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Documentation Index

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Media Mix Modeling (MMM) is a statistical approach to understanding which marketing channels are driving results — and how much. Instead of relying on last-click attribution, MMM looks at the relationship between historical spend and outcomes across all channels simultaneously, accounting for seasonality, baseline effects, and diminishing returns. Interact uses Google Meridian as its MMM engine. Your Python pipeline trains the model; Interact displays the results.

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What you can do

Data Book

Inspect the prepared dataset that was used to train the model — column definitions, time range, and channel spend series.

Model Results

View channel contributions, response curves, predictive accuracy, and model health diagnostics for the latest (or any previous) model run.

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How it works

Your data warehouse (BigQuery)
    └─ raw marketing spend + KPI data

Python pipeline (Meridian)
    └─ reads prepared data from BigQuery
    └─ trains Meridian model
    └─ exports results to GCS  →  gs://turntwo-mmm/{label}/results/model_{timestamp}.json

Turntwo Interact
    └─ Data Book page  →  queries the prepared dataset from BigQuery directly
    └─ Model Results page  →  reads the latest artifact from GCS on page load
The app does not run Meridian — it only displays what your pipeline produces. This means you control the model training schedule, the data preparation logic, and the granularity of the output.

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Multi-market support

You can configure multiple data books — one per market or label (e.g. NL, DE, UK). Each data book has its own BigQuery table and its own GCS results folder. The UI shows a dropdown to switch between them.

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Before you start

You’ll need:
  • A BigQuery connector set up in Settings → Connectors
  • A turntwo-mmm GCS bucket in your client’s GCP project, with the correct IAM role on the service account
  • At least one Data Book configured in Insights → Settings → MMM
  • A trained Meridian model with results exported to the correct GCS path

Set up MMM

Step-by-step: bucket creation, IAM roles, and settings configuration.