Mobile marketing, which involves two- or multi-way communication and promotion of an offer between a firm and its customers using the mobile, a term that refers to the mobile medium, device, channel, or technology, is growing in importance in the retailing environment. It has the potential to change the paradigm of retailing from one based on consumers entering the retailing environment to retailers entering the consumer's environment through anytime, anywhere mobile devices. We propose a conceptual framework that comprises three key entities: the consumer, the mobile, and the retailer. The framework addresses key related issues such as mobile consumer activities, mobile consumer segments, mobile adoption enablers and inhibitors, key mobile properties, key retailer mobile marketing activities and competition. We also address successful retailer mobile marketing strategies, identify the customer-related and organizational challenges on this topic, and outline future research scenarios and avenues related to these issues.
We investigate the causal relationship between brand community identification, brand affect, community loyalty, brand loyalty, brand evangelism, and community evangelism, developing a structural equation model set within the context of online brand communities for newly hybridized roses. The analysis considers size as a moderator for the relationships between constructs, comparing small and large web-based brand communities. Findings highlight that small communities operate differently from larger ones with regard to numerous aspects, and possess specific strengths and weaknesses. Members of small communities develop higher community loyalty; brand loyalty in small community stems more from community loyalty than from brand affect; small communities engage in word of mouth for the community more than in word of mouth for the brand. Managerial implications are addressed.
Economic theory indicates that E-retailers competing at price comparison sites, such as Shopper.com, must charge prices that cannot be systematically predicted by their rivals. Consistent with theory, we find significant variation in the identity of the lowprice firm as well as the level of the lowest price for 36 of the best-selling consumer electronics products sold at Shopper.com between November 1999 and May 2001. The observed pricing patterns can be explained by firms engaging in short-term price promotions in an attempt to avoid the deleterious outcome associated with price competition. Based on our arguments and the evidence presented, the managerial implications are clear: Strategic unpredictability in prices—through the use of hit and run sales—is a widely used and effective weapon for avoiding all-out price competition in online markets.
How consumer's waiting times affect their retrospective evaluations of Internet Web Sites is investigated in four computer-based experiments. Results show that waiting can but does not always negatively affect evaluations of Web Sites. Results also show that the potential negative effects of waiting can be neutralized by managing waiting experiences effectively. A conceptual framework and formal random utility model is introduced.
Based on a general framework of consumer perception and processing of advertising, this study examines the impact of animation and ad format on the attention and memorization of online ads. Consumer attention to a variety of real-world ads was measured with eye tracking and ad memory was assessed with recognition and recall tests. The results suggest that on average, animation had little or no effect on attention. We did nevertheless observe a strong interaction effect between animation and ad format, which suggests that the effect of animation is conditioned by ad format. Animation has a positive effect on attention to skyscrapers, but a negative one on attention to banners. As to memorization, animation improved recognition effects, but mainly for banners. Surprisingly, consumers could recognize ads without having looked at them, which suggests that online consumers are especially parsimonious in allocating their focal attention and memory resources to irrelevant ads when they are involved in other tasks.
In the increasingly complex retailing environment, more and more retailers operate in more than one channel, such as brick-and-mortar, catalogs, and online. Success in this dynamic environment relies on the strategic management and coordination of both online and offline pricing. This article provides an overview of findings from past research in both offline and online domains and presents an organizing framework, as well as an agenda to spur additional research.
Online trust is growing in importance as a topic of study and its influence on Internet marketing strategies is increasing. “Online trust includes consumer perceptions of how the site would deliver on expectations, how believable the site's information is, and how much confidence the site commands." (Bart, Yakov, Venkatesh Shankar, Fareena Sultan, and Glen L. Urban , “Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale Exploratory Empirical Study,” Journal of Marketing, 69(4), 133–152). In this article, we review advances in online trust research based on an overarching framework, outlining the key insights learned so far. These insights include: online trust extends beyond privacy and security, is closely connected to website design, its formation is an ongoing process, and is heterogeneous across individuals and products. We propose several ideas for future research relating to multiple aspects of online research, such as the longitudinal component, multichannel element, global aspect, personalization and cross-disciplinary nature.
