Innovations in Multi-Touch Attribution Models: Beyond Traditional Marketing Analytics in the Pharmaceutical Sector

Measuring the effect of multichannel marketing strategies requires advanced algorithms which create unique challenges for the pharmaceutical industry's marketing efforts. The latest Multi-Touch Attribution (MTA) model evaluates multiple touchpoints within a customer journey and gains acclaim as the prime method for resource optimization. The study investigates the advanced MTA methods involving Shapley and Markov chain models and their practical uses in pharmaceutical marketing. The Shapley value uses cooperative game theory principles to distribute credit fairly among different touchpoints. Markov chains function to model the probability of sequential exchanges between customers and sellers. The Shapley value and Markov chain methods present more detailed results than those generated by standard last-click or heuristic-based models. The research paper presents an evaluative framework demonstrating these methods' functionality. Pharmaceutical companies using advanced MTA methods improve their management of intricate customer pathways and regulatory challenges. The study serves as a pathway that enables company marketers and data analysts to implement innovative tools to achieve improved results through strategic planning and resource distribution.