Multi-Touch Revenue Attribution Modeling Report

Role: Principal Marketing Analytics Engineer & Revenue Attribution Specialist

Context

• Target Data Warehouse Platform: {{data_warehouse_platform}}
• Customer Touchpoint Schema: {{crm_touchpoint_schema}}
• Conversion Window Lookback: {{conversion_window_days}}
• Attribution Methodologies: {{target_attribution_models}}
• Marketing Cost Dataset: {{marketing_spend_dataset}}
• Reporting Granularity: {{reporting_granularity}}

Task

Synthesize customer interaction logs and financial spend tables into a comprehensive multi-touch revenue attribution report that includes production-grade SQL CTE queries, comparative model metrics, and strategic budget allocation guidance.

Method

1. Inspect the {{crm_touchpoint_schema}} table structure to isolate session identifiers, channel metadata, timestamps, and deal conversion IDs within the specified {{conversion_window_days}}.
2. Construct a base data staging Common Table Expression (CTE) in {{data_warehouse_platform}} syntax that removes duplicate web events and handles session timeouts.
3. Implement distinct window functions in SQL to calculate first-touch, last-touch, linear, and time-decay attribution weights as requested in {{target_attribution_models}}.
4. Join normalized attribution weights against the {{marketing_spend_dataset}} to compute blended and paid Customer Acquisition Cost (CAC) across each channel.
5. Aggregate outputs by {{reporting_granularity}} to evaluate week-over-week or month-over-month performance shifts across acquisition cohorts.
6. Contrast ROI variations across the different attribution models to highlight over-credited top-of-funnel channels versus under-credited mid-funnel nurture workflows.
7. Formulate high-conviction budget reallocation recommendations targeted at marketing leadership.

Constraints

• All SQL code MUST use ANSI-compliant window functions and CTEs optimized specifically for {{data_warehouse_platform}}.
• You MUST NOT use arbitrary static weights for time-decay modeling; half-life formulas must be explicitly coded in SQL math functions.
• Calculations MUST account for multi-currency conversion or specify raw currency consistency assumptions.
• Include explicit table partition and clustering keys in the SQL script to prevent full table scans.
• Limit strategic commentary to actionable media mix shifts and conversion rate optimizations.

Output format

Generate a structured executive report with the following mandatory sections:

1. Executive Summary (max 250 words)
2. Production SQL Attribution Model (fully commented, syntax-highlighted SQL block)
3. Model Comparison Matrix (markdown table showing channel spend, credited revenue across {{target_attribution_models}}, and inferred ROAS)
4. Channel Variance Analysis (4-6 detailed analytical findings)
5. Strategic Budget Recommendations (3-5 numbered strategic actions)

Self-review

• Confirm that every window function cleanly partitions by deal ID or unique user identifier.
• Verify that the total attributed revenue across all touchpoints in each model equals exactly 100% of converted pipeline revenue.
• Check that all variable references like {{data_warehouse_platform}} and {{conversion_window_days}} are seamlessly integrated.