Explore the vast range of titles available.

In 2013, the global mobile app market was estimated at over US$50 billion and is expected to grow to $150 billion in the next two years. In this paper, we build a structural econometric model to quantify the vibrant platform competition between mobile (smartphone and tablet) apps on the Apple iOS and Google Android platforms and estimate consumer preferences toward different mobile app characteristics. We find that app demand increases with the in-app purchase option wherein a user can complete transactions within the app. On the contrary, app demand decreases with the in-app advertisement option where consumers are shown ads while they are engaging with the app. The direct effects on app revenue from the inclusion of an in-app purchase option and an in-app advertisement option are equivalent to offering a 28% price discount and increasing the price by 8%, respectively. We also find that a price discount strategy results in a greater increase of app demand in Google Play compared with Apple App Store, and app developers can maximize their revenue by providing a 50% discount on their paid apps. Using the estimated demand function, we find that mobile apps have enhanced consumer surplus by approximately $33.6 billion annually in the United States, and we discuss various implications for mobile marketing analytics, app pricing, and app design strategies. This paper was accepted by Alok Gupta, special issue on business analytics .

Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables representing the valence and volume of reviews. In this paper, we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted, and hence the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews. To demonstrate this, we use text mining to incorporate review text in a consumer choice model by decomposing textual reviews into segments describing different product features. We estimate our model based on a unique data set from Amazon containing sales data and consumer review data for two different groups of products (digital cameras and camcorders) over a 15-month period. We alleviate the problems of data sparsity and of omitted variables by providing two experimental techniques: clustering rare textual opinions based on pointwise mutual information and using externally imposed review semantics. This paper demonstrates how textual data can be used to learn consumers' relative preferences for different product features and also how text can be used for predictive modeling of future changes in sales. This paper was accepted by Ramayya Krishnan, information systems.

Crowd-funded markets have recently emerged as a novel source of capital for entrepreneurs. As the economic potential of these markets is now being realized, they are beginning to go mainstream, a trend reflected by the explicit attention crowdfunding has received in the American Jobs Act as a potential avenue for economic growth, as well as the recent focus that regulators such as the U.S. Securities and Exchange Commission have placed upon it. Although the formulation of regulation and policy surrounding crowd-funded markets is becoming increasingly important, the behavior of crowdfunders, an important aspect that must be considered in this formulation effort, is not yet well understood. A key factor that can influence the behavior of crowd funders is information on prior contribution behavior, including the amount and timing of others' contributions, which is published for general consumption. With that in mind, in this study, we empirically examine social influence in a crowd-funded marketplace for online journalism projects, employing a unique data set that incorporates contribution events and Web traffic statistics for approximately 100 story pitches. This data set allows us to examine both the antecedents and consequences of the contribution process. First, noting that digital journalism is a form of public good, we evaluate the applicability of two competing classes of economic models that explain private contribution toward public goods in the presence of social information: substitution models and reinforcement models. We also propose a new measure that captures both the amount and the timing of others' contribution behavior: contribution frequency (dollars per unit time). We find evidence in support of a substitution model, which suggests a partial crowding-out effect, where contributors may experience a decrease in their marginal utility from making a contribution as it becomes less important to the recipient. Further, we find that the duration of funding and, more importantly, the degree of exposure that a pitch receives over the course of the funding process, are positively associated with readership upon the story's publication. This appears to validate the widely held belief that a key benefit of the crowdfunding model is the potential it offers for awareness and attention-building around causes and ventures. This last aspect is a major contribution of the study, as it demonstrates a clear linkage between marketing effort and the success of crowd-funded projects.

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.

Rapid improvements in the precision of mobile technologies now make it possible for advertisers to go beyond real-time static location and contextual information on consumers. In this paper we propose a novel “trajectory-based” targeting strategy for mobile recommendation that leverages detailed information on consumers’ physical-movement trajectories using fine-grained behavioral information from different mobility dimensions. To analyze the effectiveness of this new strategy, we designed a large-scale randomized field experiment in a large shopping mall that involved 83,370 unique user responses for a 14-day period in June 2014. We found that trajectory-based mobile targeting can, as compared with other baselines, lead to higher redemption probability, faster redemption behavior, and higher transaction amounts. It can also facilitate higher revenues for the focal store as well as the overall shopping mall. Moreover, the effect of trajectory-based targeting comes not only from improvements in the efficiency of customers’ current shopping processes but also from its ability to nudge customers toward changing their future shopping patterns and, thereby, generate additional revenues. Finally, we found significant heterogeneity in the impact of trajectory-based targeting. It is especially effective in influencing high-income consumers. Interestingly, however, it becomes less effective in boosting the revenues of the shopping mall during the weekends and for those shoppers who like to explore across products categories. Our overall findings suggest that highly targeted mobile promotions can have the inadvertent impact of reducing impulse-purchasing behavior by customers who are in an exploratory shopping stage. On a broader note, our work can be viewed as a first step toward the study of large-scale, fine-grained digital traces of individual physical behavior and how they can be used to predict—and market according to—individuals’ anticipated future behavior. This paper was accepted by Anandhi Bharadwaj, information systems.

We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone-based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel data set that consists of individual-level mobile Internet usage data that encompass individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed, to construct their social network and to compute their geographical mobility. We build an individual-level simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.

