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Supporting the Adoption Funnel with Differential Effects from Traditional Advertising, Online Displays, and a Micro-Influencer Campaign
Abstract
Growing a digital platform requires bringing users to the platform and converting them
into adopting the online service or community. Growth may be supported with
advertising, but knowledge about what type of advertising affects what stage in the
adoption funnel is incomplete. This research investigates how television and print
advertising, digital banners, and firm-created word-of-mouth from a micro-influencer
campaign differentially affect aggregate-level searches, platform visits, and platform
sign-ups of a new lifestyle and home decoration online community platform. Results from
the endogeneity-bias-controlled funnel model indicate, that traditional advertising helps
in early stages, digital banner advertisements bring users directly to the platform at
intermediate stages, and firm-created word-of-mouth helps converting users in the last
stages of the adoption funnel. These findings complement existing adoption funnel studies
with fewer or different advertising or funnel variables. For managers, the results imply
that advertising can address bottlenecks in the platform adoption funnel.
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This study examines the relative effectiveness of traditional advertising, impressions generated through firm-to-consumer (F2C) messages on Facebook, and the volume and valence of consumer-to-consumer (C2C) messages on Twitter and forums for brand-building and customer acquisition efforts. To a unique data set from a European telecom firm, the authors apply vector autoregressive modeling. This modelling approach allows considering the interrelations among traditional advertising, F2C impressions, volume and valence of C2C social messages. The results show that traditional advertising is most effective for both brand building and customer acquisition. Impressions generated through F2C social messages complement traditional advertising efforts. Thus, thoroughly orchestrating traditional advertising and a firm's social media activities may improve a firm's performance with respect to building the brand and enabling customer acquisition. Moreover, firms can stimulate the volume and valence of messages among consumers through traditional advertising that in turn affect brand building and acquisition. These findings help managers to leverage the different types of messages more adequately.
Rooted in the Integrated Marketing Communication framework, this paper conceptualizes how brand familiarity affects online and cross-channel synergies. The empirical analysis uses Bayesian Vector Autoregressive models to estimate long-term elasticities for four brands. The authors distinguish customer-initiated communication (typically online) from firm-initiated communication (typically offline). Their results indicate that within-online synergy is higher than online-offline synergy for both familiar brands but not for both unfamiliar brands. Managers of unfamiliar brands may obtain substantial synergy from offline marketing spending, even though its direct elasticity pales in comparison with that of online media while managers of familiar brands can generate more synergy by investing in different online media.
The current marketing environment is characterized by a surge in multichannel shopping and increasing choice of advertising channels. This situation requires firms to understand how advertising in one channel (e.g., online) influences sales in another channel (e.g., offline). This article studies the presence, magnitude, and carryover of these cross-channel effects for online advertising (display and search) and traditional media. The analysis considers how these advertising expenditures translate directly into sales, as well as indirectly through intermediate search advertising metrics-namely, impressions and clickthrough rate. For a high-end clothing and apparel retailer, the authors find that cross effects exist and are important and that cross-effect elasticities are almost as high as own-effect elasticities. Online display and, in particular, search advertising is more effective than traditional advertising. This result is primarily due to strong cross effects on the offline channel. Return-on-investment calculations suggest that by ignoring these cross effects, firms substantially miscalculate the effectiveness of online advertising. Notably, the authors find that traditional advertising decreases paid search click-through rates, thus reducing the net cross effect of traditional advertising.
This study investigates the effects of exposures to earned and owned social media activities and their interaction on brand purchase in a two-stage decision model (i.e., likelihood to purchase and the amount purchased offline). Our study is instantiated on a unique single-source dataset of 12-month home-scanned brand purchase records of a group of fast-moving consumer good brands and Facebook brand Fan Page messages related to the brands. We first find that exposures to earned and owned social media activities for brands have significant and positive impacts on consumers' likelihood to purchase the brands. Their effects are, surprisingly, suppressive on each other. Second, exposures to earned and owned social media activities have almost no impact on the amount purchased offline with presence of in-store promotions. Our study contributes to our knowledge body of social media marketing by demonstrating that social media activities for a brand can foster the consumer base of the brand, but that effort is not necessarily sales-oriented. In addition, our study is conducive to guiding marketers onto the strategic allocation of advertising dollars to online social channels featuring a mixture of earned and owned social media.
