users are logged in, Facebook can match purchases back to original ad clicks using personal identifiers like email, phone numbers, and postal addresses—ensuring a more complete attribution of cross-device and cross-browser conversions.

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Reason #3: When Additional Marketing Channels Involved

Another factor complicating attribution is the involvement of other marketing channels. Imagine a user clicks on a Facebook ad, then signs up for a newsletter but completes the purchase after receiving an email promotion. Or, the user clicks on an ad but later returns through a Google-branded search. In both cases, TW’s multi-touch attribution model might give credit to the last touchpoint (email or search), while Facebook continues to credit itself for initiating the purchase.

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This divergence in credit assignment highlights the limitation of Facebook’s in-platform attribution—it doesn’t account for subsequent touchpoints from other channels. However, TripleWhale does capture these interactions, which may explain the lower ROAS numbers reported on their platform.

So, Which Attribution Reporting is Correct?

Unfortunately, there’s no simple answer. Each attribution model comes with its strengths and limitations, depending on the factors involved:

Facebook's limitation is its inability to account for interactions beyond its ecosystem. TripleWhale, by capturing those other touchpoints, offers a more holistic view but may under-report Meta’s actual contribution to conversions.

The Role of Auction Training Over Attribution

As growth marketers, we tend to focus too much on attribution—how much credit each platform gets for a conversion. But attribution is like the final grade of an exam—it reflects what happened but doesn’t influence future results. The real focus should be on auction training, which is how platforms like Meta, TikTok, and Google Ads learn to show your ads to the right people.

Instead of obsessing over attribution, concentrate on optimizing auction training to achieve your goals:

• Want to reduce CAC? Train the auction to target the "movable middle."
• Looking to boost ROAS or 90-day LTV? Focus on users likely to spend more in their first purchase or within 90 days.
• Enhance free trial conversions? Target high-value signups likely to convert to paid customers.
• Reduce cost per lead? Focus on leads that are likely to convert within 28-90 days.

The Power of Predictive CAPI with Angler AI

At Angler AI, we specialize in helping brands win the auction training battle. Our predictive CAPI solution uses your zero-party and first-party data to train Meta and other walled gardens to deliver better results. More importantly, our solution ensures that your in-platform attribution aligns with your model preferences, keeping your data accurate and actionable.

Attribution might be the grade you receive, but auction training is where the real success lies. Focus on what you can control—and let us help you achieve the outcomes that matter.