Unified Multi-Touch Attribution Engine Architectural Specification

Role: Staff Revenue Infrastructure Architect specializing in analytical data mesh topologies and marketing attribution platforms.

Context

• Central analytical data warehouse: {{data_warehouse_target}}
• Omnichannel marketing touchpoint feeds: {{marketing_touchpoint_sources}}
• Core attribution models required: {{attribution_models}}
• Data refresh and computation latency budget: {{latency_requirements}}
• Data governance and privacy framework: {{governance_framework}}
• Downstream strategic reporting consumers: {{downstream_bi_consumers}}

Task

Deliver an end-to-end technical architecture specification for a scalable Multi-Touch Attribution (MTA) platform that ingests fragmented customer journeys, models conversion influence, and surfaces reliable return-on-ad-spend (ROAS) analytics to business leaders.

Method

1. Define the extract, load, transform (ELT) architecture for ingesting touchpoint telemetry from {{marketing_touchpoint_sources}} into raw staging.
2. Design the deterministic and probabilistic identity resolution engine to stitch anonymous visitor sessions into unified customer journey graphs.
3. Model the dimensional warehouse schema in {{data_warehouse_target}}, optimizing for high-volume event joins and partition pruning.
4. Specify the algorithmic compute engine implementing {{attribution_models}} (including Shapley value, Markov chains, or position-based weights).
5. Define synthetic incrementality test harnesses to calibrate algorithmic attribution against real-world holdout test results.
6. Architect semantic access layers and materialized view strategies that deliver sub-second queries to {{downstream_bi_consumers}}.
7. Detail user consent tracking, cookie-less conversion modeling, and right-to-be-forgotten deletion workflows under {{governance_framework}}.

Constraints

• Raw touchpoint events MUST be preserved in immutable storage before identity resolution or modeling.
• The computation pipeline MUST complete within the bounds specified in {{latency_requirements}}.
• Attribution calculations MUST NOT produce unallocated revenue discrepancies exceeding 0.01% across reporting cycles.
• Storage and query access policies must strictly enforce {{governance_framework}} restrictions.

Output format

1. End-to-End Architectural Topology

• Ingestion tier, compute cluster, identity graph storage, and presentation layer definitions.

2. Data Modeling & Identity Resolution Contract

• Entity-relationship (ER) diagrams, staging schemas, identity stitching logic, and graph traversal edge definitions.

3. Attribution Compute Engine Specification

• Mathematical execution steps, dbt/SQL/Spark transform logic for {{attribution_models}}, and attribution weight distributions.

4. Semantic Layer & Downstream API Contracts

• Metric definitions (CAC, ROAS, pipeline velocity), materialized view definitions, and query protocols for {{downstream_bi_consumers}}.

5. Governance, Privacy & Compliance Controls

• Consent state filtering, anonymization transforms, deletion tombstone propagation, and audit logging.

Self-review

• Does the identity graph handle cross-device cookie expiration gracefully without creating orphan conversion records?
• Are compute costs in {{data_warehouse_target}} optimized using incremental modeling rather than full historical table scans?
• Do all metrics exposed to {{downstream_bi_consumers}} reconcile perfectly with core financial ledgers?