Post-View allocation

👋 In this article, we'll decipher how post-view attribution works, its limitations, and why it's essential to evaluate its role in your marketing strategy.

Post-view attribution is one of the most overrated and misleading metrics in digital advertising. It artificially attributes conversions to ads that have simply been seen, without any evidence of real impact on purchase. This practice systematically leads to a significant overestimation of advertising performance.

How does post-view attribution work?

Here's an example to illustrate its limitations:

• A user scrolls down a page and catches a fleeting glimpse of a banner ad, perhaps without even consciously noticing it.
• Unaffected by this advertising, he later makes a purchase directly from the brand's website.
• The advertising platform then takes all the credit for this conversion, creating an illusion of performance.

ℹ️ This system is fundamentally biased, as it allows platforms to artificially inflate their results without being able to demonstrate any real impact on sales.

Fundamental problems of post-view attribution

1️⃣ The myth of retargeting

The majority of post-view conversions are actually natural conversions captured via retargeting. Case in point:

• A customer discovers a brand via a Google search with a real intention to buy.
• It is then exposed to a retargeting ad on Facebook.
• The platform takes credit for a conversion that would have happened anyway.

☝ Convictions
‍Thispractice creates a dangerous illusion of efficiency and leads to a massive waste of advertising budgets on audiences already won over.

2️⃣ The deliberate opacity of data

Advertising platforms intentionally conceal detailed post-view interaction data. This opacity is no accident: it prevents any independent analysis that might reveal the real weakness of these attributions.

Platforms like Facebook strictly limit access to raw data, making external verification of their performance claims impossible.

3️⃣ The artificial allocation window

Facebook's limitation to a one-day window is not insignificant:

• Most post-view conversions are claimed within hours of exposure.
• This short window masks the total lack of lasting impact of these ads.

4️⃣ The need for real commitment measurement

It's time to move away from superficial metrics like views and impressions and focus on authentic engagement indicators:

• In-depth analysis of the site.
• Measure qualified interactions with products.
• Follow-up of actions demonstrating a real intention to buy.

These metrics are the only true indicators of advertising effectiveness.

The persistence of a biased system

A system that benefits platforms

Advertising platforms maintain this system because it enables them to justify high rates and attract more investment, while masking the real inefficiency of many campaigns.

The pressure of immediate ROI

Marketers, under pressure to justify their investments, find themselves trapped in a system that provides them with flattering but misleading metrics.

Towards authentic performance measurement

To get away from the post-view illusion, focus on truly meaningful metrics:

• Qualified engagement rate: Measure the real quality of post-exposure interactions.
• Transparent multichannel attribution: Identify the real conversion triggers.
• Rigorous incrementality testing: Measure the real net impact of your campaigns.

💡 Tip
‍Centralizeyour data in BigQuery to keep full control over your attribution and build transparent models based on real data.

Conclusion

Post-view attribution is a fundamentally flawed model that undermines marketing effectiveness. Advertisers need to take back control of their attribution based on verifiable data and actual behavior rather than manipulated metrics.

☝ Convictions
‍Thefuture of digital marketing lies in abandoning post-view metrics in favor of rigorous measurement of incrementality and real engagement. Advertisers need to centralize their data and develop their own attribution models to escape platform dependency.