Knowledge that Transforms

An important role of informative advertising is to inform consumers of the simple fact that the shop that advertises sells a particular product. This information may help consumers to save on their search activities: instead of wandering around, a consumer can simply visit the shop that has advertised, knowing that there he can find the commodity he is looking for. The implications of this simple fact have not been studied before. Using game theoretic reasoning in a model that combines consumer search and firms' advertising we show that firms may find it optimal to advertise prices that are higher than nonadvertised prices. The important mechanism underlying this result is that advertising lowers the expected search cost for consumers. Through this analysis we provide a new insight into the role of informative advertising.

Standardizing performance expectations across different outlets within a chain, differing in their individual features, their consumers, and the nature of competition they face, can be an onerous task. We develop an integrated, nonlinear, block group-level market share model of store expectations that draws upon the existing trade area as well as store performance literatures. By incorporating and normalizing a large number of external and internal factors impacting performance, we are able to offer a means for the retailer to determine equitable standards. The model is estimated using a variation of the maximum-likelihood estimation, on a data set fashioned from several sources and aggregated at the block group and store levels. Finally, we propose a set of indices that allows us to evaluate relative performances of stores and regions given the competitive environments they face. We find that a block group-level model offers a better fit, as well as significantly richer implications, than a traditional store-level model. Results show that a significant number of stores operate well below their expected levels, an insight not obvious from the raw numbers used to report store statistics to upper management.

We develop a consumer response model to evaluate and plan pricing and promotions in durable-good markets. We discuss its implementation in the U.S. automotive industry, which “spends” about $45 billion each year in price promotions. The approach is based on a random effects multinomial nested logit model of product (e.g., a vehicle model, such as Hyundai Tucson), and transaction-type choice. Transaction types include combinations of acquisition types (e.g., purchase versus lease) and pricing instruments (cash rebates, reduced APR financing, lease payment discounts). We estimate the model using hierarchical Bayes methods to capture response heterogeneity at the local market level. We find key characteristics unique to durable-good markets. First, consumers are heterogeneous in both their brand and transaction-type preferences. Second, consumers differ in their overall price sensitivity as well as in their relative sensitivity to alternative pricing instruments (e.g., cash discounts, reduced monthly payments). Third, the most effective pricing programs tend to be those in which automakers offer consumers a menu of options to choose from (e.g., a choice among a cash discount, reduced interest rate financing, or a lease payment discount). We illustrate the model through an empirical application to a sample of data drawn from J.D. Power transaction records in the entry SUV segment and discuss examples of actual implementations.

Estimates of the prices customers are willing to pay for new products or services using responses from survey questionnaires are notoriously biased on the high side. An approach to obtaining more realistic estimates is suggested here, called the exaggeration bias-corrected contingent valuation method (EBC-CVM). The method is an alternative to conventional contingent valuation methods (CVMs) that have been used in economics and, to a lesser extent, in marketing. Two experiments and one field study are presented to demonstrate the effectiveness of the method. In each case, the proposed method outperformed conventional CVMs in comparison with real choices or more realistic price estimates.

Choice-based conjoint analysis is a popular marketing research technique to learn about consumers' preferences and to make market share forecasts under various scenarios for product offerings. Managers expect these forecasts to be “realistic” in terms of being able to replicate market shares at some prespecified or “base-case” scenario. Frequently, there is a discrepancy between the recovered and base-case market share. This paper presents a Bayesian decision theoretic approach to incorporating base-case market shares into conjoint analysis via the loss function. Because defining the base-case scenario typically involves a variety of management decisions, we treat the market shares as constraints on what are acceptable answers, as opposed to informative prior information. Our approach seeks to minimize the adjustment of parameters by using additive factors from a normal distribution centered at 0, with a variance as small as possible, but such that the market share constraints are satisfied. We specify an appropriate loss function, and all estimates are formally derived via minimizing the posterior expected loss. We detail algorithms that provide posterior distributions of constrained and unconstrained parameters and quantities of interest. The methods are demonstrated using discrete choice models with simulated data and data from a commercial market research study. These studies indicate that the method recovers base-case market shares without systematically distorting the preference structure from the conjoint experiment.

In this paper, we propose an individual-level approach to diffusion and growth models. By zooming in, we refer to the unit of analysis, which is a single consumer (instead of segments or markets) and the use of granular sales data (daily) instead of smoothed (e.g., annual) data as is more commonly used in the literature. By analyzing the high volatility of daily data, we show how changes in sales patterns can self-emerge as a direct consequence of the stochastic nature of the process. Our contention is that the fluctuations observed in more granular data are not noise, but rather consist of accurate measurement and contain valuable information. By stepping into the noise-like data and treating it as information, we generated better short-term predictions even at very early stages of the penetration process. Using a Kalman-Filter-based tracker, we demonstrate how movements can be traced and how predictions can be significantly improved. We propose that for such tasks, daily data with high volatility offer more insights than do smoothed annual data.

This paper seeks to quantify the long-term financial impact of negative word of mouth (NWOM), an issue that has long challenged extant research. We do so with real-world data on firm security prices. The developed time-series models innovatively uncover (1) short- and long-term effects of NWOM on cash flows, stock returns, and stock volatilities, and (2) NWOM's “wear-in” effects (i.e., it takes a number of months before the stock price impact of NWOM reaches the peak point) and “wear-out” effects (i.e., it takes several months after the peak before the stock price impact of NWOM dies out completely). In addition, the results related to endogeneity and feedback effects from the stock market are also interesting, supporting the idea that historical underperformance in stock prices may breed more harmful future buzz in a “vicious” cycle of NWOM. After controlling for competition, NWOM's long-term financial harm becomes more destructive in magnitude, kicks in more quickly, and haunts investors longer. Overall, these findings offer some unique implications for buzz management, time-series models quantifying the financial impact of word of mouth, and the marketing-finance interface.

This paper designs an optimal mechanism for selling a set of commonly ranked objects. Although buyers rank these objects in the same order, the rates at which their valuations change for a less-preferred object might be different. Four stylized cases are identified according to this difference: parallel, convergent, divergent, and convergent-then-divergent. In general, the optimal mechanism cannot be interpreted as a conventional second-price auction. A reserve price is imposed for each object. Depending on which of the four stylized cases is considered, a higher-value bidder may be allocated a higher-ranked or lower-ranked object. There is also a positive probability that a higher-ranked object is not allocated while a lower-ranked one is allocated. In a departure from the extant mechanism-design literature, the individual-rationality constraint for a mid-range type of bidder can be binding.

Tele.ring is a mobile phone organization selling contracts and cell phones in the Austrian market. The market situation in 2005 was highly competitive and dynamic, resulting in relatively short tariff life cycles. Excessively long lead times made tele.ring's management feel dissatisfied with their new tariff development process. Furthermore, a new competitor had entered the market, posing a major threat, and it was unclear how to effectively safeguard tele.ring's position in the market. In cooperation with the management, we implemented and tested a new segmentation, targeting, and positioning tool, which provides managers with information on their target markets, customer preferences, competitors' strengths, and customer segments. It allows for the simultaneous visualization of these data on a single map and facilitates timely and accurate decision making. In particular, we report on the design and the implementation of a new pricing scheme, “Formel 10,” which became the most successful new tariff introduction in this competitive market. tele.ring's managers were very much impressed with our tool's ability to represent the market on a single map and with its capacity to allow for intuitive interpretation. In addition, the tool enhanced internal communication between its users and different stakeholders during the new tariff development process.