摘要：There are plenty of online media that can be used by ecommerce companies in order to drive the revenue. However, use of this media is usually connected to investment into the selected media. From the company perspective, it is wise to evaluate the outcome of these investments in order to choose the best media mix possible. As customers do not usually buy during their first website visit, it is important to monitor their customer journey and assess the value to particular interactions. The objective of this paper is to analyze the data of selected companies using the Markov chains. The data about online customer journeys were analyzed. We found that the Markov model decreases the credit assigned to the channels favored by last-touch heuristic models and assigns more credit to the channels favored by first-touch or linear heuristic models. By using Markov order estimator GDL (Global Dependency Level), we also found that 4th and 5th order was the most suitable.