The increasing amount of electronic word of mouth (eWOM) has significantly affected the way consumers make purchase decisions. Empirical studies have established an effect of eWOM on sales but disagree on which online platforms, products, and eWOM metrics moderate this effect. The authors conduct a meta-analysis of 1,532 effect sizes across 96 studies covering 40 platforms and 26 product categories. On average, eWOM is positively correlated with sales (.091), but its effectiveness differs across platform, product, and metric factors. For example, the effectiveness of eWOM on social media platforms is stronger when eWOM receivers can assess their own similarity to eWOM senders, whereas these homophily details do not influence the effectiveness of eWOM for e-commerce platforms. In addition, whereas eWOM has a stronger effect on sales for tangible goods new to the market, the product life cycle does not moderate the eWOM effectiveness for services. With respect to the eWOM metrics, eWOM volume has a stronger impact on sales than eWOM valence. In addition, negative eWOM does not always jeopardize sales, but high variability does.
The authors conduct a meta-analysis on the effect of electronic word of mouth on sales by examining 51 studies (involving 339 volume and 271 valence elasticities) and primary data collected on product characteristics (durability, trialability, and usage condition), industry characteristics (industry growth and competition), and platform characteristics (expertise and trustworthiness). Their analysis reveals that electronic word-of-mouth volume (valence) elasticity is .236 (.417). More importantly, the findings show that volume and valence elasticities are higher for privately consumed, low-trialability products that operate in less competitive industries and whose reviews are carried on independent review sites. Furthermore, volume elasticities are higher for durable goods and for reviews on specialized review sites, whereas valence elasticities are greater for community-based sites.
Drawing on the results, they discuss several implications for managers and researchers and explain why valence elasticities are often found to be insignificant. Finally, they propose numerous directions for future research in the area on the basis of their findings.
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.
We propose a new hierarchical model of online and offline advertising. This model incorporates within-media synergies and cross-media synergies and allows higher-order interactions among various media. We derive the optimal spending on each medium and the optimal total budget. We also develop three hypotheses on the effects of within- and across-media synergies on both the total budget and its allocation. We estimate media effectiveness as well as the within- and cross-media synergies of offline (television, print, and radio) and online (banners and search) ads using market data for a car brand. We show that both types of synergies —within-media (i.e., intra-offline) and cross-media (online-offline)— exist. We show how within- and cross-media synergies boost the total media budget and online spending due to synergies of the online media with various offline media.
The authors review more than 250 journal articles and books to establish what is and should be known about how advertising affects the consumer-how it works. They first deduce a taxonomy of models, discuss the theoretical principles of each class of models, and summarize their empirical findings. They then synthesize five generalizations about how advertising works and propose directions for further research. Advertising effects are classified into intermediate effects, for example, on consumer beliefs and attitudes, and behavioral effects, which relate to purchasing behavior, for example, on brand choice. The generalizations suggest that there is little support for any hierarchy, in the sense of temporal sequence, of effects. The authors propose that advertising effects should be studied in a space, with affect, cognition, and experience as the three dimensions. Advertising's positioning in this space should be determined by context, which reflects advertising's goal diversity, product category, competition, other aspects of mix, stage of product life cycle, and target market.
Many advertisers adopt the integrated marketing communications per-spective that emphasizes the importance of synergy in planning multi-media activities. However, the role of synergy in multimedia communica-tions is not well understood. Thus, the authors investigate the theoretical and empirical effects of synergy by extending a commonly used dynamic advertising model to multimedia environments. They illustrate how adver-tisers can estimate and infer the effectiveness of and synergy among multi-media communications by applying Kalman filtering methodology. Using market data on Dockers brand advertising, the authors first calibrate the extended model to establish the presence of synergy between television and print advertisements in consumer markets. Second, they derive the-oretical propositions to understand the impact of synergy on media budget, media mix, and advertising carryover. One of the propositions reveals that as synergy increases, advertisers should not only increase the media budget but also allocate more funds to the less effective activ-ity. The authors also discuss the implications for advertising overspend-ing. Finally, the authors generalize the model to include multiple media, differential carryover, and asymmetrical synergy, and they identify topics for further research.
