The Bayesian design approach is an experimental design technique which has many advantages over standard experimental designs. It incorporates prior knowledge about the process into the design to suggest a set of future experiments in an optimal, sequential, and iterative fashion. Since for many complex polymerizations prior information is available, either in the form of experimental data or mathematical models, the use of Bayesian design methodology could be beneficial. Exploiting this technique in complex polymerizations could hopefully lead to optimal performance in fewer trials, thus saving time and money. Advantages of the Bayesian design approach are illustrated via case studies drawn from the nitroxide-mediated radical polymerization as an example. However, since this technique is perfectly general, it can be potentially applied to other polymerization variants.