Does Online Information Drive Offline Revenues?: Only for Specific Products and Consumer Segments!

While many offline retailers have developed informational websites that offer information on products and prices, the key question for such informational websites is whether they can increase revenues via web-to-store shopping. The current paper draws on the information search literature to specify and test hypotheses regarding the offline revenue impact of adding an informational website. Explicitly considering marketing efforts, a latent class model distinguishes consumer segments with different short-term revenue effects, while a Vector Autoregressive model on these segments reveals different long-term marketing response.We find that the offline revenue impact of the informational website critically depends on the product category and customer segment. The lower online search costs are especially beneficial for sensory products and for customers distant from the store. Moreover, offline revenues increase most for customers with high web visit frequency. We find that customers in some segments buy more and more expensive products, suggesting that online search and offline purchases are complements. In contrast, customers in a particular segment reduce their shopping trips, suggesting their online activities partially substitute for experiential shopping in the physical store. Hence, offline retailers should use specific online activities to target specific product categories and customer segments.     

Online Purchase Paths and Conversion Dynamics across Multiple Websites

Low transportation costs online allow shoppers to visit multiple e-commerce sites for a purchase decision. This research investigates online shoppers’ visit and purchase behaviors across competing websites. To consider that shoppers’ longitudinal cross-site visit data may consist of several unobserved shopping episodes, we propose a modeling approach to probabilistically clustering and relating online visits to latent shopping episodes, based on the temporal patterns of the visit events. The inferences are then used to examine shoppers’ visit-to-purchase behavior across websites. Using Internet clickstream data on individual-level browsing and transaction records at major air travel sites, we find that online shoppers’ cross-site visit patterns tend to be clustered and the purchase propensity is significantly higher at later visits within a visit cluster, compared to earlier visits. As our results suggest the possibility that visit clusters can serve as a reasonable proxy for shopping episodes, we look further into shoppers’ website choice and purchase behaviors within a cluster. We discuss how the cluster-based analysis can help managers tailor online marketing and advertising strategies based on shoppers’ cross-site visit and purchase patterns.     

Online Pricing: Review and Directions for Research

The Internet influences prices in two ways: it is a channel for obtaining information, and it is a vehicle for transactions. This paper reviews the literature on the impact of online information on prices, and also reviews the literature on how the features of online markets influence online pricing. The major influences on online pricing that are reviewed are costly and limited search, switching costs, reputation, heterogeneity in search costs, heterogeneity in demand for services, and online–offline competition. Based on this review, suggestions for further research are outlined.     

An empirical analysis of factors that influence retail website visit types

Consumers may have a variety of reasons for using a retail brand's website, including browsing, purchasing, or browsing followed by purchasing. In this study we examine visits to three different retail websites to develop a typology of website visit behaviors and reveal factors that are associated with each visit type. We find four visit types that are consistent across all brands, which we label “touching base,” “search/deliberation,” “goal-directed,” and online shopping “cart-only” visits. One of the brands has an additional visit type we term “considered visits.” The type of visit a consumer makes is influenced by a combination of marketing channels, and their visit and purchase history with the brand. For example, shoppers that are directed to a retailer's website by clicking on a search engine link (paid or organic) are more likely to make visits that are associated with a purchase goal, while visits generated by an email are more likely to be just touching base. These findings provide marketers with a more refined understanding of the different ways consumers use their websites, and the factors associated with these visit behaviors.     

An investigation of factors affecting the visits of online crowdsourcing and labor platforms

Nowadays, the economic activities have become increasingly digital since hundreds of millions of Internet users are using crowdsourcing platforms either to work at an online job as workers, or as a model of problem-solving and production as requesters. This growing workforce makes it necessary from the perspective of the online platforms, to fully understand the business dimensions of this emerging and innovative “online labor” phenomenon, which can rapidly change the future of work and work organization in the online world. This paper aims to investigate and analyze the visits of online labor platforms that offer crowdsourcing and crowdfunding services. Using websites’ metrics data drawn from Alexa for the time period 2012-2016 the paper uses Ordinary Least Squares (OLS) and Fixed Effects (FE) regression analysis to examine correlations between visits and website characteristics. The research shows that the sessions of an online labor marketplace website from mobile devices have an increasing trend to be positively correlated to the quality mechanisms a website deploys as well as on location-dependent factors. The results are expected to provide insights on how the online labor website characteristics affect their traffic and thus inform about their evolution and improvement.     

Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com

We investigate the cross channel effects of search engine advertising on Google.com on sales in brick and mortar retail stores. Obtaining causal and actionable estimates in this context is challenging: Brick and mortar store sales vary widely on a weekly basis; offline media dominate the marketing budget; search advertising and demand are contemporaneously correlated; and estimates have to be credible to overcome agency issues between the online and offline marketing groups. We report on a meta-analysis of a population of 15 independent field experiments, in which 13 well-known U.S. multi-channel retailers spent over $4 Million in incremental search advertising. In test markets category keywords were maintained in positions 1-3 for 76 product categories with no search advertising on these keywords in the control markets. Outcomes measured include sales in the advertised categories, total store sales and Return on Ad Spending. We estimate the average effect of each outcome for this population of experiments using a Hierarchical Bayesian (HB) model. The estimates from the HB model provide causal evidence that increasing search engine advertising on broad keywords on Google.com had a positive effect on sales in brick and mortar stores for the advertised categories for this population of retailers. There also was a positive effect on total store sales. Hence the increase in sales in the advertised categories was incremental to the retailer net of any sales borrowed from non-advertised categories. The total store sales increase was a meaningful improvement compared to the baseline sales growth rates. The average Return on Ad Spend (ROAS) is positive, but does not breakeven on average although several retailers achieved or exceeded break-even based only on brick and mortar sales. We examine the robustness of our findings to alternative assumptions about the data specific to this set of experiments. Our estimates suggest online and offline are linked markets, that media planners should account for the offline effects in the planning and execution of search advertising campaigns, and that these effects should be adjusted by category and retailer. Extensive replication and a unique research protocol ensure that our results are general and credible.     

How Online Product Reviews Affect Retail Sales: A Meta-analysis

A growing body of research has emerged on online product reviews and their ability to elicit performance outcomes desired by retailers; yet, a common understanding of the performance implications of online product reviews has eluded us. Scholars continue to navigate an array of studies assessing different design elements of online product reviews, and various research settings and data sources. We undertake a meta-analysis of 26 empirical studies yielding 443 sales elasticities to examine how these variables relate to retail sales. Building on well-established meta-analytical methods, we address the following questions: How does review valence influence the elasticity of retailer sales? What about review volume? For which product types and usage situations do online product reviews have a greater impact on retailer sales elasticity? Which types of online reviewers and websites exert the greatest influence on retailer sales elasticity? Our study answers these important questions and provides a much needed quantitative synthesis of this burgeoning stream of research.     

Consumer responses to offline and online low price signals: The role of cognitive elaboration

Low price signals (LPS), pricing tools where retailers promise to match or beat competitors' prices, have been increasingly popular in offline and online markets. We compare consumer evaluation of offline and online LPS as a function of how deeply they process the signals. Results of an experiment indicate that regardless of retail media consumers accept an LPS as an indicator of low price when they do not elaborate sufficiently on the signal. However, at high levels of elaboration, consumers challenge the assumptions underlying their acceptance of the signal at lower levels of elaboration whereby they become more skeptical of an online signal than an offline signal resulting in lower efficacy of the former. Implications of these findings for consumer vulnerability to false low price signaling are discussed along with other theoretical and managerial implications. Additionally, directions for future research are provided.     

Buying while expecting to sell: The economic psychology of online resale

Consumer online resale is becoming increasingly common for transactions of secondhand goods. However, when accompanied by a preconceived intention to resell a product after using it, the initial consumer purchasing decision for self-use is complicated by the estimated resale value of that good. We applied the principles of mental accounting to develop and evaluate a new concept that may influence consumer resale and purchase intention: external resale reference price (ERRP). The study examines how online consumer sellers' economic psychology of buying affected their expectation of future online resale outcome. The results indicate that (1) consumers' awareness of future online resale potential can influence their purchasing decisions; (2) ERRP, which is mediated by the estimated resale return, can increase purchase intention; and (3) the effects of ERRP on purchase intention are moderated by online resale likelihood, but are minimal when consumers are aware that resale possibility is extremely low.     