We explore how Internet browsing behavior varies between mobile phones and personal computers. Smaller screen sizes on mobile phones increase the cost to the user of browsing for information. In addition, a wider range of offline locations for mobile Internet usage suggests that local activities are particularly important. Using data on user behavior at a (Twitter-like) microblogging service, we exploit exogenous variation in the ranking mechanism of posts to identify the ranking effects. We show that (1) ranking effects are higher on mobile phones suggesting higher search costs: links that appear at the top of the screen are especially likely to be clicked on mobile phones and (2) the benefit of browsing for geographically close matches is higher on mobile phones: stores located in close proximity to a user's home are much more likely to be clicked on mobile phones. Thus, the mobile Internet is somewhat less “Internet-like”: search costs are higher and distance matters more. We speculate on how these changes may affect the future direction of Internet commerce.

In this study, we capture the trade-off between effective and annoying display advertising. We investigate both the enduring impact of display advertising on consumers' purchase decisions and the potential of persistent display advertising to stimulate annoyance in consumers. Additionally, we study the structural dynamics of these advertising effects by allowing them to be contingent on the latent state of the purchase funnel in which each consumer resides. The findings, based on the hidden Markov model that we propose, demonstrate that a tension exists between generating interest and triggering annoyance in consumers; whereas display advertising has an enduring impact on transitioning consumers farther down the purchase funnel, persistent display advertising exposures beyond a frequency threshold can have an adverse effect by increasing the chances that consumers will be annoyed. Investigating the dynamics of these annoyance effects, we reveal that consumers who reside in different stages of the purchase funnel exhibit considerably different tolerance for annoyance stimulation. Our findings also reveal that the format of display advertisements and the level of diversification of ad creatives as well as consumer demographics moderate consumers' thresholds for annoyance elicitation. For instance, advertisers can reduce annoyance elicitation as a result of frequent display advertising exposures when they diversify the display ad creatives shown to consumers as well as when they use static rather than animated display ads. Digital advertisers often harness technology-enabled advertising-scheduling strategies, such as ad repetition at the individual consumer level, in order to improve advertising effectiveness. However, such strategies might elicit annoyance in consumers as indicated by anecdotal evidence, such as the popularity of ad-blocking technologies. Our study captures this trade-off between effective and annoying display advertising. We propose a hidden Markov model that allows us to investigate both the enduring impact of display advertising on consumers’ purchase decisions and the potential of persistent display advertising to stimulate annoyance in consumers. Additionally, we study the structural dynamics of these advertising effects by allowing them to be contingent on the latent state of the funnel path in which each consumer resides. Our findings demonstrate that a tension exists between generating interest and triggering annoyance in consumers; whereas display advertising has an enduring impact on transitioning consumers further down the purchase funnel, persistent display advertising exposures beyond a frequency threshold can have an adverse effect by increasing the chances that consumers will be annoyed. Investigating the dynamics of these annoyance effects, we reveal that consumers who reside in different stages of the purchase funnel exhibit considerably different tolerance for annoyance stimulation. Our findings also reveal that the format of display advertisements and the level of diversification of ad creatives as well as consumer demographics moderate consumers’ thresholds for annoyance elicitation. For instance, advertisers can reduce annoyance elicitation as a result of frequent display advertising exposures when they use static rather than animated display ads as well as when they diversify the display ad creatives shown to consumers. Our paper contributes to the literature on digital advertising and consumer annoyance and has significant managerial implications for the online advertising ecosystem.

The phenomenon of paid search advertising has now become the most predominant form of online advertising in the marketing world. However, we have little understanding of the impact of search engine advertising on consumers' responses in the presence of organic listings of the same firms. In this paper, we model and estimate the interrelationship between organic search listings and paid search advertisements. We use a unique panel data set based on aggregate consumer response to several hundred keywords over a three-month period collected from a major nationwide retailer store chain that advertises on Google. In particular, we focus on understanding whether the presence of organic listings on a search engine is associated with a positive, a negative, or no effect on the click-through rates of paid search advertisements, and vice versa for a given firm. We first build an integrated model to estimate the relationship between different metrics such as search volume, click-through rates, conversion rates, cost per click, and keyword ranks. A hierarchical Bayesian modeling framework is used and the model is estimated using Markov chain Monte Carlo methods. Our empirical findings suggest that click-throughs on organic listings have a positive interdependence with click-throughs on paid listings, and vice versa. We also find that this positive interdependence is asymmetric such that the impact of organic clicks on increases in utility from paid clicks is 3.5 times stronger than the impact of paid clicks on increases in utility from organic clicks. Using counterfactual experiments, we show that on an average this positive interdependence leads to an increase in expected profits for the firm ranging from 4.2% to 6.15% when compared to profits in the absence of this interdependence. To further validate our empirical results, we also conduct and present the results from a controlled field experiment. This experiment shows that total click-through rates, conversions rates, and revenues in the presence of both paid and organic search listings are significantly higher than those in the absence of paid search advertisements. The results predicted by the econometric model are also corroborated in this field experiment, which suggests a causal interpretation to the positive interdependence between paid and organic search listings. Given the increased spending on search engine-based advertising, our analysis provides critical insights to managers in both traditional and Internet firms.

Omnichannel marketing is often viewed as the panacea for one-to-one marketing, but this strategic path is mired with obstacles. This article investigates three challenges in realizing the full potential of omnichannel marketing: (1) data access and integration, (2) marketing attribution, and (3) consumer privacy protection. While these challenges predate omnichannel marketing, they are exacerbated in a digital omnichannel environment. This article argues that advances in machine learning and blockchain offer some promising solutions. In turn, these technologies present new challenges and opportunities for firms, which warrant further academic research. The authors identify both recent developments in practice and promising avenues for future research.