Endogeneity arises for numerous reasons in models of consumer choice. It leads to inconsistency with standard estimation methods that maintain independence between the model's error and the included variables. The authors describe a control function approach for handling endogeneity in choice models. Observed variables and economic theory are used to derive controls for the dependence between the endogenous variable and the demand error. The theory points to the relationships that contain information on the unobserved demand factor, such as the pricing equation and the advertising equation. The authors' approach is an alternative to Berry, Levinsohn, and Pakes's (1995) product-market controls for unobserved quality. The authors apply both methods to examine households' choices among television options, including basic and premium cable packages, in which unobserved attributes, such as quality of programming, are expected to be correlated with price. Without correcting for endogeneity, aggregate demand is estimated to be upward-sloping, suggesting that omitted attributes are positively correlated with demand. Both the control function method and the product-market controls method produce downward-sloping demand estimates that are similar.
Sponsored search accounts for 40% of the total online advertising market. These ads appear as ordered lists along with the regular search results in search engine results pages. The conventional wisdom in the industry is that the top position is the most desirable position for advertisers. This has led to intense competition among advertisers to secure the top positions in the results pages.We evaluate the impact of ad placement on revenues and profits generated from sponsored search using data from for several hundred keywords from the ad campaign of an online retailer. Using a hierarchical Bayesian model, we measure the impact of ad placement on both click-through rate and conversion rate for these keywords. We find that while click through rate decreases with position, conversion rate first increases and then decreases with position. The net effect is that, contrary to conventional wisdom, the topmost position in sponsored search advertisements is not necessarily the revenue- or profit-maximizing position. Using a theoretical model we show that one potential driver of these results is the heterogeneity in search costs across consumers and the additional browsing cost incurred in evaluating products across multiple websites.Our results inform the advertising strategies of firms participating in sponsored search auctions and provide insight into consumer behavior in these environments. Specifically, they help correct a significant misunderstanding among advertisers regarding the value of the top position. Further, they reveal potential inefficiencies in present auction mechanisms used by the search engines.
With the growth and evolution of the Internet, electronic peer-to-peer referrals have become an important phenomenon, and marketers have tried to exploit their potential through viral marketing campaigns. At the same time, spam and e-mail-based viruses have cluttered electronic communications, making viral marketing campaigns problematic and challenging to deploy. The key driver in viral marketing is the effectiveness of unsolicited, electronic referrals to create awareness, trigger interest, and generate sales or product adoption. Yet, despite a large literature concerning interpersonal influence, little is known about how this electronic, or, indeed, any word-of-mouth process influences consumers' actual behaviors, particularly in a cluttered online environment. In this paper, we develop a model to help identify the role word-of-mouth plays during each stage of a viral marketing recipients' decision-making process, including the conditions that moderate such influence. We then present an innovative methodology for collecting data unobtrusively and in real time. We empirically test the model and methodology via a field study, where we observed the reactions of 1100 individuals after they received an unsolicited e-mail from one of their acquaintances, inviting them to take a survey and in turn spread the word about it. We found that characteristics of the social tie influenced recipients' behaviors, but had different effects at different stages: tie strength facilitated awareness, perceptual affinity triggered recipients' interest, and demographic similarity had a negative influence on each stage of the decision-making process. We conclude with a discussion of the theoretical and methodological contributions of our work and of managerial implications of these findings for online marketers interested in strategies for leveraging peer-to-peer referral networks.
Two-sided exchange networks (such as eBay.com) often advertise their number of users, presumably to encourage further participation. However, these networks differ markedly on how they advertise their user base. Some highlight the number of sellers, some emphasize the number of buyers, and others disclose both. We use field experiment data from a business-to-business website to examine the efficacy of these different display formats. Before each potential seller posted a listing, the website randomized whether to display the number of buyers and/or sellers, and if so, how many buyers and/or sellers to claim. We find that when information about both buyers and sellers is displayed, a large number of sellers deters further seller listings. However, this deterrence effect disappears when only the number of sellers is presented. Similarly, a large number of buyers is more likely to attract new listings when it is displayed together with the number of sellers. These results suggest the presence of indirect network externalities, whereby a seller prefers markets with many other sellers because they help attract more buyers.