Online and offline retailing: What we know and directions for future research

The fast-paced growth of e-commerce is rapidly changing consumers’ shopping habits and shaping the future of the retail industry. While online retailing has allowed companies to overcome geographic barriers to selling and helped them achieve operational efficiencies, offline retailers have struggled to compete with online retailers, and many retailers have chosen to operate both online and offline. This paper presents a review of the literature on the interaction between e-commerce and offline retailing, highlighting empirical findings and generalizable insights, and discussing their managerial implications. Our review includes studies published in more than 50 different academic journals spanning various disciplines from the inception of the internet to present. We organize our paper around three main research questions. First, what is the relationship between online and offline retail channels including competition and complementarity between online and offline sellers as well as online and offline channels of an omnichannel retailer? Under this question we also try to understand the impact of e-commerce on market structure and what factors impact the intensity of competition /complementarity. Second, what is the impact of e-commerce on consumer behavior? We specifically investigate how e-commerce has impacted consumer search, its implications for price dispersion, and user generated content. Third, how has e-commerce impacted retailers’ key managerial decisions? The key research questions under this heading include: (i) What is the impact of big data on retailing? (ii) What is the impact of digitization on retailer outcomes? (iii) What is the impact of e-commerce on sales concentration? (iv) What is the impact of e-commerce and platforms on pricing? And (v) How should retailers manage product returns across online and offline channels? Under each section, we also develop detailed recommendations for future research which we hope will inspire continued interest in this domain.     

Why is Price Dispersion Higher Online than Offline? The Impact of Retailer Type and Shopping Risk on Price Dispersion

When physically similar products, of similar quality, are offered by retailers both online and offline, we often observe that the dispersion in prices of these products online is greater than the price dispersion offline. This observation runs counter to early theories that suggested price dispersion online would be smaller than that offline due to the ease of search and information availability online. This paper investigates and provides an explanation for this puzzling phenomenon by examining the impact of two important drivers of price dispersion: retailer type and consumers’ shopping risk. Retailer type refers to whether a retailer is a pure offline, pure online, or dual channel retailer. Shopping risk is defined as the product of consumers’ perceived risk of shopping and the transaction uncertainty related to shopping at different types of retailers.A game-theoretic approach is adopted to model consumers’ price search and product purchase, as well as price competition within and across retailer types in online and offline markets. Equilibrium pricing strategies are derived for different retailer types competing for different consumer segments with different levels of perceived shopping risk. The impact of retailer type and shopping risk on online versus offline price dispersion are quantified, and conditions when price dispersion is greater online than offline are identified.Results indicate that price dispersion is greater online when the number of pure online retailers is sufficiently large and is increasing in the number of pure online retailers. In addition, a reduction in online shopping risk may actually increase online price dispersion. Results further suggest that even without any online sales, dual channel retailers should maintain their online presence for the purpose of information dissemination, which justifies the importance for pure offline retailer to incorporate webrooming strategies, where consumers can search for prices online but purchase offline.     

A Hierarchical Marketing Communications Model of Online and Offline Media Synergies

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 Impact of Online and Offline Information Sources on Automobile Choice Behavior

The Internet has significantly impacted the information search behavior of consumers. Many consumers regularly consult Internet sources for information on product categories, brands, manufacturers, and retailers, particularly when making a purchase decision about major durable goods. Automobiles are one example of such goods. The Internet has become a major source for information on automobile brands, attributes, and dealers. While much research has been done on the impact of the Internet on automobile information search behavior and search costs, there is limited work on the relationship between Internet use and the ultimate automobile choice. This type of relationship may have interesting managerial implications for both manufacturers of automobiles and firms that provide information on automobiles. This paper attempts to address this gap. It examines whether Internet use is associated with different choice patterns for automobiles. Using discrete choice analysis on automobile choice data, we explore whether there will be differences in the salience of specific information types for online versus offline consumers. We find that Internet users rely more on ratings while non-Internet users rely more on recommendations when making automobile choices. Our findings have several useful managerial implications for information provision, both online and offline.     

The roles of multiple channels in predicting website visits and purchases: Engagers versus closers

