Using Markov Chain Marginal Bootstrap in Optimizing Marketing Strategies

Resumo

In marketing law, models such as Markov chain models are gaining popularity with every passing day, thanks to effective marketing strategies in acquiring and retaining customers. However, the accuracy of the models is often put into question due to the insufficiencies regarding the estimation of model parameters, which tend to be complicated. In this article, however, we aim at solving this problem by introducing the Markov Chain Marginal Bootstrap method, which looks up to an efficient estimation framework by greatly modifying the biasness state. Using this methodology, we explored the uncertainties surrounding parameter estimation. This thesis demonstrates how MCMB improves estimation capability by overcoming data uncertainty and heterogeneity among other factors. With improved parameter estimation, MCMB enhances our understanding of customer behaviour and provides us with the ability to design better marketing strategies. Additionally, we present the application of the MCMB procedure to the analysis of the data from the subscription business model and discuss its possibilities in the analysis and forecasting of customer behaviour. Other issues emphasized in the article include optimizing the marketing campaign strategy by implementing better approaches while considering the parameter uncertainty limitations discussed earlier in the article.