"The Best Price You'll Ever Get" The 2005 Employee Discount Pricing Promotions in the U.S. Automobile Industry

During the summer of 2005, the Big Three U.S. automobile manufacturers offered a customer promotion that allowed customers to buy new cars at the discounted price formerly offered only to employees. The initial months of the promotion were record sales months for each of the Big Three firms, suggesting that customers thought that the prices offered during the promotions were particularly attractive. In fact, such large rebates had been available before the employee discount promotion that many customers paid higher prices following the introduction of the promotions than they would have in the weeks just before. We hypothesize that the complex nature of auto prices, the fact that prices are negotiated rather than posted, and the fact that buyers do not participate frequently in the market leads customers to rely on "price cues" in evaluating how good current prices are. We argue that the employee discount pricing promotions were price cues, and that customers responded to the promotions as a signal that prices were discounted.

Direct and Indirect Bargaining Costs and the Scope of the Firm

Forthcoming publication in The Journal of BusinessCenter for Innovation and Product Development (CIPD) and the Lean Aerospace Initiative (LAI

Advertising in a Competitive Market: The Role of Product Standards, Customer Learning, and Switching Costs

Standard models of competition predict that firms will sell less when competitors target their customers with advertising. This is particularly true in mature markets with many competitors that sell relatively undifferentiated products. However, the authors present findings from a large-scale randomized field experiment that contrast sharply with this prediction. The field experiment measures the impact of competitors' advertising on sales at a private label apparel retailer. Surprisingly, for a substantial segment of customers, the competitors' advertisements increased sales at this retailer. This robust effect was obtained through experimental manipulation and by measuring actual purchases from large samples of randomly assigned customers. The effect size is also large, with customers ordering more than 4% more items in some categories in the treatment condition (vs. the control). The authors examine how these positive spillovers vary across product categories to illustrate the importance of product standards, customer learning, and switching costs. The findings have the potential to change our understanding of competition in mature markets

Fast Polyhedral Adaptive Conjoint Estimation

We propose and test a new adaptive conjoint analysis method that draws on recent polyhedral “interior-point” developments in mathematical programming. The method is designed to offer accurate estimates after relatively few questions in problems involving many parameters. Each respondent’s ques-tions are adapted based upon prior answers by that respondent. The method requires computer support but can operate in both Internet and off-line environments with no noticeable delay between questions. We use Monte Carlo simulations to compare the performance of the method against a broad array of relevant benchmarks. While no method dominates in all situations, polyhedral algorithms appear to hold significant potential when (a) metric profile comparisons are more accurate than the self-explicated importance measures used in benchmark methods, (b) when respondent wear out is a concern, and (c) when product development and/or marketing teams wish to screen many features quickly. We also test hybrid methods that combine polyhedral algorithms with existing conjoint analysis methods. We close with suggestions on how polyhedral methods can be used to address other marketing problems.Sloan School of Management and the Center for Innovation in Product Development at MI

Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales

Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. However, information technology in general and Internet markets in particular have the potential to substantially increase the collective share of niche products, thereby creating a longer tail in the distribution of sales. This paper investigates the Internet's “long tail” phenomenon. By analyzing data collected from a multichannel retailer, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution when compared with traditional channels. Previous explanations for this result have focused on differences in product availability between channels. However, we demonstrate that the result survives even when the Internet and traditional channels share exactly the same product availability and prices. Instead, we find that consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet. If the relationships we uncover persist, the underlying trends in technology portend an ongoing shift in the distribution of product sales.MIT Center for Digital BusinessNational Science Foundation (U.S.) (Grant IIS-0085725

Application and Test of Web-based Adaptive Polyhedral Conjoint Analysis

In response to the need for more rapid and iterative feedback on customer preferences, researchers are developing new web-based conjoint analysis methods that adapt the design of conjoint questions based on a respondent’s answers to previous questions. Adapting within a respondent is a difficult dy-namic optimization problem and until recently adaptive conjoint analysis (ACA) was the dominant method available for addressing this adaptation. In this paper we apply and test a new polyhedral method that uses “interior-point” math programming techniques. This method is benchmarked against both ACA and an efficient non-adaptive design (Fixed). Over 300 respondents were randomly assigned to different experimental conditions and were asked to complete a web-based conjoint exercise. The conditions varied based on the design of the con-joint exercise. Respondents in one group completed a conjoint exercise designed using the ACA method, respondents in another group completed an exercise designed using the Fixed method, and the remaining respondents completed an exercise designed using the polyhedral method. Following the conjoint exer-cise respondents were given $100 and allowed to make a purchase from a Pareto choice set of five new-to-the-market laptop computer bags. The respondents received their chosen bag together with the differ-ence in cash between the price of their chosen bag and the$100. We compare the methods on both internal and external validity. Internal validity is evaluated by comparing how well the different conjoint methods predict several holdout conjoint questions. External validity is evaluated by comparing how well the conjoint methods predict the respondents’ selections from the choice sets of five bags. The results reveal a remarkable level of consistency across the two validation tasks. The polyhe-dral method was consistently more accurate than both the ACA and Fixed methods. However, even better performance was achieved by combining (post hoc) different components of each method to create a range of hybrid methods. Additional analyses evaluate the robustness of the predictions and explore al-ternative estimation methods such as Hierarchical Bayes. At the time of the test, the bags were proto-types. Based, in part, on the results of this study these bags are now commercially available.The Sloan School of Management, the Center for Innovation in Product Development at MIT and the EBusiness Center at MI

Harbingers of Failure

The authors identify customers, termed “Harbingers of failure,” who systematically purchase new products that flop. Their early adoption of a new product is a strong signal that a product will fail—the more they buy, the less likely the product will succeed. Firms can identify these customers through past purchases of either new products that failed or existing products that few other customers purchase. The authors discuss how these insights can be readily incorporated into the new product development process. The findings challenge the conventional wisdom that positive customer feedback is always a signal of future success

Optimizing Product Line Designs: Efficient Methods and Comparisons

We compare a broad range of optimal product line design methods. The comparisons take advantage of recent advances that make it possible to identify the optimal solution to problems that are too large for complete enumeration. Several of the methods perform surprisingly well, including Simulated Annealing, Product-Swapping and Genetic Algorithms. The Product-Swapping heuristic is remarkable for its simplicity. The performance of this heuristic suggests that the optimal product line design problem may be far easier to solve in practice than indicated by complexity theory

Decision Stages and Asymmetries in Regular Retail Price Pass-Through

We study the pass-through of wholesale price changes onto regular retail prices using an unusually detailed data set obtained from a major retailer. We model pass-through as a two-stage decision process that reflects both whether as well as how much to change the regular retail price. We show that pass-through is strongly asymmetric with respect to wholesale price increases versus decreases. Wholesale price increases are passed through to regular retail prices 70% of the time while wholesale price decreases are passed through only 9% of the time. Pass-through is also asymmetric with respect to the magnitude of the wholesale price change, with the magnitude affecting the response to wholesale price increases but not decreases. Finally, we show that covariates such as private label versus national brand, 99-cent price endings, and the time since the last wholesale price change have a much stronger impact on the first stage of the decision process (i.e., whether to change the regular retail price) than on the second stage (i.e., how much to change the regular retail price)