In today’s online environment, consumers and sellers interact through multiple channels such as email, search engines, banner ads, affiliate websites and comparison-shopping websites. In this paper, we investigate whether knowing the history of channels the consumer has used until a point of time is predictive of their future visit patterns and purchase conversions. We propose a model in which future visits and conversions are stochastically dependent on the channels a consumer used on their path up to a point. Salient features of our model are: (1) visits by consumers are allowed to be clustered, which enables separation of their visits into intra- and inter-session components, (2) interaction effects between channels where prior visits and conversions from channels impact future inter-session visits, intra-session visits and conversions through a latent variable reflecting the cumulative weighted inventory of prior visits, (3) each channel attracts inter-session and intra-session visits differently, (4) each channel has different association with conversion conditional on a customer’s arrival to the website through that channel, (5) each channel engages customers differently (i.e., keeps the customer alive for a next session or for a next visit within a session), (6) the channel from which there was an arrival in the previous session can have an enhanced ability to generate an arrival for the same channel in the current session (channel persistence), and (7) parsimonious specification for high dimensionality in a low-velocity, sparse-data environment. We estimate the model on easy-to-collect first-party data obtained from an online retailer selling a durable good and find that information on the identities of channels and incorporation of inter- and intra-session visits have significant predictive power for future visitation and conversion behavior. We find that some channels act as “closers” and others as “engagers”—consumers arriving through the former are more likely to make a purchase, while consumers arriving through the latter, even if they do not make a purchase, are more likely to visit again in the future or extend the current session. We also find that some channels engage customers more than others, and that there are interaction effects between the channels visited. Our estimates show that the effect of prior inventory of visits is different from the immediate prior visit, and that visit and purchase probabilities can increase or decrease based on the history of channels used. We discuss several managerial implications of the model including using the predictions of the model to aid in selecting customers for marketing actions and using the model to evaluate a policy change regarding the obscuring of channel information.     

Digital distribution and the prohibition of resale markets for information goods

An existing theoretical literature finds that frictionless resale markets cannot reduce profits of monopolist producers of perfectly durable goods. This paper starts by presenting logical arguments suggesting this finding does not hold for goods consumers tire of with use, implying the impact of resale is an empirical question. The empirical impact is then estimated in the market for video games, one of many markets in which producers may soon legally prevent resale by distributing their products digitally as downloads or streamed rentals. Estimation proceeds in two steps. First, demand parameters are estimated using a dynamic discrete choice model in a market with allowed resale, using data on new sales and used trade-ins. Then, using these parameter estimates, prices, profits, and consumer welfare are simulated under counterfactual environments. When resale is allowed, firms are unable to prevent their goods from selling for low prices in later periods. The ability to do so by restricting resale outright yields significant profit increases. Renting, however, does not raise profits as much due to a revenue extraction problem.     

Used goods, not used bads: Profitable secondary market sales for a durable goods channel

The existing literature on channel coordination typically models markets where used goods are not sold, or are sold outside the standard channel. However, retailers routinely sell used goods for a profit in markets like textbooks. Further, such markets are characterized by a renewable consumer population over time, rather than the static consumer population often assumed in prior literature. We show that accounting for these market characteristics alters the optimal contract form as compared to the contracts derived in prior research. In particular, when new goods are sold in both the first and second periods of our model, the optimal contract differs from those in prior literature in that it can exhibit a negative fixed fee in the second period and requires contracting over the resale price in the second period. The model shows that the manufacturer makes higher profits from allowing used-good sales alongside new-good sales than from shutting down the retailer-profitable secondary market, and that unit sales expand with a profitable secondary market over those achievable without a secondary market. Furthermore, in contrast to previous investigations of durable goods markets that ignore the possibility of a retailer-profitable secondary market, we show conditions under which the manufacturer would optimally choose to sell no new goods in the second period, ceding the market entirely to the used-goods retailer. This research thus expands our knowledge of how durable goods markets work by incorporating the profitable operation of a retailer-run resale market.

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Website attributes that increase consumer purchase intention: A conjoint analysis

Prior studies investigate factors that affect consumer preferences in online shopping websites. However, prior studies, due to their methodological limitations, do not thoroughly investigate consumer preference structures that reflect the relative importance of attributes and features of shopping websites. By synthesizing prior literature, this research proposes and investigates a comprehensive list of attributes and features of shopping websites that increase consumer purchase intention. This research used IT-based platforms for data collection, and collected data from university students. Data analysis enabled us to categorize consumers that use online shopping websites into three groups based on the attributes they prefer. Consumers with different levels of computer expertise also showed differing preference structures. This research has implications in both research and practice.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

A conceptual model of motivations for consumer resale on C2C websites

More consumers nowadays are acting the role of reseller by taking advantage of online consumer-to-consumer (C2C) auction sites. Such auctions may involve new behaviours created by the new technology. Yet the amateur consumer seller, a key player in C2C online auctions, has rarely been the focus of research; scholars know little about online reselling behaviour. Using a literature review, from participant observation and in-depth interviews with 48 Taiwanese consumers involved in 250 online resale transactions, 4 intrinsic motivations of online resale behaviour are identified. A conceptual model of consumer resale motivations is presented and corresponding research propositions are developed based on the existing literature and the preliminary findings of this exploratory empirical study. Finally, conclusions, managerial implications and outline directions for further research are discussed.