Bundling is pervasive in the market, examples include desktop computer bundles, digital single-lens reflex camera kits and cookware sets, to name a few. The advancement in information technology allows more and more companies to provide customized bundles to customers. Wind and Mahajan (1997) recognize the importance of researching mass customization and suggest companies to use consumers’ input “as a response (to a conjoint analysis-type task) that provides operational guidelines for the design of products to inventory for the segment that is not willing to pay the premium required for customized products”.
In addition to conjoint analysis, researchers and practitioners are using “build-your-own-bundle” or configuration approach. In a configuration study, participants are presented with a menu from which they can choose individual items to build up their desired product bundle. The process mimics the real decision process, is easy to implement, and is straight forward for participants to understand. However, as the size of the menu grows, the number of possible bundles grows geometrically. This results in computation difficulties.
This dissertation investigates the application of configuration approach, and examines if it extends and complements the choice-based conjoint (CBC) approach. We first develop an aggregate model for analyzing configuration data. We show analytically that the aggregate choice model consistent with configuration data has a closed form representation which takes the form of a Multivariate Logistic (MVL) model. WE discuss the strengths and weaknesses of the configuration approach.
Because configuration and conjoint data tasks have different strengths and weaknesses, taking advantages of these two choice tasks may improve the understanding of consumer preferences for bundles. A fundamental assumption in the data fusion literature is that the same decision making process is applied under different choice tasks. We examine if consumer decision making process is the same under CBC and configuration studies by comparing the estimation results from CBC and conjoint studies. We show that these two procedures may not be fully comparable. To combine the two data sources we need a data fusion model that takes into account the differences to obtain a reasonable result.