The success of new ventures can hinge on obtaining venture capital (VC) funding. Virtually every successful IT venture has depended on VC funding early in its history. However, obtaining venture capital is difficult. Unlike earlier studies on VC funding that consider new ventures to be homogeneous, this study seeks to identify factors that VCs consider when they make funding decisions for IT ventures. Building on prior research in the area of agency and business risk, we develop a theoretical model that draws on work in finance and entrepreneurship. The model suggests that VCs consider two types of risk: business risk and agency risk. The relative importance of these two types of risk may be different across industries. We test this model using data from 139 business plans for IT startups that were considered for funding by VCs. Traditional structural equation modeling (SEM) does not accommodate non-normal data or dichotomous outcome variables. Using the Robust Weighted Least Squares approach, we test our model with non-normal data and dichotomous outcomes. In addition, we use Tetrad analysis to check model fit against alternative models, floor and ceiling analysis to test sample frame validity, relative effect size comparison to test relative elasticity of effects, and a Monte Carlo estimation approach to test overall model power and power of individual paths. We find that business risk is an important factor in startup funding for IT ventures. We do not find agency risk to be an important consideration in start-up funding for IT ventures.
While Chap. 3’s univariate models capture dynamic patterns of a single variable, and multivariate models allow us to analyze the dynamic effect of marketing actions on performance, they leave several interesting phenomena unexplored. These include:1.dual causality among marketing and performance, which could be tied in a long-term equilibrium; 2.explaining competitive marketing activity and predicing competitive reaction, and 3.uncovering decision rules of key market players, such as retailers (Dekimpe and Hanssens 1999).
In recent years, word-of-mouth (WOM) marketing has been the subject of considerable interest among managers and academics alike. However, there is very little common knowledge on what drives the value of WOM programs and how they should be designed to optimize value. Firms therefore frequently rely on relatively simple metrics to measure the success of their WOM marketing efforts and mainly use rules of thumb when making crucial program design decisions. This article proposes a new method to measure WOM program value that is based on the impact of WOM on the firm’s customer equity. It then provides recommendations for the five main questions managers face when planning a WOM program: Who to target? When to launch the program? Where to launch it? Which incentives to offer? and How many participants to include?
Seeded marketing campaigns (SMCs) involve firms sending products to selected customers and encouraging them to spread word of mouth (WOM). Prior research has examined certain aspects of this increasingly popular form of marketing communication, such as seeding strategies and their efficacy. Building on prior research, this study investigates the effects of SMCs that extend beyond the generation of WOM for a campaign’s focal product by considering how seeding can affect WOM spillover effects at the brand and category levels. The authors introduce a framework of SMC-related spillover effects, and empirically estimate these with a unique data set covering 390 SMCs for products from 192 different cosmetics brands. Multiple spillover effects are found, suggesting that while SMCs can be used primarily to stimulate WOM for a focal product, marketers must also account for brand- and category-level WOM spillover effects. Specifically, seeding increases conversations about that product among nonseed consumers, and, interestingly, decreases WOM about other products from the same brand and about competitors’ products in the same category as the focal product. These findings indicate that marketers can use SMCs to focus online WOM on a particular product by drawing consumers away from talking about other related, but off-topic, products.
Over the past 15 years, digital media platforms have revolutionized marketing, offering new ways to reach, inform, engage, sell to, learn about, and provide service to customers. As a means of taking stock of academic work’s ability to contribute to this revolution, this article tracks the changes in scholarly researchers’ perspectives on three major digital, social media, and mobile (DSMM) marketing themes from 2000 to 2015. The authors first use keyword counts from the premier general marketing journals to gain a macro-level view of the shifting importance of various DSMM topics since 2000. They then identify key themes emerging in five-year time frames during this period: (1) DSMM as a facilitator of individual expression, (2) DSMM as decision support tool, and (3) DSMM as a market intelligence source. In both academic research to date and corresponding practitioner discussion, there is much to appreciate. However, there are also several shortcomings of extant research that have limited its rel...
