Showing papers in "Marketing Science in 2014"

The Service Revolution and the Transformation of Marketing Science

Abstract: The nature of marketing science is changing in a systematic, predictable, and irrevocable way. As information technology enables ubiquitous customer communication and big customer data, the fundamental nature of the firm's connection to the customer changes: better, more personalized service can be offered, from which service relationships are deepened, and consequently, more profitable customers grow the influence of service within the goods sector and expand the service sector in the economy. Marketing is becoming more personalized, and marketing science techniques that exploit customer heterogeneity are becoming more important. Information technology improvements also guarantee the increasing importance and usage of computationally intensive data processing and “big data.” Most importantly, these trends have already lasted for more than a century, and they will become even more pronounced in the coming years as a result of the monotonic nature of technology improvement. These changes imply a transformation of marketing science in both the topics to be emphasized and the methods to be employed. Increasingly, and inevitably, all of marketing will come to resemble to a greater degree the formerly specialized area of service marketing, only with an increased emphasis on marketing analytics.

Invited Paper-Even the Rich Can Make Themselves Poor: A Critical Examination of IV Methods in Marketing Applications

Abstract: 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.

Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance

Abstract: We study the relative importance of online word of mouth and advertising on firm performance over time since product introduction. The current research separates the volume of consumer-generated online word of mouth OWOM from its valence, which has three dimensions---attribute, emotion, and recommendation oriented. Firm-initiated advertising content is also classified as attribute or emotion advertising. We also shed light on the role played by advertising content on generating the different types of OWOM conversations. We use a dynamic hierarchical linear model DHLM for our analysis. The proposed model is compared with a dynamic linear model, vector autoregressive/system of equations model, and a generalized Bass model. Our estimation accounts for potential endogeneity in the key measures. Among the different OWOM measures, only the valence of recommendation OWOM is found to have a direct impact on sales; i.e., not all OWOM is the same. This impact increases over time. In contrast, the impact of attribute advertising and emotion advertising decreases over time. Also, consistent with prior research, we observe that rational messages i.e., attribute-oriented advertising wears out a bit faster than emotion-oriented advertising. Moreover, the volume of OWOM does not have a significant impact on sales. This suggests that, in our data, “what people say” is more important than “how much people say.” Next, we find that recommendation OWOM valence is driven primarily by the valence of attribute OWOM when the product is new and driven by the valence of emotion OWOM when the product is more mature. Our brand-level results help us classify brands as consumer driven or firm driven, depending on the relative importance of the OWOM and advertising measures, respectively.

Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans

Abstract: We estimate a dynamic structural model of sales force response to a bonus-based compensation plan. This paper provides substantive insight into how different elements of the compensation plan enhance productivity. We find evidence that 1 bonuses enhance productivity across all segments; 2 overachievement commissions help sustain the high productivity of the best performers, even after attaining quotas; and 3 quarterly bonuses help improve performance of the weak performers by serving as pacers to keep the sales force on track in achieving its annual sales quotas. The paper also introduces two main methodological innovations to the marketing literature: First, we implement empirically the method proposed by Arcidiacono and Miller [Arcidiacono P, Miller RA 2011 Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity. Econometrica 796:1823--1867] to accommodate unobserved latent-class heterogeneity using a computationally light two-step estimator. Second, we illustrate how discount factors can be estimated in a dynamic structural model using field data through a combination of 1 an exclusion restriction separating current and future payoff and 2 a finite-horizon model in which there is no forward-looking behavior in the last period.

Manufacturer and Retailer Strategies to Impact Store Brand Share: Global Integration, Local Adaptation, and Worldwide Learning

Abstract: Although store brands SBs are becoming increasingly important across the world, their success varies dramatically across consumer packaged goods categories and countries. The purpose of this paper is to provide insight into how such differences in SB success originate. Using a unique data set that combines scanner data for a three-to five-year period with consumer survey data n = 20,987 for scores of food, household care, and personal care categories from 23 countries around the world, we identify cross-national regularities as to the role of nine manufacturer and retailer factors in explaining SB market share. For each manufacturer and retailer factor, we determine whether it can be part of a global integration strategy, whether it can be included in a local adaptation strategy, or whether it is a candidate for worldwide learning. Our findings have important implications for national brand manufacturers and retailers.

Predicting Individual Behavior with Social Networks

Abstract: With the availability of social network data, it has become possible to relate the behavior of individuals to that of their acquaintances on a large scale. Although the similarity of connected individuals is well established, it is unclear whether behavioral predictions based on social data are more accurate than those arising from current marketing practices. We employ a communications network of over 100 million people to forecast highly diverse behaviors, from patronizing an off-line department store to responding to advertising to joining a recreational league. Across all domains, we find that social data are informative in identifying individuals who are most likely to undertake various actions, and moreover, such data improve on both demographic and behavioral models. There are, however, limits to the utility of social data.In particular, when rich transactional data were available, social data did little to improve prediction.

