The Art of 1+1=1: Why Your CAPI Setup Might Be Killing Your Ad Performance

The Art of 1+1=1: Why Your CAPI Setup Might Be Killing Your Ad Performance

Articles

Don’t Get Fooled by “Ghost” Conversions

When advertisers first set up a Server-Side API (CAPI), they often see a sudden surge in conversions. On the surface, it looks like a miracle. But look closer—if your CPA looks too good to be true, you’re likely falling victim to Double Counting.

Without proper deduplication, your dashboard is lying to you. You are reporting the same purchase twice: once from the browser and once from the server. This “junk data” poisons your ad platform’s machine learning, causing it to optimize for ghost users that don’t exist.

The Secret Sauce: The Event ID

Deduplication is actually a simple concept that is incredibly hard to execute perfectly. It relies on a unique Event ID.

Think of it as a luggage tag. When a customer buys something, the browser attaches a tag. When the server confirms the sale, it must attach the exact same tag. When TikTok or Meta receives two data packets with the same tag, they merge them. If the tags don’t match, they count them as two separate people.

3 Common Mistakes We See in the Field

  1. Asymmetric ID Generation: Generating a random ID in the browser but using a database ID on the server. They will never match.
  2. The Latency Trap: If the server signal arrives too long after the browser signal, the window for merging closes. The platform treats the server data as a new, late conversion.
  3. Normalization Failures: Hashing “[email protected]” on one side and “[email protected]” on the other. In the world of SHA256, those are two completely different people.

Reliability is the Foundation of Mirai

At Mirai Track, we don’t just “send data.” We orchestrate it. Our infrastructure ensures that every Event ID is synchronized in real-time across the entire funnel.

Scaling an ad account on doubled-counted data is like building a skyscraper on a swamp. It will collapse. We provide the solid ground of data integrity, ensuring that when the algorithm learns, it learns from the truth.

Is your tracking setup telling the truth, or just what you want to hear?