Consumers make multicategory decisions in a variety of contexts such as choice of multiple categories during a shopping trip or mail-order purchasing. The choice of one category may affect the selection of another category due to the complementary nature (e.g., cake mix and cake frosting) of the two categories. Alternatively, two categories may co-occur in a shopping basket not because they are complementary but because of similar purchase cycles (e.g., beer and diapers) or because of a host of other unobserved factors. While complementarity gives managers some control over consumers' buying behavior (e.g., a change in the price of cake mix could change the purchase probability of cake frosting), co-occurrence or co-incidence is less controllable. Other factors that may affect multi-category choice may be (unobserved) household preferences or (observed) household demographics. We also argue that not accounting for these three factors simultaneously could lead to erroneous inferences. We then develop a conceptual framework that incorporates complementarity, co-incidence and heterogeneity (both observed and unobserved) as the factors that could lead to multi-category choice. We then translate this framework into a model of multi-category choice. Our model is based on random utility theory and allows for simultaneous, interdependent choice of many items. This model, the multi probit model, is implemented in a Hierarchical Bayes framework. The hierarchy consists of three levels. The first level captures the choice of items for the shopping basket during a shopping trip. The second level captures differences across households and the third level specifies the priors for the unknown parameters. We generalize some recent advances in Markov chain Monte Carlo methods in order to estimate the model. Specifically, we use a substitution sampler which incorporates techniques such as the Metropolis Hit-and-Run algorithm and the Gibbs Sampler. The model is estimated on four categories (cake mix, cake frosting, fabric detergent and fabric softener) using multicategory panel data. The results disentangle the complementarity and co-incidence effects. The complementarity results show that pricing and promotional changes in one category affect purchase incidence in related product categories. In general, the cross-price and cross-promotion effects are smaller than the own-price and own-promotions effects. The cross-effects are also asymmetric across pairs of categories, i.e., related category pairs may be characterized as having a “primary” and a “secondary” category. Thus these results provide a more complete description of the effects of promotional changes by examining them both within and across categories. The co-incidence results show the extent of the relationship between categories that arises from uncontrollable and unobserved factors. These results are useful since they provide insights into a general structure of dependence relationships across categories. The heterogeneity results show that observed demographic factors s...

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link Gamification and Mobile Marketing Effectiveness AbstractA variety of business sectors have been buffeted by the diffusion of mobile technology, a trend that presents a variety of difficult challenges but interesting opportunities to marketers. One such opportunity is gamification, which, one hopes, will enhance appeal to mobile consumers.Our sense from both personal experience and the literature is that the gamified mobile apps currently offered by firms mostly miss the mark. We provide a systematic overview of game design and note how principles derived from that field are highly applicable to gamification in mobile marketing settings. We are aided by the work of Schell (2008), whose Elemental GameTetrad Model allows us to offer a coherent look at how gamification should affect mobile marketing outcomes.

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The two main influences leading to adoption at the individual consumer level are marketing communication and interpersonal communication. Although evidence of the effect of these two influences is abundant at the market level, there is a paucity of research documenting the simultaneous effect of both influences at the individual consumer level. Thus, the primary objective of this paper is to fill the gap in the literature by documenting the existence and magnitude of both influences at the customer level while controlling for unobserved temporal effects. The pharmaceutical industry provides an appropriate context to study this problem. It has been conjectured that adoption and usage patterns of a new drug by physicians—“contagion”—acts as a “consumption externality,” as it allows a given physician to learn about the efficacy and use of the drug. In addition, pharmaceutical companies target individual physicians via marketing activities such as detailing, sampling, and direct-to-consumer advertising. Our data contain the launch of a new drug from an important drug category. We chose two unrelated markets (Manhattan and Indianapolis) for our empirical analysis. We model an individual physician's decision to adopt a new drug in a given time period as a binary choice decision. This decision is modeled as a function of temporal trends (linear and quadratic) and individual physician-level contagion and marketing activity (both individual level and market level). Our contagion measure aggregates the adoption behavior of geographically near physicians for each physician in our sample. Our results from the Manhattan market indicate that both targeted communication and contagion have an effect on the individual physician's adoption decision. A major challenge is to rule out alternative explanations for the detected contagion effect. We therefore carry out a series of tests and show that this effect persists even after we control for the effects of time, individual salespeople, other marketing instruments, local market effects, and the effects of some institutional factors. We believe that our contagion effect arises because the consumption externality is stronger for geographically close physicians. We discuss some underlying processes that are probably giving rise to the contagion effect we detected. Finally, we compute the social multiplier of marketing and find it to be about 11%. We also use the estimated parameters to compare the relative effect of contagion and targeted marketing. We find that marketing plays a large (relative) role in affecting early adoption. However, the role of contagion dominates from month 4 onward and, by month 17 (or about half the duration of our data), asymptotes to about 90% of the effect.new product adoption, social networks, social interactions, contagion, word of mouth, hierarchical Bayesian methods, pharmaceutical industry