Academic research has focused on the quality perceptions that drive customer satisfaction as the key to achieving e-service success. This paper develops a process-based model that relates perceptions of managerially actionable site characteristics to online satisfaction, which mediates the effects of site characteristics on intention to recommend e-services. A unique data set provided by Web Mystery Shoppers International Inc. (webmysteryshoppers.com), a market research supplier, enables the model to be refined using data from samples of responses to each of the competitive websites for one financial service, and then to be tested using similar data for another financial e-service and then for a travel e-service. The model, which accounts for most of the variance in online satisfaction and online intention to recommend in the fitted data, is largely confirmed on cross validation. Process evaluations and satisfaction mediate the effects of actionable website characteristics on intention to recommend e-services.
Today's multichannel, multimedia retail marketing environment presents a number of brand management challenges. From a micro perspective, marketers must manage each individual channel and communication option to maximize their direct sales and brand equity effects, as well as any indirect brand equity effects from being associated with a particular channel or communication option. From a macro perspective, marketers must design and implement channel and communication options such that sales and brand equity effects are synergistic. Concepts, frameworks, and future research directions are put forth to address these different challenges.
Companies have made major improvements in improving the ROI in areas such as production, logistics, and services. However, examining the productivity of marketing has long been ignored and has led many companies to view it as an expenditure that can be cut in difficult economic times. Calculating ROI for marketing expenditures such as media can help marketers defend their decisions, allocate limited resources the most profitably, and perhaps obtain larger budgets. In the study presented here, we perform a cross media analysis to compare interactive and traditional media.The Ford F-150 is used as a case example to illustrate how effectively comparing media results can improve resource allocation and maximize productivity from media expenditures.
In this article, we review user information technology acceptance literature, formulate a model of consumer adoption of third generation mobile multimedia services, validate it through a qualitative exploratory study comprising 24 focus groups in six markets, and empirically test the proposed model on the Italian market. The results show that perceived usefulness, ease of use, price, and speed of use are the most important determinants of adoption of multimedia mobile services, in that order. They also show that the importance of determinants differs by age groups or segments. The results can help managers proactively design interventions (such as training and marketing activities) targeted at populations of users that may be less inclined to adopt and use new multimedia mobile services.
In light of mature markets and increasing competitive pressure, retaining the existing customer base becomes crucial for the future success of a firm. As a consequence, firms are increasingly interested in understanding the factors influencing and driving customer retention. One factor that is hypothesized to have an impact on customer retention is the growing use of the Internet channel. Firms are interested in understanding whether and how the Internet use induces a change in customer retention.The aim of this paper is to empirically quantify the impact of Internet use on customer retention when accounting for potentially present self-selection. Furthermore, the paper will derive managerial implications on how to use customer channel migration to improve overall customer retention. The results of the empirical study indicate a strong positive impact of Internet use on customer retention. Hence, migrating customers to the Internet channel has the potential to increase overall retention rates.
We develop hypotheses about the effects of the dimensions (innova-tiveness, optimism, discomfort, and insecurity) of technology readiness on two key stages of Internet acceptance, adoption, and usage of different Internet-based activities, and test them through a two-stage model using U.S. consumer survey data. The findings show that these dimensions have significant enduring effects on the two stages at varying levels of perceived risk.
This article considers the supermarket manager's problem of forecasting-demand for a product as a function of the product's attributes and of market-control variables. To forecast sales on the stock keeping unit (SKU) level, a good model should account for product attributes, historical sales levels, and store specifics, and to control for marketing mix. One of the challenges here is that many variables which describe product, store, or promotion conditions are categorical with hundreds or thousands of levels in a single attribute. Identifying the right product attributes and incorporating them correctly into a prediction model is a very difficult statistical problem. This article proposes an analytical engine that combines techniques from statistical market response modeling, datamining, and combinatorial optimization to produce a small, efficient rule set that predicts sales volume levels.
How consumers feel about themselves—particularly in relation to technology—may have an important influence on their adoption and use of technology. Although research on electronic channels has shown that Web site and consumer characteristics are important predictors of consumer trust, researchers have not considered the role played by consumers' commitment to their identity as technology users. This paper explores whether consumer identity commitment and calculative commitment to electronic channels impact consumer use of electronic channels and perceived value from the service firm. More specifically, it examines whether these effects are mediated by trust in technology and trust in the firm. Using survey data from 834 consumers engaged in both offline and online banking, plus transaction frequency data supplied by a host firm, the study finds that identity commitment plays an important role in building consumer trust in technology and that calculative commitment increases transaction frequency directly, unmediated by trust in technology. Theoretical and managerial implications of these findings are explored.
