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Matomo in Marketing Mix Modeling (MMM)

Sellforte supports two Matomo outcome datasets depending on the nature of your business. Choose the dataset that best matches what you want your marketing to drive.

Matomo Ecommerce Outcomes

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

Dataset definition

Ecommerce purchase outcomes from Matomo with visit-level acquisition and geographic context, used for marketing measurement and optimization.

Grain & sample

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

Example rows (illustrative):

site id date order id country currency campaign name referrer type referrer name orders revenue
1 2025-01-01 O-10001 FI EUR brand_sale search google 1 129.00
1 2025-01-01 O-10002 FI EUR prospecting social facebook 1 89.00
1 2025-01-02 O-10003 SE SEK (not set) direct direct 1 59.00

Dimensions

  • site id

  • date

  • order id

  • country

  • currency

  • campaign name

  • campaign keyword

  • referrer type

  • referrer name

  • new vs returning

Metrics

  • orders

  • revenue

  • total revenue

  • subtotal

  • shipping

  • tax

Source retrieval recipe

Source system: Matomo
Extract type: Matomo Reporting API

Required parameters

  • idSite

  • Date range

Dimensions queried

  • date

  • orderId

  • country

  • currency

  • campaignName

  • campaignKeyword

  • referrerType

  • referrerName

  • visitorType

Metrics queried

  • nb_orders

  • revenue

  • total_revenue

  • subtotal

  • shipping

  • tax

Matomo Event Outcomes (Goals)

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

Dataset definition

Conversion outcomes from Matomo based on goals, with visit-level acquisition and geographic context, used for marketing measurement and optimization.

Grain & sample

Each row represents one goal conversion per date, goal, country, and visit-level acquisition context.

Example rows (illustrative):

site id date goal name country campaign name referrer type referrer name conversions
1 2025-01-01 Lead Submitted FI prospecting search google 12
1 2025-01-01 Sign Up FI retargeting social facebook 8
1 2025-01-02 Lead Submitted SE (not set) direct direct 6

Dimensions

  • site id

  • date

  • goal name

  • country

  • campaign name

  • campaign keyword

  • referrer type

  • referrer name

  • new vs returning

Metrics

  • conversions

Source retrieval recipe

Source system: Matomo
Extract type: Matomo Reporting API

Required parameters

  • idSite

  • Date range

  • Goal selection (goal ID or goal name)

Dimensions queried

  • date

  • goalName

  • country

  • campaignName

  • campaignKeyword

  • referrerType

  • referrerName

  • visitorType

Metrics queried

  • nb_conversions

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 Matomo sites are used for each brand or product category, Sellforte can derive these dimensions from site identifiers and include them 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.