Adaptive website content is an increasingly popular method for e-commerce platforms to personalize the content shown to its visitors in an attempt to increase the conversion rates of their platforms. However, in order to select the content relevant for a specific customer, the online platform should be able to rapidly interpret the explicit choices, as well as the implicitly sensed context, in which the choices are made. Creating a so-called context-aware e-commerce platform thus requires not only the understanding of users’ content-preferences, but also of the users’ context influencing this decision-making and the opportunities of the e-commerce platform to actively respond to this, in addition to analysis of customer behaviour that goes beyond traditional A/B testing used by websites and online services to quickly evaluate hypothesis with real users. Statistical choice modelling and machine learning are both methods with the ability of doing such advanced analysis of choice behaviour. By inferring preferences and trade-offs from people’s choices observed in real life, or hypothetically stated choice experiments, choice models can be estimated with which future choices can be predicted. As statistical choice models provide insight into the most important parameters that influence decisionmaking, conversion rates of e-commerce platforms can be maximized by optimizing those parameters. Moreover, it has long been acknowledged in discrete choice literature that the context in which a decision is made affects one’s decision-making. Machine learning on the other hand, takes an algorithmic approach and bases its prediction on patterns found in the data. The characteristic of machine learning to allow for high-order interactions that are not pre-specified, could be beneficial as the context in which decisions are made, is often fuzzy and highdimensional. However, as this is a black box method, the method will not explicitly give insight into the parameters influencing behaviour, but instead, train itself to maximize the outcome. Although a substantial amount of research is conducted on the difference between statistical choice models and machine learning, and A/B testing and machine learning are both widely used methods for the dynamic content selection on e-commerce platforms, and thus for conversion optimization, less research however, has been done on the complementary use of the two methods within the boundaries of a simple context-aware system-experiment. Nowadays, it is often stated that machine learning has an accuracy far beyond statistical methods and that it is the recommended method for future data analytics and prediction, as machine learning enables determination of outcomes in which large number of variables with complex relationships are involved. However, should machine learning always be the recommended method for an e-commerce platform trying to maximize its conversion rate with the use of dynamic content, or is more traditional statistical choice modelling sometimes still a better solution for smaller organizations less experienced in data analytics? The scientific objective of this study was to investigate the applicability of both statistical discrete response modelling and machine learning methods to maximize, separately or complementary, conversion rate on ecommerce platforms. By using the e-commerce platform Fatboy.com as test environment, in which both methods were required to handle the context-aware opportunities of contemporary online platforms, the potential in applicability of both methods could be examined. The objective within the case study Fatboy® was to understand how the conversion rates of e-commerce platforms can be maximized using statistical discrete response modelling, machine learning, or complementary to each other.