You Bought the Science. Did You Fix the Data First?

Key takeaways:
✦ Measurement vendors sell methodology. They cannot sell you the data foundation required to support it.
✦ Incrementality testing and MMM are only as rigorous as the data environment they run on.
✦ Nobody in the sales process has an incentive to name the real problem.

Cannes Lions (June 22–26) will be full of measurement vendors.

Incrementality platforms. MMM providers. AI-native attribution tools. All of them selling the same promise: buy the science, get the answer.

The promise is real. The assumption underneath it is not.

The real problem nobody names in the sales process

Incrementality testing built on inconsistently tagged campaigns returns confidently wrong lift numbers. MMM fed by fragmented, agency-siloed data optimizes against a fiction. The model runs. The outputs look precise. The deck gets built.

The methodology is not the problem. The data environment is.

And nobody in the room has an incentive to say that out loud. The vendor wants the deal. The agency wants to look capable. The brand team wants to hand the CMO a number. So the number gets produced, the methodology gets a panel at the Palais, and the data problem stays buried until the results do not hold.

This is not a critique of the measurement category. The science these platforms are building is serious. But rigorous methodology applied to a broken data environment does not produce better answers.

It produces more sophisticated wrong ones.

So before you invest in the science, be honest about what you are feeding it.

What would your team honestly say about the data environment your measurement tools are running on?

The AI Readiness Self-Score:
🔴 Fragmented. Campaign data is siloed across platforms, agencies, and spreadsheets. No unified taxonomy. Measurement outputs reflect that fragmentation back at us.
🟡 Partially centralized. Inconsistencies remain across partners and platforms. We use the numbers but quietly discount them.
🟠 Mostly centralized with known gaps. Measurement is directionally reliable but we know where the weak points are.
🟢 Governed, standardized, and monitored. We trust the data environment our measurement tools are running on.

The measurement investment is only as strong as the foundation underneath it.

My AI Readiness Diagnostic surfaces exactly where that foundation stands before you build on it.
👉 https://lnkd.in/gw4UB-KZ