Programmatic: How to boost your campaigns with "Reverse Analytics"?

A while back, I was chatting with some Media Traders who told me: "The problem with all those semantic & brand safety segments on marketplaces is that they're a bit of a black box. The only way to verify their effectiveness is to test and audit them, which is a pretty heavy process."

What if I told you there’s a method—not very widespread in programmatic—that makes auditing segments much quicker and easier?

➡️ Enter Reverse Analytics

But what exactly is it?

It’s a variation of reverse-engineering based on data analysis.

You compare a priori and a posteriori data to:

• See the impact of a specific filter on available volumes
• Try to deduce how the filter actually operates

🧪 How does it work in practice?

Here is a simple example: Want to know the exact impact of a contextual segment on publisher inventory?
1️⃣ Export the Bid Request volumes per site (e.g., France, display, web/mobile web) from your DSP/SSP/Curator planner.
2️⃣ Activate the pre-bid filter you want to test.
3️⃣ Export the new report.
4️⃣ Merge both reports in Excel using a VLOOKUP
5️⃣ Create a Pivot Table: you can now see the estimated block rate for every single site.

Pretty handy, right?

There are several highly interesting use cases for Reverse Analytics:

✔️ Identify where brand safety filters are actually blocking impressions (for instance, to avoid paying fees where they have zero impact)

✔️ Verify if a contextual segment makes sense (or not at all—like a "baby" contextual segment letting Bid Requests slip through on a webmail site)

✔️ Find sites with ultra-high affinity for a specific audience segment (socio-demographic or intentional) to unlock more Bid Requests and avoid paying unnecessary data costs

✔️ Understand why a carbon measurement tool is blocking a specific site.

In short: it’s a great way to fine-tune your investment strategies! 💡

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