A field test similar to Godes and Mayzlin's (2009) conceptually replicates the sales effect of word-of-mouth campaigns and empirically confirms Haenlein and Libai's (2013) findings that seeds with high value to the brand are preferred among noncustomers of the product because they show the largest effect on incremental sales. Seeds with low value to the brand, as in Godes and Mayzlin's study, may be preferred if marketers are limited to work with product customers only. Additionally, those peers that are unaware of the campaign product, but have bought the brand in the past are mainly responsible for incremental sales.
As firms increasingly rely on online media to acquire consumers, marketing managers rely on online metrics such as click-through rate (CTR) and cost per acquisition (CPA). However, these standard online advertising metrics are plagued with attribution problems and do not account for synergy or dynamics. These issues can easily lead firms to overspend on some actions and thus waste money and/or underspend in others, leaving money on the table.
We develop a multivariate time series model to investigate the dynamic interaction between paid search and display ads and calibrate the model using data from a bank that uses online ads to acquire new checking account customers. The model suggests that both search and display ads exhibit dynamics that improve their effectiveness and ROI over time. Moreover, our results suggest that display ads increase search conversion. However, display ads may also increase search clicks, thereby increasing search advertising costs. After accounting for these three effects, we estimate that each $1 invested in display and search leads to a return of $1.24 for display and $1.75 for search ads. These ROI estimates are respectively 10% and 38% higher than those obtained by standard metrics, which may have led the company to under-invest. We use these results to show how optimal budget allocation may shift after accounting for attribution and dynamics. Although display benefits from synergy attribution, the strong dynamic effects of search call for an increase in search advertising budget share by up to 36% in our context.
Viral products and ideas are intuitively understood to grow through a person-to-person diffusion process analogous to the spread of an infectious disease; however, until recently it has been prohibitively difficult to directly observe purportedly viral events, and thus to rigorously quantify or characterize their structural properties. Here we propose a formal measure of what we label "structural virality" that interpolates between two conceptual extremes: content that gains its popularity through a single, large broadcast and that which grows through multiple generations with any one individual directly responsible for only a fraction of the total adoption. We use this notion of structural virality to analyze a unique data set of a billion diffusion events on Twitter, including the propagation of news stories, videos, images, and petitions. We find that across all domains and all sizes of events, online diffusion is characterized by surprising structural diversity; that is, popular events regularly grow via both broadcast and viral mechanisms, as well as essentially all conceivable combinations of the two. Nevertheless, we find that structural virality is typically low, and remains so independent of size, suggesting that popularity is largely driven by the size of the largest broadcast. Finally, we attempt to replicate these findings with a model of contagion characterized by a low infection rate spreading on a scale-free network. We find that although several of our empirical findings are consistent with such a model, it fails to replicate the observed diversity of structural virality, thereby suggesting new directions for future modeling efforts.
This study examines the effects of Internet display advertising using cookie-level data from a field experiment at a financial tools provider. The experiment randomized assignment of cookies to treatment (firm ads) and control conditions (charity ads), enabling the authors to handle different sources of selection bias, including targeting algorithms and browsing behavior. They analyze display ad effects for users at different stages of the company's purchase funnel (i.e., nonvisitor, visitor, authenticated user, and converted customer) and find that display advertising positively affects visitation to the firm's website for users in most stages of the purchase funnel, but not for those who previously visited the site without creating an account. Using a binary logit model, the authors calculate marginal effects and elasticities by funnel stage and analyze the potential value of reallocating display ad impressions across users at different stages. Expected visits increase almost 10% when display ad impressions are partially reallocated from nonvisitors and visitors to authenticated users. The authors also show that results from the controlled experiment data differ significantly from those computed using standard correlational approaches.
In this study, the authors develop an inexpensive method to help firms assess the relative effectiveness of multiple advertising media. Specifically, they use a firm's loyalty program database to capture media exposure, through an online media survey, for all the media in which the firm advertises. In turn, the exposure data are matched with the purchase history for these same respondents, thereby creating single-source data. The authors illustrate their method for a large retailer that undertook a short-term promotional sale by advertising in television, radio, newspaper, magazine, online display ad, sponsored search, social media, catalog, direct mail, and e-mail channels. In this case, seven of the ten media significantly influence purchase outcomes. Finally, the authors demonstrate how to use their advertising response model to determine the optimal budget allocation across each advertising media channel.
