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Digital Marketing Academy · Lesson

Modeling Marketing Data

Clean, joined tables.

Why Model at All

Raw connector tables are messy: inconsistent column names, mixed currencies, different grains, and platform-specific quirks. Querying them directly produces wrong, irreproducible numbers.

Data modeling is the discipline of turning raw rows into clean, consistent, business-ready tables. It is where ROAS, conversion, and revenue get defined once, correctly, so every report agrees.

The Star Schema

The dominant analytics model is the star schema: a central fact table of measurable events surrounded by dimension tables that describe them. Facts hold numbers (spend, clicks, revenue); dimensions hold context (campaign, date, channel, customer).

This shape is intuitive for marketers and efficient for BI tools, which join one fact to several dimensions to slice metrics by any attribute.

        dim_date
           |
dim_channel -- fct_ad_spend -- dim_campaign
           |
        dim_account

fct_ad_spend (facts): impressions, clicks, cost, conversions
dims: who / what / when context

All lessons in this course

  1. Why a Warehouse
  2. ETL and Connectors
  3. Modeling Marketing Data
  4. Dashboards That Drive Action
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