Firms are increasingly using technology to enable targeted, or “personalized,” pricing strategies. In settings where prices are transparent to all consumers, however, there is the potential for interpersonal price differences to be perceived as inherently unfair. In response, firms may strategically obfuscate their prices so that direct interpersonal com-parisons are more difficult. The feasibility of such a pricing strategy is not well understood. In this paper, we investigate the conditions under which it is profitable for firms to engage in price obfuscation, given the potential fairness concerns of consumers. We study how price obfuscation affects consumer fairness concerns, consumer demand, and equilibrium pricing strategies. The findings suggest that if obfuscation mitigates fairness concerns, it can arise as an equilibrium outcome, even if consumers are aware of the seller’s strategic behavior and are able to update their beliefs and expectations about the prices offered to their peers accordingly. To test the theoretical predictions, an experiment is conducted in which price obfuscation is varied both exogenously and endogenously. The results confirm that buyers have intrinsic distributional (based on the seller’s margins) and peer-induced fairness (due to others being charged different prices) concerns when prices are transparent. In particular, disadvantaged peer-induced fairness concerns enter utility as an intrinsic cost that the seller has to compensate for through lower prices. Obfuscation effectively reduces peer-induced fairness concerns and increases sellers’ pricing power. However, this pricing power is constrained by distributive inequity becoming more salient when prices are obfuscated.
论文原文：
Allender, W. J., et al. (2021). "Price fairness and strategic obfuscation." Marketing Science 40(1): 122-146.
We assess the impact of home-sharing on residential house prices and rents. Using a data set of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we show that Airbnb has a positive impact on house prices and rents. This effect is stronger in zip codes with a lower share of owner-occupiers, consistent with non-owner-occupiers being more likely to reallocate their homes from the long-to the short-term rental market. At the median owner-occupancy rate zip code, we find that a 1% increase in Airbnb listings leads to a 0.018% increase in rents and a 0.026% increase in house prices. Considering the median annual Airbnb growth in each zip code, these results translate to an annual increase of $9 in monthly rent and $1,800 in house prices for the median zip code in our data, which accounts for about one-fifth of actual rent growth and about one-seventh of actual price growth. Finally, we formally test whether the Airbnb effect is due to the reallocation of the housing supply. Consistent with this hy-pothesis, we find that although the total supply of housing is not affected by the entry of Airbnb, Airbnb listings increase the supply of short-term rental units and decrease the supply of long-term rental units.
论文原文：
Barron, K., et al. (2021). "The effect of home-sharing on house prices and rents: Evidence from Airbnb." Marketing Science 40(1): 23-47.
Understanding consumer preferences is important for new product management, but is famously challenging in the absence of actual sales data. Stated-preference data are relatively cheap but less reliable, whereas revealed-preference data based on actual choices are reliable but expensive to obtain prior to product launch. We develop a cost-effective solution. We argue that people do not automatically know their preferences, but can make an effort to acquire such knowledge when given sufficient incentives. The method we develop regulates people’s preference-learning incentives using a single parameter, realization probability, meaning the probability with which an individual has to actually purchase the product she says she is willing to buy. We derive a theoretical relationship between realization probability and elicited preferences. This allows us to forecast demand in real purchase settings using inexpensive choice data with small to moderate realization probabilities. Data from a large-scale field experiment support the theory and demonstrate the predictive validity and cost-effectiveness of the pro-posed method.
论文原文：
Cao, X. and J. Zhang (2021). "Preference learning and demand forecast." Marketing Science 40(1): 62-79.
Firms use coupons to stimulate demand. Although couponing is popular in practice, limited research has examined the causal effects of coupons on visit, search, and purchase behaviors among heterogeneous customers. In this paper, we examine coupon effects using data from a randomized field experiment with an online retailer in which customers were divided into two heterogeneous customer segments (low value and high value) with two types of coupon discounts (base value and better value). We find couponing is effective in increasing revenue, primarily by attracting customers who purchase without coupon redemption, and the lift in revenue per customer is larger for the high-value segment. Using clickstream data of customer visit and search behavior, we find most of the revenue lift arises from a corresponding lift in the likelihood of visiting the website under couponing. Though the lift in visit likelihood is relatively homogeneous across customer segments under the base coupon, the high-value segment has a higher purchase conversion rate than the low-value segment, leading to an amplified revenue lift. We also find a deeper discount leads to higher redemption and purchase conversion for the high-value segment but does not change visit likelihood. Finally, most of the search behaviors are unchanged under couponing, suggesting the mix of customers brought in under couponing are similar to those who visit without receiving coupons.
Gopalakrishnan, A. and Y. H. Park (2021). "The impact of coupons on the visit-to-purchase funnel." Marketing Science 40(1): 48-61.
