First-Order Absorbing Markov Chain Analysis for Web Navigation and Conversion Rate Optimization

Improving conversion rates (CVR) in e-commerce and web-based online applications requires understanding and optimizing user navigation behavior. This study employs a first-order absorbing Markov chain (AMC) framework to model user journeys through websites, where transient states represent web pages and absorbing states denote conversion or exit events. Unlike traditional attribution methods that retroactively assign credit to past touchpoints, our AMC framework enables both diagnostic analysis—quantifying each web page’s contribution to overall conversion probability—and prescriptive simulation by evaluating hypothetical structural changes, such as modifications to link transition probabilities. Experiments on both synthetic datasets—representing diverse browsing patterns including goal-oriented flows, random browsing, and leaky funnels—and real-world e-commerce clickstream data show that the AMC approach provides more interpretable and robust insights than a logistic regression baseline, particularly in low-CVR scenarios common in e-commerce environments. A key contribution is the introduction of a conversion impact metric that serves as a forward-looking decision-support tool by quantifying how individual link modifications influence overall conversion probability, enabling data-driven decision-making for website optimization. Through its emphasis on interpretability and transparency, this framework demonstrates the practical utility of AMC-based approaches for data-driven user experience design and website structure optimization.