AI Analytics & Attribution

Modern AI attribution tools offer multiple models, each with different use cases:\n\n(1) Last-click attribution - Credits 100% of revenue to the final touchpoint before conversion; simple to implement but severely undervalues top-of-funnel awareness channels (content, SEO, social) that initiate buyer journeys\n\n(2) First-click attribution - Credits 100% to the first interaction; useful for understanding what drives initial awareness but ignores nurturing channels that convert prospects\n\n(3) Linear attribution - Distributes credit equally across all touchpoints; better than single-touch models but oversimplifies by treating all interactions as equally valuable\n\n(4) Time-decay attribution - Assigns more credit to touchpoints closer to conversion (e.g., 40% to last touch, 30% to second-to-last, etc.); accounts for recency bias but still uses fixed rules rather than data\n\n(5) Position-based (U-shaped) attribution - Typically assigns 40% credit to first touch, 40% to last touch, and 20% distributed among middle touchpoints; recognizes importance of awareness and conversion but arbitrarily weights middle touches\n\n(6) Data-driven attribution - ML algorithms analyze hundreds of thousands of conversion paths to determine actual impact of each channel based on how often it appears in successful vs. unsuccessful journeys; most accurate but requires significant data (10K+ conversions)\n\n(7) Incrementality-based attribution - Uses controlled experiments (lift tests) to measure true incremental impact of each channel by comparing conversion rates when channel is on vs. off\n\nFor B2B companies with longer sales cycles (3-12 months), data-driven attribution combined with incrementality testing provides the most accurate results, as it accounts for complex multi-touch journeys while validating true causation rather than just correlation. Companies with <5K annual conversions may need to start with position-based models due to insufficient data for machine learning.