Free, content-oriented Web sites depend on advertising revenues that are based on the number of visitors to the site. To induce visitors to acquire information quickly, these sites present information items according to their popularity.We empirically examine two key determinants of a visitor's inter-acquisition time (popularity and the number of information items previously acquired by the visitor) using a hazard model estimated from data obtained from del.icio.us, a social bookmarking site. The results indicate that interacquisition times are longer for heavy users and for less popular information items. The results are relevant for other free content-providing sites such as cnet.com and music.yahoo.com.
When consumers employ more than one channel within a single transaction,-they can obtain services from one retailer and place their business with another, hence they can engage in free riding. Conversely, customers may be inclined to stay with thesameretailer,evenwhenthey switch channels.We used empirical data to determine the magnitude of both effects in two directions: from online shops to traditional retail stores and vice versa.We found that over 20% of consumers are free riders, and that retailers retain substantially fewer customers in both directions.To explain the variance within the free-riding rate and the customer-retention rate, we investigated the influence of selected product characteristics (search characteristics, speed of technological change, and purchase frequency) on cross-channel consumer behavior.Managerial recommendations based on the magnitude of the effects concern channel management and the relationship between retailers and manufacturers.
We investigate consumer preference for online versus offline purchasing of a complex service (home mortgage), across the three stages of purchasing, namely, pre-purchase, purchase, and post-purchase. Our analysis of data from 300 consumers shows that (1) the offline channel is generally preferred over the online channel across all the stages, and (2) the channel usage intention in a particular stage is moderated by the consumer's Internet experience. Specifically, in both the pre- and post-purchase stages, the usage intention for the online channel is higher when consumers have more favorable Internet experience. In the purchase stage, consumers prefer the offline channel over the online channel, regardless of their Internet experience. Furthermore, we find that the drivers of channel preference are substantially different across the three buying stages due to (in)congruities between channel benefits desired and channel capabilities offered.
This case study shows how interactive marketing campaigns can be systematically fine-tuned and made more productive through adaptive experimentation. It details the use of adaptive experimentation in a viral marketing campaign at Plaxo, Inc., a company that provides Internet-based updating of personal contact information. The experiences of Plaxo highlight that even for a product that is amenable to viral marketing, growth is not guaranteed. To achieve a desired level of growth, Plaxo identified the product features that contributed to greater adoption conversion of recipients of its marketing message and improved them through continuous experimentation. To overcome potential negative side effects of aggressive viral growth, the company used a carefully crafted feedback loop via internet alert services that tapped into the blogging community. This practice allowed management to better understand negative perceptions of the product and take timely corrective actions.
Customer Relationship Management (CRM) is about introducing the right product to the right customer at the right time through the right channel to satisfy the customer's evolving demands; however, most existing CRM practice and academic research focuses on methods to select the most profitable customers for a scheduled CRM intervention. In this article, we discuss a two-step procedure comprising “adaptive learning” and “proactive” CRM decisions. We also discuss three key components for customer-centric CRM: adaptive learning, forward-looking, and optimization. We then formulate CRM interventions as solutions to a stochastic dynamic programming problem under demand uncertainty in which the company learns about the evolution of customer demand as well as the dynamic effect of its marketing interventions, and make optimal CRM decisions to balance the cost of interventions and the long-term payoff. Finally, we choose two examples to demonstrate the input, output, and benefit of “adaptive” learning and “proactive” CRM.
Virtual try-on technology (referred to in this article as Virtual Try-on) can deliver product information that is similar to the information obtained from direct product examination. In addition, the interactivity and customer involvement created by Virtual Try-on can enhance the entertainment value of the online shopping experience. We used focus group interviews and an online national survey to investigate online apparel shoppers' use of Virtual Try-on to reduce product risks and increase enjoyment in online shopping.We also examined the impact of two important external variables (innovativeness and technology anxiety) that are not included in the electronic Technology Acceptance Model (e-TAM) but were expected to influence adoption of Virtual Try-on and whether or not gender differences existed in the Virtual Try-on adoption process. We examined this dual (functional and hedonic) role of Virtual Try-on by applying a modified e-TAM model to the Virtual Try-on technology adoption process and tested model invariance among male and female shoppers using Virtual Try-on in a simulated online shopping experience. The extended research model was validated in the context of Virtual Try-on adoption.Technology anxiety and innovativeness had significant moderating effects on the relationship between attitude and use of Virtual Try-on technology; however, there was no significant gender difference in the overall adoption process for Virtual Try-on.