Motivated by their increasing prevalence, we study outcomes when competing sellers use machine learning algorithms to run real-time dynamic price experiments. These algorithms are often misspecified, ignoring the effect of factors outside their control, for example, competitors’ prices. We show that the long-run prices depend on the informational value (or signal-to-noise ratio) of price experiments: if low, the long-run prices are consistent with the static Nash equilibrium of the corresponding full information setting. However, if high, the long-run prices are supra-competitive—the full information joint monopoly outcome is possible. We show that this occurs via a novel channel: competitors’ algorithms’ prices end up running correlated experiments. Therefore, sellers’ misspecified models overestimate the own price sensitivity, resulting in higher prices. We discuss the implications on competition policy.
论文原文：
Hansen, K. T., et al. (2021). "Frontiers: Algorithmic collusion: Supra-competitive prices via independent algorithms." Marketing Science 40(1): 1-12.
In response to growing environmental concerns, governments have promoted products that are less harmful to the environment—green products—through various incentives. We empirically study the impact of a commonly used nonmonetary incentive— namely, the single-occupancy permission to high-occupancy vehicle (HOV) lanes—on green and non-green product demand in the U.S. automobile industry. The HOV incentive could increase unit sales of green vehicles by enhancing their functional value through time saving. On the other hand, the incentive may prove counterproductive if it reduces the symbolic value (i.e., signaling a proenvironmental image) consumers derive from green vehicles. Assessing the effectiveness of green-product incentives is challenging, given the endogenous nature of governments’ incentive provisions. To identify the effect of the HOV incentive on unit sales of green and non-green vehicles, we take advantage of HOV-incentive changes in two states, and we employ a multitude of quasi-experimental methods using a data set at the county–model–month level. Unlike previous studies that only examine the launch of the HOV incentive and find an insignificant association be-tween incentive launch and green-vehicle demand, we concentrate on its termination. We find that the termination of the HOV incentive decreases unit sales of vehicles covered by the incentive by 14.4%. We provide suggestive evidence that this significant negative effect of HOV-incentive termination is due to the elimination of the functional value the incentive provides: time saving. Specifically, we find that the negative effect is more pronounced in counties where consumers value time saving more (i.e., counties with a longer commute to work and higher income). Additionally, in line with prior literature, the launch of the HOV incentive is not found to have a significant effect on green-vehicle sales. Combined, our findings reveal that the effect of termination is not simply the opposite of that of launch, implying that governments’ green-product incentives could backfire.
论文原文：
He, C., et al. (2021). "The end of the express road for hybrid vehicles: Can governments’ green product incentives backfire?" Marketing Science 40(1): 80-100.
This research analyzes a firm’s investment in advertising that signals quality when consumers learn about quality not only from such advertising but also from interactions with other consumers in the form of observational learning or word of mouth. Further, word-of-mouth interactions may involve underreporting (not everyone shares experiences), positivity (positive experiences are communicated more widely than negative ones), or negativity (negative experiences are communicated more widely than positive ones). The analysis focuses on whether a firm should advertise more or less aggressively in the presence of such consumer interactions compared with their absence and offers four key insights. First, consumer interactions can amplify the signaling effect of advertising, and as a consequence, to prevent mimicking it may be optimal for a high-quality firm to become more aggressive and spend more on advertising to signal quality in the presence of such interactions than without. Second, as underreporting increases, it can be optimal to reduce advertising, sometimes significantly. Third, with increasing positivity, it can be optimal to increase advertising. Fourth, even with increasing nega-tivity, under certain conditions it may still be optimal to increase advertising rather than decrease it.
论文原文：
Joshi, Y. V. and A. Musalem (2021). "When consumers learn, money burns: Signaling quality via advertising with observational learning and word of mouth." Marketing Science 40(1): 168-188.
It is well understood that a downstream firm’s service can impact the performance of vertical channels. Although many academic works address the service provision of the downstream firm, empirically quantifying the impact has been challenging because the downstream firm’s service is often unobservable to the researcher. I propose a new empirical framework that incorporates the downstream firm’s unobserved endogenous service provision by modifying the standard demand model. I apply this empirical framework to proprietary data from a franchise network in the car radiator market to quantify the downstream firms’ (e.g., franchisees’) endogenous service. Counterfactuals under maximum resale price maintenance (RPM) policies show that the standard demand model ignoring the franchisees’ endogenous service reduction (i.e., service externality) results in more optimistic counterfactual predictions than the developed framework does. Such service externality can be mitigated if the service provision cost is lower for fran-chisees. Last, I examine boundary conditions: under the extreme regime of maximum RPM aiming to fully extract franchisees’ profit, I find that information asymmetry is a greater concern for the upstream firm within the focal industry. Additionally, when service ex-ternality is combined with channel information asymmetry, maximum RPM at such ex-tremes may no longer increase the franchisor’s profit.
论文原文：
Kim, T. T. (2021). "When franchisee service affects demand: An application to the car radiator market and resale price maintenance." Marketing Science 40(1): 101-121.
