This document discusses marketing attribution and compares different attribution models. It summarizes: - Traditional attribution models like last-click and first-click attribution have limitations and do not capture the full customer journey across channels. - Data-driven attribution models use algorithms and machine learning on large datasets to analyze customer paths and attribute conversions more accurately across channels. - The document evaluates several attribution models and presents results from applying different models to a client's marketing data, showing how budgets can be optimally reallocated across channels based on each model's findings. - A data-driven attribution approach using multiple channels and algorithms provides more insights into the customer journey and funnel, allows for more accurate budget allocation, and can improve