Marketing is a field that is rich in data. Our data is of high quality, often at a highly disaggregate level, and there is considerable variation in the key variables for which estimates of effects on outcomes such as sales and profits are desired. The recognition that, in some general sense, marketing variables are set by firms on the basis of information not always observable by the researcher has led to concerns regarding endogeneity and widespread pressure to implement instrumental variables methods in marketing problems. The instruments used in our empirical literature are rarely valid and the IV methods used can have poor sampling properties, including substantial finite sample bias and large sampling errors. Given the problems with IV methods, a convincing argument must be made that there is a first order endogeneity problem and that we have strong and valid instruments before these methods should be used. If strong and valid instruments are not available, then researchers need to look toward supplementing the information available to them. For example, if there are concerns about unobservable advertising or promotional variables, then the researcher is much better off measuring these variables rather than using instruments (such as lagged marketing variables) that are clearly invalid. Ultimately, only randomized variation in marketing variables (with proper implementation and large samples) can be argued to be a valid instrument without further assumptions.
In this article we argue that (1) legitimacy is an important resource for gaining other resources, (2) such resources are crucial for new venture growth, and (3) legitimacy can be enhanced by the strategic actions of new ventures. We review the impact of legitimacy on new ventures as well as sources of legitimacy for new ventures, present strategies for new ventures to acquire legitimacy, explore the process of building legitimacy in the new venture, and examine the concept of the legitimacy threshold.
Inofec, a small- to medium-sized enterprise in the business-to-business sector, desired a more analytic approach to allocate marketing resources across communication activities and channels. We developed a conceptual framework and econometric model to empirically investigate (1) the marketing communication effects on off-line and online purchase funnel metrics and (2) the magnitude and timing of the profit impact of firm-initiated and customer-initiated contacts. We find evidence of many cross-channel effects, in particular, off-line marketing effects on online funnel metrics and online funnel metrics on off-line purchases. Moreover, marketing communication activities directly affect both early and later purchase funnel stages (website visits, online and off-line information, and quote requests). Finally, we find that online customer-initiated contacts have substantially higher profit impact than off-line firm-initiated contacts. Shifting marketing budgets toward these activities in a field experiment yielded net profit increases 14 times larger than those for the status quo allocation.
Despite a 20-year trend toward integrated marketing communications, advertisers seldom coordinate television and search advertising campaigns. We find that television advertising for financial services brands increases both the number of related Google searches and searchers’ tendency to use branded keywords in place of generic keywords. The elasticity of a brand’s total searches with respect to its TV advertising is .17, an effect that peaks in the morning. These results suggest that practitioners should account for cross-media effects when planning, executing, and evaluating both television and search advertising campaigns.
Consumers are exposed to advertisers across a number of channels. As a result, a conversion or a sale may be the result of a series of ads that were displayed to the consumer. This raises the key question of attribution: which ads get credit for a conversion and how much credit do each of these ads get? This is one of the most important issues facing the advertising industry. Although the issue is well documented, current solutions are often simplistic. Current practices apply simplistic methods like attributing the sale to the most recent ad exposure that penalize prior exposures and give undue credit to ad exposures further down in the conversion funnel. In this paper, we address the problem of attribution using a unique data-set from the online campaign of a car launch. We present a Hidden Markov Model of an individual consumer's behavior based on the concept of a conversion funnel that captures the consumer's deliberation process. We observe that different ad formats, e.g. display and search ads, affect the consumers differently and in different states of their decision process. Display ads usually have an early impact on the consumer, moving him from a state of dormancy to a state where he is aware of the product and it might enter his consideration set. However, when the consumer actively interacts with these ads (e.g. by clicking on them), his likelihood to convert considerably increases. Secondly, we present an attribution scheme based on the proposed model that assigns credit to an ad based on the incremental impact it has the consumer's probability to convert.
