Using Online Auctions to Choose Optimal Product Configurations

In the current environment, product design involves choosing from a vast array of components and subcomponents. By developing modular platforms and communicating with multiple suppliers, each product becomes a bundle of features. The multitude of potential product configurations poses a challenge in identifying optimal configurations to offer customers. For most companies, the greatest challenge is measuring consumers’ marginal value for enhanced features. If this data were available, companies would offer those configurations with the highest margin. To date, consumers’ value for product features was estimated using decision analysis techniques, surveys, or conjoint analysis. This research proposes a different methodology for measuring consumers’ value. The emergence of active auction markets for a wide variety of products and services provides a venue for calibrating customer preferences. By offering different bundles of features, a company can measure the marginal increase in auction price obtained from enhanced features. Coupled with cost data, this information facilitates the evaluation of gross profit margin from offering different configurations. The methodology is assessed on used laptop computers sold via auction. Analyzing auction results indicates that consumers do value better features, and the incremental value of enhancements varies across features. Estimating cost differences from online posted prices in non-auction situations provide a foundation for estimating the efficient frontier of optimal bundles of features in a value-cost space. Data envelopment analysis is used in this context to define the efficient frontier. As online auction markets expand and evolve, this methodology could be implemented for many new products and services, which are offered as a bundle of features. Examples include many consumer electronics, travel packages, and communication services.