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Google Analytics 4 in Marketing Mix Modeling (MMM)

Sellforte uses GA4 data to measure website and app outcomes such as ecommerce purchases and key business events, used for marketing measurement and optimization.

Choose the right GA4 outcome scope

  • Ecommerce outcomes → if you sell products online and track purchases or revenue

  • Event outcomes → if you track leads, signups, bookings, subscriptions, or other key events

Ecommerce outcomes (GA4)

Use this section if your business sells products online and you want to measure purchases or revenue.

Dataset definition

Ecommerce purchase outcomes from GA4, including transaction-level revenue metrics and acquisition context, used as business outcome signals for modeling.

Grain & sample

Each row represents one ecommerce transaction per date, country, currency, and session-level acquisition context.

Example rows (illustrative):

date transaction id country currency session source / medium ecommerce purchases purchase revenue
2025-01-01 T12345 FI EUR google / cpc 1 129.00
2025-01-01 T12346 FI EUR facebook / paid 1 89.00
2025-01-02 T12347 SE SEK direct / none 1 59.00

Dimensions

  • property id

  • date

  • transaction id

  • country

  • currency

  • session campaign id

  • session campaign

  • session source / medium

  • session default channel group

Metrics

  • ecommerce purchases

  • purchase revenue

  • total revenue

  • shipping amount

  • tax amount

Source retrieval recipe

Source system: Google Analytics 4
Extract type: GA4 Reporting API (Data API)

Required parameters

  • property_id (used in request path: properties/{property_id})

  • Date range

Dimensions queried

  • date

  • transactionId

  • country

  • currencyCode

  • sessionCampaignId

  • sessionCampaignName

  • sessionSourceMedium

  • sessionDefaultChannelGroup

Metrics queried

  • ecommercePurchases

  • purchaseRevenue

  • totalRevenue

  • shippingAmount

  • taxAmount

Event outcomes (GA4)

Use this section if your business focuses on leads, signups, bookings, subscriptions, or other non-purchase conversions.

Dataset definition

Key business event outcomes from GA4, used as conversion signals for marketing measurement and optimization.

Grain & sample

Each row represents one event type per date, country, and session-level acquisition context.

Example rows (illustrative):

date event name country session source / medium key events
2025-01-01 generate_lead FI google / cpc 12
2025-01-01 sign_up FI facebook / paid 8
2025-01-02 generate_lead SE direct / none 6

Dimensions

  • property id

  • date

  • event name

  • country

  • session campaign id

  • session campaign

  • session source / medium

  • session default channel group

Metrics

  • event count

  • key events

Source retrieval recipe

Source system: Google Analytics 4
Extract type: GA4 Reporting API (Data API)

Required parameters

  • property_id (used in request path: properties/{property_id})

  • Date range

  • Event filtering (key events or explicit event name list)

Dimensions queried

  • date

  • eventName

  • country

  • sessionCampaignId

  • sessionCampaignName

  • sessionSourceMedium

  • sessionDefaultChannelGroup

Metrics queried

  • eventCount

  • keyEvents

Derived modeling dimensions

Sellforte also supports business-specific modeling dimensions such as brand and product category. These dimensions are not typically provided as native fields by marketing or analytics platforms, so they need to be derived from other dimensions using one of the following approaches:

  • Account-based differentiation
    When separate GA4 properties are used for each brand or product category, these can be derived from property identifiers and included as explicit columns in the dataset.

  • Campaign naming conventions
    Brand or product category can be inferred from structured campaign names and included explicitly as columns in the extracted data.

  • Inline enrichment during data extraction
    Customers may add brand or product category columns as part of their API queries, SQL transformations, or export logic. Use brand and/or product_category as column names.