Ely Dahan

The Predictive Power of Internet-Based Product Concept Testing Using Visual Depiction and Animation

One critical step in new product development is selecting from among multiple possible product concepts the one that the firm will carry forward into the marketplace. There is a need for low-cost, parallel testing of the appeal of new product concepts, the results of which closely mirror ultimate market performance. In this article, the authors first describe an Internet-based product concept testing method they developed that incorporates virtual prototypes of new product concepts, substituting them for physical prototypes. The method can be used with either static representations of the products or with dynamic representations that demonstrate how the product works through a simulated video clip of its operation. The objective of this method is to allow design teams to select the best of several new concepts within a product category with which to proceed, without having to develop physical prototypes. The authors then provide a rigorous test of both virtual prototype methods against tests using both physical prototypes and attribute-only (i.e., no visuals), full-profile conjoint analysis. Nine concepts compete against two actual products in the tests. Market shares from the test using the physical prototypes are defined as the “actual” market shares. Predicted market shares for the attribute-only, full-profile conjoint analysis and each of the two virtual prototype methods are compared to those obtained for the physical prototypes. Both static and animated virtual prototype tests produced market shares that closely mirrored those obtained with the physical products, outperforming the set of predictions across the full range of products produced in the attribute-only conjoint analysis. Interestingly, the attribute-only conjoint analysis identified the top three products, in correct order. It was unable to differentiate performance below these top three products. Furthermore, it predicted market shares for the top three products to be well below those achieved using physical prototypes. As virtual prototypes cost considerably less to build and test than their physical counterparts, design teams using Internet-based product concept research may be able to afford to explore a much larger number of concepts. Virtual prototypes and the testing methods associated with them may help reduce the uncertainty and cost of new product introductions by allowing more ideas to be concept tested in parallel with target consumers.

An Extreme-Value Model of Concept Testing

We model concept testing in new product development as a search for the most profitable solution to a design problem. When allocating resources, developers must balance the cost of testing multiple designs against the potential profits that may result. We propose extreme-value theory as a mathematical abstraction of the concept-testing process. We investigate the trade-off between the benefits and costs of parallel concept testing and derive closed-form solutions for the case of profits that follow extreme-value distributions. We analyze the roles of the scale and tail-shape parameters of the profit distribution as well as the cost of testing in determining the optimal number of tests and total budget for the concept phase of NPD. Using an example, we illustrate how to estimate and interpret the scale and tail-shape parameters. We find that the impact of declining concept-testing costs on expected profits, the number of concepts tested, and total spending depend on the scale/cost ratio and tail-shape parameter of the profit distribution.

Securities trading of concepts (STOC)

Before bringing a new product to market it is necessary to perform a significant amount of testing to determine whether the product is poised to be a “winner” or a “loser.” Traditionally individual and aggregate preferences have been measured using surveys, focus groups, concept tests and conjoint studies. However, these methods can be biased, costly and time-consuming to conduct, particularly when evaluating large numbers of concepts. Through their research, Dahan, Lo, Poggio, Chan and Kim seek to develop an alternative method for efficiently measuring preferences and more accurately predicting new product success based on the efficiency and incentive-compatibility of security trading markets.

Securities Trading of Concepts (STOC)

Identifying winning new product concepts can be a challenging process that requires insight into private consumer preferences. To measure consumer preferences for new product concepts, the authors apply a “securities trading of concepts,” or STOC, approach, in which new product concepts are traded as financial securities. The authors apply this method because market prices are known to efficiently collect and aggregate private information regarding the economic value of goods, services, and firms, particularly when trading financial securities. This research compares the STOC approach against stated-choice, conjoint, constant-sum, and longitudinal revealed-preference data. The authors also place STOC in the context of previous research on prediction markets and experimental economics. Across multiple product categories, the authors test whether STOC (1) is more cost efficient than other methods, (2) passes validity tests, (3) measures expectations of others, and (4) reveals individual preferences, not just those of the crowd. The results show that traders exhibit a self-preference bias when trading. Ultimately, STOC offers two key advantages over traditional market research methods: cost efficiency and scalability. For new product development teams deciding how to invest resources, this scalability may be especially important in the Web 2.0 world.

Preference Markets: Organizing Securities Markets for Opinion Surveys with Infinite Scalability

Preference markets address the need for scalable, fast and engaging market research. The Web 2.0 paradigm, in which users contribute numerous ideas that may lead to new products, requires new methods of filtering those ideas for their marketability, and preference markets offer just such a mechanism. For faster new product development decisions, we implement a flexible prioritization methodology for product features and concepts, one that scales up in the number of testable alternatives, limited only by the number of participants. Preferences are measured by trading stocks whose prices are based upon share of choice of new products and features. We develop a conceptual model of scalable preference markets, and test it experimentally. We find that benefits of the methodology 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.

Preference Markets in New Product Development

Abstract 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 for product features and concepts is tested. It 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. Benefits of preference markets include speed, scalability, flexibility, and respondent enthusiasm for the method.

The Voice of the Patient Methodology: A Novel Mixed-Methods Approach to Identifying Treatment Goals for Men with Prostate Cancer

BackgroundMany guidelines advocate the use of shared decision making for men with newly diagnosed prostate cancer. Decision aids can facilitate the process of shared decision making. Implicit in this approach is the idea that physicians understand which elements of treatment matter to patients. Little formal work exists to guide physicians or developers of decision aids in identifying these attributes. We use a mixed-methods technique adapted from marketing science, the ‘Voice of the Patient’, to describe and identify treatment elements of value for men with localized prostate cancer.MethodsWe conducted semi-structured interviews with 30 men treated for prostate cancer in the urology clinic of the West Los Angeles Veteran Affairs Medical Center. We used a qualitative analysis to generate themes in patient narratives, and a quantitative approach, agglomerative hierarchical clustering, to identify attributes of treatment that were most relevant to patients making decisions about prostate cancer.ResultsWe identified five ‘traditional’ prostate cancer treatment attributes: sexual dysfunction, bowel problems, urinary problems, lifespan, and others’ opinions. We further identified two novel treatment attributes: a treatment’s ability to validate a sense of proactivity and the need for an incision (separate from risks of surgery).ConclusionsApplication of a successful marketing technique, the ‘Voice of the Customer’, in a clinical setting elicits non-obvious attributes that highlight unique patient decision-making concerns. Use of this method in the development of decision aids may result in more effective decision support.

A Bayesian hierarchical model for discrete choice data in health care

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.