Multi-Touch Attribution: Markov & Shapley

Multi-Touch Attribution (MTA) Frameworks (Markov Chain Removal Effects & Cooperative Game-Theoretic Shapley Values) evaluate the marginal fractional contribution of diverse marketing touchpoints (Search, Social, Display, Email, Influencer) along the customer conversion journey; Rule-based attribution (Last-Touch, First-Touch, Linear) introduces severe heuristic bias; Algorithmic Attribution includes: 1) Markov Chain Attribution via Removal Effects: modeling touchpoint paths as absorbing state transition probability matrices $P$; computing the fractional drop in overall system conversion probability when channel $i$ is removed ($R_i = 1 - \frac{P(\text{Conversion w/o } i)}{P(\text{Conversion with All})}$) to apportion credit; 2) Shapley Value Attribution: calculating average marginal contributions across all $2^N$ combinatorial coalition subsets satisfying axiomatic game-theoretic fairness (efficiency, symmetry, additivity).

Multi-channel marketing budget allocation, paid acquisition campaign optimization, and customer journey analytics.

Eliminates Last-Touch bias that artificially over-credits lower-funnel search ads while devaluing top-of-funnel brand awareness channels.

Derive the absorbing Markov Chain transition matrix formulation and step-by-step calculation of Removal Effects?

Derive the Shapley Value cooperative game formula and define its 4 governing axioms (Efficiency, Symmetry, Dummy, Additivity)?

How to resolve the exponential $O(2^N)$ computational complexity of exact Shapley values via Monte Carlo coalition sampling?

Explain why privacy regulations (Apple ATT, third-party cookie deprecation) break user-level MTA tracking and drive adoption of aggregated MMM?

How to calibrate algorithmic MTA models against ground-truth causal Geo-Lift experiments?