Consumer Attitude Metrics for Guiding Marketing Mix Decisions

Abstract: Marketing managers often use consumer attitude metrics such as awareness, consideration, and preference as performance indicators because they represent their brand's health and are readily connected to marketing activity. However, this does not mean that financially focused executives know how such metrics translate into sales performance, which would allow them to make beneficial marketing mix decisions. We propose four criteria---potential, responsiveness, stickiness, and sales conversion---that determine the connection between marketing actions, attitudinal metrics, and sales outcomes. We test our approach with a rich data set of four-weekly marketing actions, attitude metrics, and sales for several consumer brands in four categories over a seven-year period. The results quantify how marketing actions affect sales performance through their differential impact on attitudinal metrics, as captured by our proposed criteria. We find that marketing--attitude and attitude--sales relationships are predominantly stable over time but differ substantially across brands and product categories. We also establish that combining marketing and attitudinal metrics criteria improves the prediction of brand sales performance, often substantially so. Based on these insights, we provide specific recommendations on improving the marketing mix for different brands, and we validate them in a holdout sample. For managers and researchers alike, our criteria offer a verifiable explanation for differences in marketing elasticities and an actionable connection between marketing and financial performance metrics.

Competitive Poaching in Sponsored Search Advertising and Its Strategic Impact on Traditional Advertising

Abstract: Traditional advertising, such as TV and print advertising, primarily builds awareness of a firm's product among consumers, whereas sponsored search advertising on a search engine can target consumers closer to making a purchase because they reveal their interest by searching for a relevant keyword. Increased consumer targetability in sponsored search advertising induces a firm to “poach” a competing firm's consumers by directly advertising on the competing firm's keywords; in other words, the poaching firm tries to obtain more than its “fair share” of sales through sponsored search advertising by free riding on the market created by the firm being poached. Using a game theory model with firms of different advertising budgets, we study the phenomenon of poaching, its impact on how firms allocate their advertising budgets to traditional and sponsored search advertising, and the search engine's policy on poaching. We find that, as budget asymmetry increases, the smaller-budget firm poaches more on the keywords of the larger-budget firm. This may induce the larger-budget firm to allocate more of its budget to traditional advertising, which, in turn, hurts the search engine's advertising revenues. Therefore, paradoxically, even though poaching increases competition in sponsored search advertising, the search engine can benefit from limiting the extent of poaching. This explains why major search engines use “ad relevance” measures to handicap poaching on trademarked keywords.

The Company That You Keep: When to Buy a Competitor's Keyword

Abstract: In search advertising, brand names are often purchased as keywords by the brand owner or a competitor. We aim to understand the strategic benefits and costs of a firm buying its own brand name or a competitor's brand name as a keyword. We model the effect of search advertising to depend on the presence or absence of a competitor's advertisement on the same results page. We find that the quality difference between the brand owner and the competitor moderates the purchase decision of both firms. Interestingly, in some cases, a firm may buy its own brand name only to defend itself from the competitor's threat. It is also possible that the brand owner, by buying its own branded keyword, precludes the competitor from buying the same keyword. Our result also implies that the practice of bidding on the competitor's brand name creates a prisoner's dilemma, and thus both firms may be worse off, but the search engine captures the lost profits. We also discuss the difference in our results when the search is for a generic keyword instead of a branded keyword. Finally, we find some empirical support for our theory from the observation of actual purchase patterns on Google AdWords.

Dynamic Targeted Pricing in B2B Relationships

Abstract: We model the multifaceted impact of pricing decisions in business-to-business B2B relationships that are governed by trust. We show how a seller can develop optimal intertemporal targeted pricing strategies to maximize profits over time while taking into consideration the impact of pricing decisions on short-term profit margin, reference price formation, and long-term relationships. Our modeling framework uses a hierarchical Bayesian approach to weave together a multivariate nonhomogeneous hidden Markov model, buyer heterogeneity, and control functions to facilitate targeting, capture the evolution of trust, and control for price endogeneity. We estimate our model on longitudinal transactions data from a retailer in the industrial consumables domain. We find that buyers in our data set can be best represented by two latent states of trust toward the seller---a “vigilant” state that is characterized by heightened price sensitivity and a cautious approach to ordering and a “relaxed” state with purchase behaviors that are consistent with high relational trust. The seller's pricing decisions can transition buyers between these two states. An optimal dynamic and targeted pricing strategy based on our model suggests a 52% improvement in profitability compared with the status quo. Furthermore, a counterfactual analysis examines the seller's optimal pricing policy under fluctuating commodity prices.

