Configure a Multi-Touch Attribution Model

Multi-touch attribution distributes credit for a conversion across all the marketing touchpoints a customer encountered before completing the goal action. Unlike last-touch or first-touch heuristics, an algorithmic model uses the full path of interactions to learn which combinations of channels and sequence patterns are most causally predictive of conversion. The output is a per-touchpoint fractional credit value that can be aggregated across campaigns to calculate true incremental return on ad spend.

Configuring the model requires the practitioner to specify: which event type represents a conversion (purchase, form submit, subscription), optionally a monetary value field for weighted revenue attribution, the look-back window (how far back in the journey to consider), and the channel fields that identify each touchpoint category. Run frequency should match the pace of campaign changes — monthly for stable evergreen programs, weekly for high-rotation performance marketing.

Parallel viability note: Dedicated multi-touch attribution tools (e.g., Rockerbox, Northbeam, Triple Whale) are vendor-neutral alternatives that operate on event-level data feeds and return score datasets compatible with any analytics warehouse. Snowflake and BigQuery can host custom Shapley-value implementations. AEP's Attribution AI is tightly integrated with the XDM event schema and writes directly to the Platform dataset layer, which reduces pipeline complexity when the primary profile store is AEP but adds a dependency on the Intelligent Services licensing tier.