Communication and information technologies are adding new capabilities for rapid and inexpensive customer input to all stages of the product development (PD) process. In this article we review six web-based methods of customer input as examples of the improved Internet capabilities of communication, conceptualization, and computation. For each method we give examples of user-interfaces, initial applications, and validity tests. We critique the applicability of the methods for use in the various stages of PD and discuss how they complement existing methods. For example, during the fuzzy front end of PD the information pump enables customers to interact with each other in a web-based game that provides incentives for truth-telling and thinking hard, thus providing new ways for customers to verbalize the product features that are important to them. Fast polyhedral adaptive conjoint estimation enables PD teams to screen larger numbers of product features inexpensively to identify and measure the importance of the most promising features for further development. Meanwhile, interactive web-based conjoint analysis interfaces are moving this proven set of methods to the web while exploiting new capabilities to present products, features, product use, and marketing elements in streaming multimedia representations. User design exploits the interactivity of the web to enable users to design their own virtual products thus enabling the PD team to understand complex feature interactions and enabling customers to learn their own preferences for new products. These methods can be valuable for identifying opportunities, improving the design and engineering of products, and testing ideas and concepts much earlier in the process when less time and money is at risk. As products move toward pretesting and testing, virtual concept testing on the web enables PD teams to test concepts without actually building

informs ® doi 10.1287/mnsc.1050.0379

Ongoing dialogues between customers and web-based virtual advisors (e.g., Kelley Blue Book’s Auto Choice Advisor) are an untapped source of marketing research data for new product opportunities based on new combinations of customer needs. These data provide the scale necessary for complex products (148 trucks and 129 customer needs in our application). The tracking data are available at little incremental cost. The calibration data rely on AIO data (activities, interests, and opinions) that are collected routinely by many firms. Because we embed Bayesian methods, calibration evolves readily and incrementally for new trucks (many enter each year) and for new features (technology evolves rapidly in this industry). There are five components to