Click-through rates are still the de facto measure of Internet advertising effectiveness. Unfortunately, click-through rates have plummeted. This decline prompts two critical questions: (1) Why do banner ads seem to be ineffective and (2) what can advertisers do to improve their effectiveness? To address these questions, we utilized an eye-tracking device to investigate online surfers' attention to online advertising. Then we conducted a large-scale survey of Internet users' recall, recognition, and awareness of banner advertising. Our research suggests that the reason why click-through rates are low is that surfers actually avoid looking at banner ads during their online activities. This implies that the larger part of a surfer's processing of banners will probably be done at the pre-attentive level. If such is the case, click-through rate is an ineffective measure of banner ad performance. Our research also shows that banner ads do have an impact on traditional memory-based measure of effectiveness. Thus, we claim that advertisers should rely more on traditional brand equity measures such as brand awareness and advertising recall. Using such measures, we show that repetition affects unaided advertising recall, brand recognition, and brand awareness and that a banner's message influences both aided advertising recall and brand recognition.
Marketers often compete to incorporate fast-moving images in their online banner ads to break through the ad clutter, in the hope for a positive perception of the ads. However, the findings of this study suggest that this strategy may not work. An experiment was designed to explore the effects of the degree of animation on memory and attitudes toward ads. The results showed inverted U-shaped relationships between the level of animation and both recognition rates and A ad, suggesting the existence of unintended negative effects of highly animated online banner ads. Under high-animation conditions, subjects experienced negatively valenced thoughts and unpleasant feelings, which negatively influenced A ad. Also, subjects were highly aroused, as indicated by the increased level of emotional intensity; this arousal inhibited subjects' ad recognition performance. These findings show different processing mechanisms under different animation levels, and suggest that marketers should exercise caution when using animation in their ads.
Consumers have become increasingly savvy about technology in recent years, and many of them ignore Web ads during online activities. In this context, measuring advertising effects based on the traditional cognitive models of information processing may undervalue the effectiveness of Web ads. This study experimentally examined the effects of unconscious processing of Web ads by manipulating the level of attention paid to the ad (directed vs. nondirected attention). Online advertisers should be encouraged by the findings of this study. The results suggest that, upon exposure to Web ads, consumers experience priming caused by implicit memory and build a more favorable attitude toward the advertised brand regardless of the levels of attention they paid to the advertisements. Furthermore, those who unconsciously processed Web ads did not remember seeing the ad explicitly, but they were more likely to include the advertised brand in the consideration set than those who had no exposure.
The Internet opens new opportunities for conducting pre-purchase information search. Lower search costs have been found to affect use of the Internet for this purpose. Benefits in terms of the large amounts of information available and freedom from physical contact with sales staff have also been found to affect the use of the Internet for information search positively. In this paper, low costs and benefits are put together in a model. The model was tested by means of structural equation modeling on a sample of Danish Internet users (n = 233). The main result is that the amount of Internet use affects use of the Internet for pre-purchase information search more than perceived low search costs and perceived availability of information. Further, the test of the model did not support that pleasure in shopping and a preference for personal contact to sales staff affect the use of the Internet for information search.
This study examines the impact of having an anthropomorphic information agent (a humanlike chatbot that acts as an interactive online information provider) in an online store on consumers' attitude toward the Web site, product, and their purchase intentions. Using consumer experiments, we show that the impact of the anthropomorphic information agent is moderated by the amount of static product information available on the Web site and the consumer's consumption motive at the time of visiting the Web site. Our results indicate that the anthropomorphic information agent has a positive effect when static product information on the Web site is limited. Furthermore, we show that when detailed product information is readily available on the Web site, the anthropomorphic information agent can prove detrimental when the consumer has a utilitarian consumption motive.