We propose a new statistical instrument-free method to tackle the endogeneity problem. The proposed method models the joint distribution of the endogenous regressor and the error term in the structural equation of interest (the structural error) using a copula method, and makes inferences on the model parameters by maximizing the likelihood derived from the joint distribution. Similar to the “exclusion restriction” in instrumental variable methods, extant instrument-free methods require the assumption that the unobserved instruments are exogenous, a requirement that is difficult to meet. The proposed method does not require such an assumption. Other benefits of the proposed method are that it allows modeling of discrete endogenous regressors and offers a new solution to the slope endogeneity problem. In addition to linear models, the method is applicable to the popular random coefficient logit model with both aggregate data and individual data. We demonstrate the performance of the proposed method via a series of simulation studies and an empirical example.
Firms nowadays are increasingly proactive in trying to strategically capitalize on consumer networks and social interactions. In this paper, we complement an emerging body of research on the engineering of word-of-mouth (WOM) effects by exploring a different angle through which firms can strategically exploit the value-generation potential of the user network. Namely, we consider how software firms should optimize the strength of network effects at utility level by adjusting the level of embedded social media features in tandem with the right market seeding and pricing strategies, in the presence of seeding disutility. We explore two opposing seeding cost models where seeding-induced disutility can be either positively or negatively correlated with customer type. We consider both complete and incomplete information scenarios for the firm. Under complete information, we uncover a complementarity relationship between seeding and building social media features which holds for both disutility models. When the cost of any of these action increases, rather than compensating by a stronger action on the other dimension in order to restore the overall level of network effects, the firm will actually scale back on the other initiative as well. Under incomplete information, this complementarity holds when seeding disutility is negatively correlated with customer type but may not always hold in the other disutility model, potentially leading to fundamentally different optimal strategies. We also discuss how our insights apply to asymmetric networks.
Area Editor: Eitan Muller Keywords: Interpersonal Communication Word-of-mouth New product growth Brand choice Bass model SUV Interpersonal Communication (IPC), which includes word of mouth, plays an important role in the adoption of new products. Despite the extensive study of the role of IPC at the category level, its role at the brand level has not received its due attention. Using quarterly data on brand-level sales and marketing variables in the SUV category over 10 years, we show that IPC among previous adopters of a brand directly influences the sales of not only the brand itself but also the product category as a whole, which, in turn, indirectly influences brand sales. Additionally, we show that category-level IPC is distinct and separate from brand-level effects. Based on a detailed model-based accounting of IPC effects, we separate the direct effect of brand-level IPC on the sales of a brand from its indirect effect through product category sales, while allowing for category-level IPC and the ef-fects of marketing variables. We demonstrate the various effects of IPC using a numerical experiment.
This paper explores factors that influence consumer's intentions to purchase online at an electronic commerce website. Specifically, we investigate online purchase intention using two different perspectives: a technology-oriented perspective and a trust-oriented perspective. We summarise and review the antecedents of online purchase intention that have been developed within these two perspectives. An empirical study in which the contributions of both perspectives are investigated is reported. We study the perceptions of 228 potential online shoppers regarding trust and technology and their attitudes and intentions to shop online at particular websites. In terms of relative contributions, we found that the trust-antecedent perceived risk and the technology-antecedent perceived ease-of-use directly influenced the attitude towards purchasing online.European Journal of Information Systems (2003) 12, 4148. doi:10.1057/palgrave.ejis.3000445
(This article originally appeared in Management Science, January 1969, Volume 15, Number 5, pp. 215--227, published by The Institute of Management Sciences.) A growth model for the timing of initial purchase of new products is developed and tested empirically against data for eleven consumer durables. The basic assumption of the model is that the timing of a consumer's initial purchase is related to the number of previous buyers. A behavioral rationale for the model is offered in terms of innovative and imitative behavior. The model yields good predictions of the sales peak and the timing of the peak when applied to historical data. A long-range forecast is developed for the sales of color television sets.
