brim/break
Back to stack
05 · Layer

Measurement.

Privacy-native marketing attribution for DACH B2B

When iOS14 kills your tracking and the cookie banner hides 40-70% of your conversions. Statistical MMM on aggregate data — no PII, no user IDs, no cookies. Channel contribution + saturation curves you can actually measure.

5+
Channels modeled
Weekly
Model refresh
0 PII
Privacy-native
~95%
Convergence rate
We've all been there

What you've probably seen.

  • Attribution tools crown the wrong channels as heroes
  • GA4 + cookie banner drop 40-70% of conversion data
  • Marketing budgets split by gut feel
  • iOS14 + GDPR kill multi-touch attribution
How we solve it

How we set it up.

  • Bayesian MMM on aggregate data — zero PII risk
  • Saturation curves per channel, clear diminishing returns
  • Weekly refresh, monthly board update
  • Privacy-native by construction, GDPR-clean
Toolchain
PyMC-MarketingBigQueryAirbyteCloud RunLooker StudiodbtGeo TestsBayesian InferencePyMC-MarketingBigQueryAirbyteCloud RunLooker StudiodbtGeo TestsBayesian Inference
Saturation curves

Each curve = one channel. Y-axis: contribution to revenue. X-axis: spend. Flat zone = diminishing returns, that's where the money burns.

Example workflow

Example: Weekly model refresh

  1. 01Daily channel-spend sync (Fivetran/Airbyte → BigQuery)
  2. 02Weekly PyMC-Marketing job on Cloud Run
  3. 03Saturation curves + contribution plot generated
  4. 04Looker dashboard for C-level — new budget allocation

Want us to build this for you?

30 min demo. We walk you through a real setup, live.