Hierarchical Bayesian Approach and Genetic Algorithm in Illustrating Customer Preference and Market Segmentation

Nowadays, enterprises need to consider not only if the products or services they provided can conform to the needs of most consumers, but also how to satisfy the demand of a specific market segment and appeal them to repurchase as well due to the severe market competition. Besides, in an enterprise, the capability to both generate and collect data has also been expanded enormously and provides us with huge amount of data. It is noted that the number of such databases keeps growing rapidly because of the availability of powerful and affordable database management systems. To compete effectively in such an environment, business managers have changed their thoughts and treat the heterogeneity of customer as an important issue. Moreover, some enterprises start to study the customer heterogeneity when they have different brand choices. The main objective of this study is to explore the identification of customer heterogeneity and classification by using the Hierarchical Bayesian Model (HBM). Moreover, in order to resolve the efficiency issues regarding the reference estimates of the Hierarchical Bayesian model, we have integrated the genetic algorithm (GA) technology to expedite the speed of convergence amid the Markov Chain Monte Carlo (MCMC). Based on actual application results, we have discovered that the proposed method contains obvious supportive functions.