Applications of random utility models to scanner data have been widely presented in marketing for the last 20 years. One particular problem with these applications is that they have ignored possible correlations between the independent variables in the deterministic component of utility (price, promotion, etc.) and the stochastic component or error term. In fact, marketing-mix variables, such as price, not only affect brand purchasing probabilities but are themselves endogenously set by marketing managers. This work tests whether these endogeneity problems are important enough to warrant consideration when estimating random utility models with scanner panel data. Our results show that not accounting for endogeneity may result in a substantial bias in the parameter estimates.
Instrumental variable (IV) methods are commonly used in accounting research (e.g., earnings management, corporate governance, executive compensation, and disclosure research) when the regressor variables are endogenous. While IV estimation is the standard textbook solution to mitigating endogeneity problems, the appropriateness of IV methods in typical accounting research settings is not obvious. Drawing on recent advances in statistics and econometrics, we identify conditions under which IV methods are preferred to OLS estimates and propose a series of tests for research studies employing IV methods. We illustrate these ideas by examining the relation between corporate disclosure and the cost of capital.
Many firms have introduced internet-based customer self-service applications, such as online payments or brokerage services. Despite high initial signup rates, not all customers actually shift their dealings online. We investigate whether the multi-stage nature of the adoption process (an `adoption funnel') for such technologies can explain this low take-up. We use exogenous variation in events that possibly interrupt adoption, in the form of vacations and public holidays in different German states, to identify the effect on regular usage of being interrupted earlier in the adoption process. We find that interruptions in the early stages of the adoption process reduce a customer's probability of using the technology regularly. Our results suggest significant cost-saving opportunities from eliminating interruptions in the adoption funnel.
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 inter-relationship between organic search listings and paid search advertisements using a unique panel dataset based on aggregate consumer response to several hundred keywords over three months 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, 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 (MCMC) 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 on an average the impact of organic clicks on increases in paid clicks is 3.5 times stronger than vice-versa. Using counter-factual 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 identification, 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 positive interdependence effect predicted by the econometric model is also corroborated in controlled experiments and potentially suggests a causal interpretation to the results obtained from the econometric model. Given the increased spending on search engine based advertising, our analysis provides critical insights to managers in both traditional and Internet firms.
Abstract In this paper, we investigate the effectiveness of the firm’s proactive management of customer-to-customer communication. We are particularly interested in understanding how, if at all, the firm should go about effecting meaningful word-of-mouth. To tackle this problem, we collect data from two sources: 1) We implemented,a large-scale field test in which a national firm created word of mouth,through,two populations: customers and non-customers, 2) We collected data from an online experiment. We break our theoretical problem into two subproblems. First, we ask, “What kind of WOM drives sales?” Motivated by previous research, we hypothesize that for a product with a low initial awareness level, the WOM that is most effective at driving sales is created by less-loyal (not highly-loyal) customers, and occurs between acquaintances (not friends). We find support for this in the field test as well as in an experimental,setting. Hence we demonstrate the potential usefulness of exogenously-created WOM: conversations,are created where,none would naturally have occured otherwise. Then, we ask, “Which agents are most effective at creating this kind of WOM?” In particular, we are interested in evaluating the effectiveness of the commonly-used opinion leader designation. We find that while opinion leadership is useful in identifying potentially effective spreaders of WOM among very loyal customers, it is less useful for the sample of less loyal customers. Keywords: Word of Mouth, Promotion, Advertising 1I ntroduction
The video game industry has attracted more and more attention not only from technology giants such as Microsoft but also from software developers and private investors. Information technology dictates how game console producers compete in the marketplace. Intensive IT competition in each console generation has shifted the market balance. Competitors jockey to position themselves as the first-mover within a generation or to wait and enter the market with cheaper and more advanced technologies. To capture the characteristics of IT-intensive products, we propose a multigeneration diffusion model that captures both cannibalization and competition effects. We apply the model to analyze game console diffusion with real shipment data for three game consoles from two companies: Sony and Microsoft. We analyze two scenarios: one with only Sony¡¯s products, and one with both companies¡¯ products. We find that the cannibalization between Sony¡¯s products is minimal, and Microsoft maintains a strong competitive edge that has challenged Sony¡¯s market position. The results also explain how Sony has maintained its position as the market leader over the last two generations. This research sheds light on the nature of an IT-intensive game console competition between companies and generations.