This paper studies how smart recommendation agents (those that filter and integrate information and offer feedback to customers) influence consumer decision making in online stores, in comparison to recommendation agents that are merely “knowledgeable” of the alternative options that exist in a product assortment. The cognitive cost model is used to propose hypotheses that link information search and alternative evaluation with two shopping environment influences that are typical of online settings. A study that simulates search and evaluation in a Web-based choice environment is conducted to test the hypotheses. The results offer insights into how the “feedback” provided by a recommendation agent on the product options available may have an effect on search and evaluation in an online store.
Scoring models predict responses to some contact that will be made in the future, helping an organization decide which customers to target. They are usually built from a single “proxy” contact from the past, for which responses have already been observed. This approach is risky because there could be differences between the proxy and future contact, and other exogenous factors could have changed. We propose averaging predictions from multiple scoring models and develop a rationale for this approach by showing under certain assumptions that the expected squared difference between the true responses to the future contact and the predicted values from the averaged model is less than or equal to the expected squared difference from a single previous contact. The improvement of the aggregated model over the single model increases as (1) the variation in effect sizes across contacts increases, (2) the number of averaged contacts increases, and (3) the variance of the effect estimates increases. We incorporate the effects of external factors in our model by weighting the coefficients with a general linear model (GLM). Using data from a retail catalog company and a nonprofit organization, we evaluate our model empirically by testing whether our assumptions hold, examine the extent of variation in slopes and predicted values across models build from various previous contacts, evaluate the amount of improvement over extant models in terms of prediction error and performance as measured by a gains table, and study how improvement depends on the number of averaged contacts. Conservative estimates suggest that our method could increase annual profits for the nonprofit organization by over a half-million dollars and tens of thousands of dollars for the small catalog company.
This article examines the impact of using incremental amounts of purchasing data on the ability to classify consumers in consumer packaged goods categories for direct marketing purposes. Building on the work of Rossi, McCulloch, and Allenby (1996), who focused on the impact of three information sets—(a) demographics only, (b) demographics and one purchase made by a consumer, and (c) demographics plus an entire purchasing history of a consumer—we examine the impact of each additional purchase, starting with no purchasing information (i.e., demographics only) through 20 purchases. Using two different classification models, a Multinomial Logit model and an Artificial Neural Network model, we examine the sensitivity of classification accuracy to each additional purchase. We use these results in a profitability analysis of a hypothetical direct marketing campaign to determine the optimal number of purchases to use for classification in the category studied. The findings suggest an optimal number of purchasing observations exists for classification and targeting purposes and this optimal number falls between one purchase and a “history” of purchases as studied by Rossi et al. Our findings illustrate the importance of conducting a sensitivity analysis to identify the optimal amount of purchasing data to use when classifying consumers for the purpose of a direct marketing campaign.
Price is an important factor affecting consumer preference for online brands and, not surprisingly, has played a dominant role in the Internet marketing literature. Concurrently, important nonprice factors that account for individual online brand preferences have received scant attention. This research examines nonprice influences on preferences for online brands and demonstrates that brand character, brand offerings, prior Internet shopping experience, brand familiarity, and brand evaluation affect online brand preference. Using data from cyber and extension brands, our results show that influences on consumer preference for the types of brands examined are different. In the case of cyber brands, familiarity, character, and brand offerings are related to brand preference. In contrast, extension brands benefit from having market-based counterparts, and for these firms, brand familiarity is not a predictor of consumer preference. Breadth of offerings is the most important predictor for both cyber- and extension-brand preference. Future research and managerial implications are discussed.
Marketers face a myriad of decisions when developing a Web site for e-commerce. In this article, we attempt to organize our current understanding of consumer behavior into streams of research that address the development of marketplaces for the digital economy. We start by characterizing computerbased decision environments as Marketplaces of the Artificial, arguing that the unbundling of product information from products presents many decisions and opportunities for the design of decision environments. We then review four areas of research, identifying themes in each area. These areas are (a) the economics of search, (b) cognitive cost approaches, (c) constructed preference approaches, and (d) phenomenological approaches. We illustrate each approach, highlighting its assumptions and discussing examples of research questions and results.
Each customer varies in his/her lifetime value to a firm. A firm would like to estimate the lifetime value of each customer and use this information in planning differential marketing initiatives targeted at each customer. Customer lifetime value computations require different approaches depending on the business application that a firm is looking at. The authors present two approaches of computing customer lifetime value and offer some best practice applications. The authors also address challenges that firms typically face in implementing the customer lifetime value approach to marketing.