attribution-analysis-modeling

Multi-touch attribution models assign credit to each channel in a customer journey. This Skill computes attribution using Markov chains, Shapley values, and baseline models (first-touch, last-touch, linear, time-decay) on customer path data to show which channels drive conversions and revenue.

Yes. This Skill applies multiple attribution approaches simultaneously—Markov, Shapley, and position-based models—then compares results side-by-side to reveal how model choice affects channel credit allocation and ROI calculations across your data.

Attribution analysis accepts CSV, JSON, and Excel files containing customer journey paths. The Skill reconstructs sequences, validates data, generates transition matrices, and outputs visualizations and reports compatible with pandas and plotly for downstream analysis.

Markov chain attribution models channel sequences as state transitions to measure removal effects—how conversion drops when a channel is absent. Shapley values distribute credit using coalitional game theory, treating all channel combinations equally. Each reveals different optimization insights.

Yes. This Skill calculates channel-level conversion rates, cost-per-acquisition, and ROI metrics, then generates actionable optimization recommendations based on attribution weights and journey performance to guide marketing spend reallocation.

The Skill handles Markov chain computation, Shapley value algorithms, and model comparison automatically. You provide customer journey data in CSV, JSON, or Excel format; the Skill manages path construction, transition matrices, and comparative visualizations without requiring deep statistical coding.