New Product Development 2.0: Preference Markets—How Scalable Securities Markets Identify Winning Product Concepts and Attributes*

Preference markets address the need for scalable, fast, and engaging market research in new product development. The Web 2.0 paradigm, in which users contribute numerous ideas that may lead to new products, requires new methods of screening those ideas for their marketability, and preference markets offer just such a mechanism. For faster new product development decisions, a flexible prioritization methodology is implemented for product features and concepts, one that scales up in the number of testable alternatives, limited only by the number of participants. New product preferences for concepts, attributes, and attribute levels are measured by trading stocks whose prices are based upon share of choice of new products and features. A conceptual model of scalable preference markets is developed and tested experimentally. Benefits of the methodology are found to include speed (less than one hour per trading experiment), scalability (question capacity grows linearly in the number of traders),  flexibility (features and concepts can be tested simultaneously), and respondent enthusiasm for the method.

[1]  E. Fama,et al.  Efficient Capital Markets : II , 2007 .

[2]  Karl T. Ulrich,et al.  Research Note: User Design of Customized Products , 2007 .

[3]  M. Sawhney,et al.  Collaborating to create: The Internet as a platform for customer engagement in product innovation , 2005 .

[4]  Thomas A. Rietz,et al.  Wishes, expectations and actions: a survey on price formation in election stock markets , 1999 .

[5]  Stephen J. Hoch,et al.  Perceived consensus and predictive accuracy: The pros and cons of projection. , 1987 .

[6]  Martin Spann,et al.  Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters , 2009 .

[7]  David M. Pennock,et al.  Prediction Markets: Does Money Matter? , 2004, Electron. Mark..

[8]  T. Palfrey,et al.  Asset Valuation in an Experimental Market , 1982 .

[9]  Olivier Toubia Idea Generation, Creativity, and Incentives , 2006 .

[10]  Joel Huber,et al.  The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice , 1993 .

[11]  H. Ernst,et al.  Identification of Lead Users for Consumer Products via Virtual Stock Markets , 2009 .

[12]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[13]  David M. Pennock,et al.  The Real Power of Artificial Markets , 2001, Science.

[14]  John R. Hauser,et al.  Fast Polyhedral Adaptive Conjoint Estimation , 2002 .

[15]  Allan Timmermann,et al.  How Learning in Financial Markets Generates Excess Volatility and Predictability in Stock Prices , 1993 .

[16]  Greg M. Allenby,et al.  A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules , 2004 .

[17]  C. Whan Park,et al.  Choosing What I Want versus Rejecting What I Do Not Want: An Application of Decision Framing to Product Option Choice Decisions , 2000 .

[18]  V. Smith,et al.  Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets , 1988 .

[19]  C. Plott,et al.  Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets , 1988 .

[20]  V. Smith Microeconomic Systems as an Experimental Science , 1982 .

[21]  Ely Dahan,et al.  The virtual customer , 2002 .

[22]  S. Sunder MARKET FOR INFORMATION: EXPERIMENTAL EVIDENCE' , 1992 .

[23]  Stephen J. Hoch,et al.  Who Do We Know: Predicting the Interests and Opinions of the American Consumer , 1988 .

[24]  Oded Netzer,et al.  Alternative Models for Capturing the Compromise Effect , 2004 .

[25]  J. Orlin,et al.  Greedoid-Based Noncompensatory Inference , 2007 .

[26]  Steven M. Shugan The Cost Of Thinking , 1980 .

[27]  R. Lucas Expectations and the neutrality of money , 1972 .

[28]  Matthew J. Salganik,et al.  Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market , 2006, Science.

[29]  J. Davitz,et al.  A survey of studies contrasting the quality of group performance and individual performance, 1920-1957. , 1958, Psychological bulletin.

[30]  Roland T. Rust,et al.  Feature Fatigue: When Product Capabilities Become Too Much of a Good Thing , 2005 .

[31]  Robert Forsythe,et al.  Anatomy of an Experimental Political Stock Market , 1992 .

[32]  Martin Spann,et al.  Internet-Based Virtual Stock Markets for Business Forecasting , 2003, Manag. Sci..

[33]  Steven H. Cohen,et al.  Choice Menus for Mass Customization: An Experimental Approach for Analyzing Customer Demand with an Application to a Web-Based Information Service , 2001 .

[34]  Richard P. Larrick,et al.  Intuitions About Combining Opinions: Misappreciation of the Averaging Principle , 2006, Manag. Sci..

[35]  Kenneth Oliven,et al.  Suckers Are Born but Markets Are Made: Individual Rationality, Arbitrage, and Market Efficiency on an Electronic Futures Market , 2004, Manag. Sci..

[36]  A. Mackinlay,et al.  Event Studies in Economics and Finance , 1997 .

[37]  C. Plott,et al.  Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models , 1982, Journal of Political Economy.

[38]  E. Fama EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[39]  Tomaso Poggio,et al.  Securities Trading of Concepts (STOC) , 2011 .

[40]  Anita Elberse,et al.  The Power of Stars: Do Star Actors Drive the Success of Movies? , 2007 .

[41]  R. Hanson,et al.  Information aggregation and manipulation in an experimental market , 2006 .

[42]  J. Eliashberg,et al.  Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures , 2003 .