marketing communication. The first effect refers to marketing communication that enables consumers to update their prior beliefs and reduce uncertainty about the true quality of the new product through a Bayesian learning process. Because marketing communication affects consumer utility indirectly through perceived product quality, we refer to it as the "indirect Bayesian learning effect," or simply "indirect effect." The second effect consists of all effects that are not indirect (e.g., reminder effects) that influence preferences through goodwill accumulation. Because this effect is manifest in a direct shift in consumer utility, we refer to it as the "direct goodwill effect," or "direct effect."Our empirical analysis uses data from a category of ethical drugs. In the pharmaceutical industry, direct marketing communication with physicians is usually referred to as detailing. Detailing comprises promotional visits made to physicians by pharmaceutical representatives. 1 The main sources of information considered by physicians to inform their current diagnoses and prescription decisions are detailing, meetings and conferences, and feedback from previous prescriptions. Additional sources of information include word of mouth and journal advertising. Our main focus is the effect of detailing on the evolution of physician prefer-

This article focuses on whether banner advertising affects purchasing patterns on the Internet. Using a behavioral database that consists of customer purchases at a Web site along with individual advertising exposure, the authors measure the impact of banner advertising on current customers' probabilities of repurchase, while accounting for duration dependence. The authors model the probability of a current customer making a purchase in any given week (since the last purchase) with a survival model that uses a flexible, piecewise exponential hazard function. The advertising covariates are purely advertising variables and advertising/individual browsing variables. The model is cast in a hierarchical Bayesian framework, which enables the authors to obtain individual advertising response parameters. The results show that the number of exposures, number of Web sites, and number of pages all have a positive effect on repeat purchase probabilities, whereas the number of unique creatives has a negative effect. Returns from targeting are the highest for the number of advertising exposures. The findings also add to the general advertising literature by showing that advertising affects the purchase behavior of current (versus new) customers.

Sales response models are widely used as the basis for optimizing the marketing mix. Response models condition on the observed marketing-mix variables and focus on the specification of the distribution of observed sales given marketing-mix activities. The models usually fail to recognize that the levels of the marketing-mix variables are often chosen with at least partial knowledge of the response parameters in the conditional model. This means that contrary to standard assumptions, the marginal distribution of the marketing-mix variables is not independent of response parameters. The authors expand on the standard conditional model to include a model for the determination of the marketing-mix variables. They apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. They do not assume a priori that details are set optimally, but instead they infer the extent to which sales force managers have knowledge of responsiveness, and they use this knowledge to set the level of sales force contact. The authors find that their modeling approach improves the precision of the physician-specific response parameters significantly. They also find that physicians are not detailed optimally; high-volume physicians are detailed to a greater extent than low-volume physicians without regard to responsiveness to detailing. It appears that unresponsive but high-volume physicians are detailed the most. Finally, the authors illustrate how their approach provides a general framework.

M any firms operate customer communities online. This is motivated by the belief that customers who join the community become more engaged with the firm and/or its products, and as a result, increase their economic activity with the firm. We describe this potential economic benefit as "social dollars." This paper contributes evidence for the existence and source of social dollars using data from a multichannel entertainment products retailer that launched a customer community online. We find a significant increase in customer expenditures attributable to customers joining the firm's community. While self-selection is a concern with field data, we rule out multiple alternative explanations. Social dollars persist over the time period observed and arose primarily in the online channel. To assess the source of the social dollar, we hypothesize and test whether it is moderated by participation behaviors conceptually linked to common attributes of customer communities. Our results reveal that posters (versus lurkers) of community content and those with more (versus fewer) social ties in the community generated more (fewer) social dollars. We found a null effect for our measure of the informational advantage expected to accrue to products that differentially benefit from content posted by like-minded community members. This overall pattern of results suggests a stronger social than informational source of economic benefits for firm operators of customer communities. Several implications for firms considering investments in and/or managing online customer communities are discussed.