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Interpersonal communications have long been recognized as an influential source of information for consumers. Internet-based media have facilitated information exchange among firms and consumers, as well as observability and measurement of such exchanges. However, much of the research addressing online communication focuses on ratings collected from online forums. In this paper, we look beyond ratings to a more comprehensive view of online communications. We consider the sales effect of the volume of positive, negative, and neutral online communications captured by Web crawler technology and classified by automated sentiment analysis. Our modeling approach captures two key features of our data, dynamics and endogeneity. In terms of dynamics, we model daily measures of online communications about a firm and its products as contributing to a latent demand-generating stock variable. To account for the endogeneity, we extend the latent instrumental variable technique to account for dynamic endogenous regressors. Our results demonstrate a significant effect of positive, negative, and neutral online communications on daily sales performance. Failure to account for endogeneity results in a severe attenuation of the estimated effects. From a managerial perspective, we demonstrate the importance of accounting for communication valence as well as the impact of shocks to positive, negative, and neutral online communications.

The authors present a modeling approach to assess the purchase conversion performance of individual keywords in paid search advertising. The model facilitates estimation of daily keyword conversion and click-through rates in a sparse data environment while accounting for the endogenous position of the text advertisement served in response to a search. Position endogeneity in paid search data can arise from both omitted variables and measurement error. The authors propose a latent instrumental variable approach to address this problem. They estimate their model on keyword-level paid search data containing daily information on impressions, clicks, and reservations for a major lodging chain. They find that higher positions increase both the click-through and conversion rates. When advertisements are served in higher positions, approximately one-third of new conversions is due to increased clickthrough while approximately two-thirds are due to increased conversion rates. The authors show that the keyword list generated on the basis of their estimated conversion rates outperforms the status quo list as well as lists generated by observed conversion and click-through rates.

We develop a two-stage consumer-level model of paid search advertising response based on standard aggregated data provided to advertisers by major search engines such as Google or Bing. The proposed model uses behavioral primitives in accord with utility maximization and allows recovering parameters of the heterogeneity distribution in consumer preferences. The model is estimated on a novel paid search data set that includes information on the ad copy. To that end, we develop an original framework to analyze composition and design attributes of paid search ads. Our results allow us to correctly evaluate the effects of specific ad properties on ad performance, taking consumer heterogeneity into account. Another benefit of our approach is allowing recovery of preference correlation across the click-through and conversion stage. Based on the estimated correlation between price- and position-sensitivity, we propose a novel contextual targeting scheme in which a coupon is offered to a consumer depending on the position in which the paid search ad was displayed. Our analysis shows that total revenues from conversion can be increased using this targeting scheme while keeping cost constant.

This study investigates the effects of consumer activity in online media (paid, owned, and earned) on sales and their interdependencies with the traditional marketing mix elements of price, advertising and distribution. We develop an integrative conceptual framework that links marketing actions to online consumer activity metrics along the consumer’s path to purchase (P2P). Our framework proposes that the path to purchase has three basic stages–learning (cognitive), feeling (affective), behavior (conative)—and that these can be measured with novel online consumer activity metrics such as clicking on a paid search ads (cognitive) or Facebook likes and unlikes of the brand (affective). Our empirical analysis of a fast moving consumer good supports a know–feel–do pathway for the low–involvement product studied. We find, for example, that earned media can drive sales. However, we find that the news is not all good as it relates to online consumer activity: higher consumer activity on earned and owned media can lead to consumer disengagement in the form of unlikes. While traditional marketing such as distribution (60%) and price (20%) are the main drivers of sales variation for the studied brand, online owned (10%), (un)earned (3%), and paid (2%) media explain a substantial part of the path to purchase. It is noteworthy that TV advertising (5%) explains significantly less than online media in our case. Overall, our study should help strengthen marketers’ case for building share in consumers’ hearts and minds, as measured through consumer online activity and engagement.

Endogeneity in empirical marketing research is an increasingly discussed topic in academic research. Mentions of endogeneity and related procedures to correct for it have risen 5x across the field’s top journals in the past 20 years, but represent an overall small portion of extant research. Yet there is often substantial difficulty in reconciling issues of endogeneity with many of the substantive questions of interest to marketing strategy for both theoretical and/or practical reasons. This paper provides an overview of main causes of endogeneity, approaches to addressing it, and guidance to marketing strategy researchers to balance these issues as the field continues to move towards more methodological sophistication, potentially at the expense of managerial tractability.

