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Using information on advertising and mortgages originated by subprime lenders, we study whether advertising helped consumers find cheaper mortgages. Lenders that advertise more within a region sell more expensive mortgages, measured as the excess rate of a mortgage after accounting for borrower, contract, and regional characteristics. These effects are stronger for mortgages sold to less sophisticated consumers. We exploit regional variation in mortgage advertising induced by the entry of Craigslist and other tests to demonstrate that these findings are not spurious. Analyzing advertising content reveals that initial/introductory rates are frequently advertised in a salient fashion, where reset rates are not.

How do firms respond to entry in multi-sided markets? We address this question by studying the impact of Craigslist, a website providing classified-advertising services, on local U.S. newspapers. We exploit temporal and geographical variation in Craigslist's entry to show that newspapers with greater reliance on classified-ad revenue experience a larger drop in classified-ad rates after Craigslist's entry. The impact of Craigslist's entry on the classified-ad side appears to propagate to other sides of the newspapers' market. On the subscriber side, these newspapers experience an increase in subscription prices, a decrease in circulation, and an increase in differentiation from each other. On the display-ad side, affected newspapers experience a decrease in display-ad rates. We also find evidence that affected newspapers are less likely to make their content available online. Finally, we estimate that Craigslist's entry leads to $5.0 billion (year 2000 dollars) in savings to classified-ad buyers during 2000-2007. This paper was accepted by Sandra Slaughter, information systems.

The phenomenon of sponsored search advertising—where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results—is gaining ground as the largest source of revenues for search engines. Using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. Our paper proposes a novel framework to better understand the factors that drive differences in these metrics. We use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods. Using a simultaneous equations model, we quantify the relationship between various keyword characteristics, position of the advertisement, and the landing page quality score on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision. Specifically, we find that the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank as one goes down the search engine results page. Though search engines take into account the current period's bid as well as prior click-through rates before deciding the final rank of an advertisement in the current period, the current bid has a larger effect than prior click-through rates. We also find that an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser's cost per click. Furthermore, our analysis shows that keywords that have more prominent positions on the search engine results page, and thus experience higher click-through or conversion rates, are not necessarily the most profitable ones—profits are often higher at the middle positions than at the top or the bottom ones. Besides providing managerial insights into search engine advertising, these results shed light on some key assumptions made in the theoretical modeling literature in sponsored search.

This paper studies the effects of various types of online advertisements on purchase conversion by capturing the dynamic interactions among advertisement clicks themselves. It is motivated by the observation that certain advertisement clicks may not result in immediate purchases, but they stimulate subsequent clicks on other advertisements, which then lead to purchases. We develop a novel model based on mutually exciting point processes, which consider advertisement clicks and purchases as dependent random events in continuous time. We incorporate individual random effects to account for consumer heterogeneity and cast the model in the Bayesian hierarchical framework. We construct conversion probability to properly evaluate the conversion effects of online advertisements. We develop simulation algorithms for mutually exciting point processes to compute the conversion probability and for out-of-sample prediction. Model comparison results show the proposed model outperforms the benchmark models that ignore exciting effects among advertisement clicks. Using a proprietary data set, we find that display advertisements have relatively low direct effect on purchase conversion, but they are more likely to stimulate subsequent visits through other advertisement formats. We show that the commonly used measure of conversion rate is biased in favor of search advertisements and underestimates the conversion effect of display advertisements the most. Our model also furnishes a useful tool to predict future purchases and advertisement clicks for the purpose of targeted marketing and customer relationship management. This paper was accepted by Eric Bradlow, special issue on business analytics .

What the firm should say in an advertising message, the choice of content , is a critical managerial decision. Here, we focus on a particular aspect of the advertising content choice: an attribute-focused appeal versus an appeal with no direct information on product attributes. We make two assumptions that capture the reality of the advertising context. First, we assume that the bandwidth of advertising is limited: a firm can only communicate about a limited number of attributes. Second, we assume that consumers are active: they can choose to engage in a costly search to obtain additional product-related information. In this setting, we show that there exists an equilibrium where the high-quality firm chooses to produce messages devoid of any attribute information in order to invite the consumer to engage in search, which is likely to uncover positive information about the product. Whereas most of the previous literature has focused on the decision to advertise as a signal of quality, we show that message content, coupled with consumer search, can also serve as a credible signal of quality. In an extension, we show that our results are robust to endogenizing the firm's decision on the amount of advertising spending.

This paper develops a structural model of newspaper markets to analyze the effects of ownership consolidation, taking into account not only firms' price adjustments but also the adjustments in newspaper characteristics. A new dataset on newspaper prices and characteristics is used to estimate the model. The paper then simulates the effect of a merger in the Minneapolis newspaper market and studies how welfare effects of mergers vary with market characteristics. It finds that ignoring adjustments of product characteristics causes substantial differences in estimated effects of mergers.

I examine how the increasing ability of firms to target their ads influences market outcomes when consumers have access to advertising-avoidance tools. Although firms generally benefit from improved targeting, consumers need not. I also show that there may be too little blocking of ads in equilibrium and consider the role of targeted advertising when niche firms compete against mass-market firms.

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.

Advertising's influence on firm sales and firm value has drawn early attention from economists and accountants and more recent attention from marketers. Most studies that have investigated a link between advertising and sales have found such a link. However, studies that have investigated a link between advertising and firm value have only sometimes found that link. Meta-analysis has failed to determine moderators that govern the link between advertising and firm value. In this article, the authors hypothesize that advertising influences firm value for a differentiator because advertising can elaborate the firm's point of difference into brand equity, thereby building firm value. Advertising cannot build brand equity for a cost leader because such a firm has no point of difference on which to build. Identifying differentiators and cost leaders on the basis of firms' reactions to a change in accounting regulations, the authors confirm hypotheses: advertising is related to sales for all firms, but it is more strongly related to firm value for differentiators than for cost leaders. Beyond explaining differences in advertising effectiveness, this study's indicator of differentiation versus cost leadership should enhance future analyses of marketing's effect on firm-level outcomes using archival financial data.

This paper examines how firms should allocate their advertising budgets between consumers who have a high preference for their products (i.e., strong segment) and those who prefer competing products (i.e., weak segment). Targeted advertising transmits relevant information to otherwise uninformed consumers and it is used as a price discrimination device. With targeted advertising and price discrimination, we find that, when the attractiveness of the weak segment is low, each firm advertises more intensively in its strong segment. The same result arises when the attractiveness of the weak segment is high and advertising is sufficiently expensive. Interestingly, when the attractiveness of the weak segment is high but advertising costs are sufficiently low, it is optimal for each firm to advertise more intensively in its weak segment. The paper also investigates how advertising strategies and equilibrium profits are affected by price discrimination. Compared with uniform pricing, firms can increase or reduce the intensity of advertising targeted to each segment when price discrimination is allowed. Furthermore, when the attractiveness of the weak market is high, price discrimination boosts firms’ profits provided that advertising costs are sufficiently low. The reverse happens when advertising costs are high.