A Case Study from the Perfume Industry
This tutorial covers Driver Analysis and Product Optimization with Bayesian networks and BayesiaLab. It provides hands-on examples of how Bayesian networks can be used effectively in the field of marketing science.
In this study we want to examine how product attributes, i.e. fragrance characteristics perceived by consumers, relate to purchase intent for specific perfumes. Given the large number of attributes in our study, we also want to identify common concepts among these attributes in order to make interpretation easier and communication with managerial decision makers more effective.
Secondly, we want to utilize the generated understanding of consumer dynamics so product developers can optimize the characteristics of the perfumes under study in order to increase purchase intent among consumers, which is our ultimate business objective. Using Bayesian networks - and BayesiaLab as the software tool - this optimization process is feasible for the first time and represents a major innovation for product development.