In Internet paid search advertising, marketers pay for search engines to serve text advertisements in response to keyword searches that are generic (e.g., \"hotels\") or branded (e.g., \"Hilton Hotels\"). Although stand-alone metrics usually show that generic keywords have higher apparent costs to the advertiser than branded keywords, generic search may create a spillover effect on subsequent branded search. Building on the Nerlove—Arrow advertising framework, the authors propose a dynamic linear model to capture the potential spillover from generic to branded paid search. In the model, generic search advertisements serve to expose users to information about the brand's ability to meet their needs, raising awareness that the brand is relevant to the search. In turn, this can induce additional future search activity for keywords that include the brand name. Using a Bayesian estimation approach, the authors apply the model to data from a paid search campaign for a major lodging chain. The results show that generic search activity positively affects future branded search activity through awareness of relevance. However, branded search does not affect generic search, demonstrating that the spillover is asymmetric. The findings have implications for understanding search behavior on the Internet and the management of paid search advertising.

The authors propose a new approach to evaluate the perceptions and performance of a large set of paid search ads. This approach consists of two parts. First, primary data on hundreds of ads are collected through paired comparisons of their relative ability to generate awareness, interest, desire, action, and click performance. The authors use the Elo algorithm, a statistical model calibrated on paired comparisons, to score the full set of ads on relative perceptions and click performance. The estimated scores validate the theoretical link between perceptions and performance. Second, the authors predict the perceptions and performance of new ads relative to the existing set using textual content metrics. The predictive model allows for direct effects and interactions of the text metrics, resulting in a \"large p, small n\" problem. They address this problem with a novel Bayesian implementation of the VANISH model, a penalized regression approach that allows for differential treatment of main and interaction effects, in a system of equations. The authors demonstrate that this approach ably forecasts relative ad performance by leveraging perceptions inferred from content alone.

Many online shoppers initially acquired through paid search advertising later return to the same website directly. These so-called \"direct type-in\" visits can be an important indirect effect of paid search. Because visitors come to sites via different keywords and can vary in their propensity to make return visits, traffic at the keyword level is likely to be heterogeneous with respect to how much direct type-in visitation is generated. Estimating this indirect effect, especially at the keyword level, is difficult. First, standard paid search data are aggregated across consumers. Second, there are typically far more keywords than available observations. Third, data across keywords may be highly correlated. To address these issues, the authors propose a hierarchical Bayesian elastic net model that allows the textual attributes of keywords to be incorporated. The authors apply the model to a keyword-level data set from a major commercial website in the automotive industry. The results show a significant indirect effect of paid search that clearly differs across keywords. The estimated indirect effect is large enough that it could recover a substantial part of the cost of the paid search advertising. Results from textual attribute analysis suggest that branded and broader search terms are associated with higher levels of subsequent direct type-in visitation.

Marketers increasingly face modeling situations where the number of independent variables is large and possibly approaching or exceeding the number of observations. In this setting, covariate selection and model estimation present significant challenges to usual methods of inference. These challenges are exacerbated when covariate interactions are of interest. Most extant regularization methods make no distinction between main and interaction terms in estimation. The linear VANISH model is an exception to these methods. The linear VANISH model is a regularization method for models with interaction terms that ensures proper model hierarchy by enforcing the heredity principle. We derive the generalized VANISH model for nonlinear responses, including duration, discrete choice, and count models widely used in marketing applications. In addition, we propose a VANISH model that allows to account for unobserved consumer heterogeneity via a mixture approach. In three empirical applications we demonstrate that our proposed model outperforms main effects models as well as other methods that include interaction terms.

This paper investigates how exposure to Internet display advertising affects the subsequent choices users make of brand-specific pages to view within a website. Using individual-level clickstream data from a third-party automotive website, we tracked the web pages selected by users as they browsed the site and their exposures to premium placement display ads for different vehicle makes (e.g., Ford, Toyota). Pages on the site were classified into those that displayed information about a specific vehicle make (a “make page”) versus those that did not (a “non-make page”). For each “make-page” viewed, the specific automotive make selected (e.g., Ford, Toyota) was also recorded. We use these data to develop a model of users’ make-specific page choices as a function of prior banner ad exposure on the site. Consumer heterogeneity is captured using a Bayesian Mixture approach. We find that banner ads influence subsequent choices of which make-specific pages to view for ads, served during the current browsing session but not for ads served in previous sessions. The effect of banner ads is also segmented: users in one segment (54%) reacted positively, users in a second segment (46%) were not influenced. Using a standard continuous approach to heterogeneity, we would have concluded–incorrectly–that banner advertising has no effect on the subsequent selection of make-specific pages. For the positively reacting segment, we estimate that the elasticity of make-page choice with respect to banner ad exposure is just under 0.2. Users in this segment appear less focused in their site browsing behavior and tend to stay longer than users in the non-reacting segment.