Define calculated metrics and attribution models for journey reporting

Calculated metrics transform raw event counts into business-meaningful KPIs — conversion rates, add-to-cart ratios, revenue per visit — without requiring changes to the underlying data schema. Attribution models then answer the harder question: when a customer converted after touching multiple journey steps and channels, how should credit be allocated across those touchpoints? Configuring both layers correctly is what turns raw behavioral data into actionable journey intelligence.

The metric-definition workflow requires understanding which XDM field paths carry the underlying counts (e.g., commerce.productViews, commerce.productListAdds, commerce.purchases), which aggregation function is appropriate (Sum for counts, Approximate Count Distinct for unique visitors), and whether the metric should be scoped to a specific container (event, session, or person). Attribution configuration adds a second dimension: the lookback window determines how far back in time the model searches for credit-worthy touchpoints, while the model type (Last Touch, First Touch, Linear, Participation) determines how that credit is spread.

Parallel viability: Medium-to-high parallelism. Composable BI tools (Looker, Tableau, dbt metrics layer) all support calculated metrics and configurable attribution windows. The CJA-specific advantages are tight integration with the AEP Identity graph for person-level session stitching and the shared data model that lets the same metric definitions be reused across the same workspace that shows funnel and flow analyses. Teams with a mature metrics layer in an existing BI tool should evaluate whether replatforming metric definitions to CJA delivers incremental value, especially if they do not already use AEP for real-time profile resolution.