Morphing Banner Advertising

Abstract: Researchers and practitioners devote substantial effort to targeting banner advertisements to consumers, but they focus less effort on how to communicate with consumers once targeted. Morphing enables a website to learn, automatically and near optimally, which banner advertisements to serve to consumers to maximize click-through rates, brand consideration, and purchase likelihood. Banners are matched to consumers based on posterior probabilities of latent segment membership, which are identified from consumers' clickstreams. This paper describes the first large-sample random-assignment field test of banner morphing---more than 100,000 consumers viewed more than 450,000 banners on CNET.com. On relevant Web pages, CNET's click-through rates almost doubled relative to control banners. We supplement the CNET field test with an experiment on an automotive information-and-recommendation website. The automotive experiment replaces automated learning with a longitudinal design that implements morph-to-segment matching. Banners matched to cognitive styles, as well as the stage of the consumer's buying process and body-type preference, significantly increase click-through rates, brand consideration, and purchase likelihood relative to a control. The CNET field test and automotive experiment demonstrate that matching banners to cognitive-style segments is feasible and provides significant benefits above and beyond traditional targeting. Improved banner effectiveness has strategic implications for allocations of budgets among media.

Prerelease Buzz Evolution Patterns and New Product Performance

Abstract: This study examines the dynamics of online buzz over time before product release. Employing functional data analysis, we treat the curve of prerelease buzz evolution trajectory as the unit of analysis and find that the shape of the curve significantly adds power in predicting new product performance compared with using product characteristics and firm advertising alone. Moreover, daily prerelease buzz evolution data enable accurate sales forecasting long before product release, which allows sufficient time for managers to adjust product design and/or marketing strategy. For example, the forecasting accuracy using an early buzz evolution curve ending on the 61st day before product release is not only higher than that using accumulated buzz volume until then but also higher than that using the total volume of all buzz up until product release. Beyond the sales outcome, we find that prerelease buzz is quickly reflected in firm stock returns before product release and reduces the absolute amount of postrelease stock price correction. The model accounts for endogeneity, and the results are robust after controlling for buzz sentiment. We also explore the factors influencing prerelease buzz evolution patterns, thus generating insights into how to manage prerelease buzz dynamics to enhance new product performance.

Nonmonotonic Status Effects in New Product Adoption

Abstract: We investigate how the tendency to adopt a new product independently of social influence, the recipients' susceptibility to such influence, and the sources' strength of influence vary with social status. Leveraging insights from social psychology and sociology about middle-status anxiety and conformity, we propose that for products that potential adopters expect to boost their status, both the tendency to adopt independently from others and the susceptibility to contagion is higher for middle-status than for low-and high-status customers. Applying a nested case-control design to the adoption of commercial kits used in genetic engineering, we find evidence that status affects i how early or late one adopts regardless of social influence, ii how susceptible one is to such influence operating through social ties, and iii how influential one's own behavior is in triggering adoption by others. The inverse-U patterns in i and ii are consistent with middle-status anxiety and conformity. The findings have implications for how to use status to better understand adoption and contagion mechanisms, and for targeting customers when launching new products.

Signaling Through Pricing by Service Providers with Social Preferences

Abstract: In many service markets such as consulting, auto repair, financial planning, and healthcare, the service provider may have more information about the customer's problem than the customer, and different customers may impose different costs on the service provider. In principle, the service provider should ethically care about the customer's welfare, but it is possible that a provider may maximize only its own profit. Moreover, the customer may not know ex ante whether the provider is ethical or purely self-interested. We develop a game-theoretic model to investigate pricing strategies and the market outcome in service markets where the provider has two-dimensional private information about her own type whether ethical or self-interested and about the customer's condition whether serious or minor. We show that in a less ethical market, a self-interested provider will charge different prices based on the customer's condition, whereas an ethical provider will charge the same price for both conditions. In contrast, in a more ethical market, both the self-interested and the ethical provider will charge the same uniform price to both types of customers. Interestingly, both market efficiency and the customer's ex ante expected surplus might be lower in a more ethical market than in a less ethical one.

Sell Probabilistic Goods? A Behavioral Explanation for Opaque Selling

Abstract: Probabilistic or opaque selling, whereby a seller hides the exact identity of a product until after the buyer makes a payment, has been used in practice and received considerable attention in the literature. Under what conditions, and why, is probabilistic selling attractive to firms? The extant literature has offered the following explanations: to price discriminate heterogeneous consumers, to reduce supply-demand mismatches, and to soften price competition. In this paper, we provide a new explanation: to exploit consumer bounded rationality in the sense of anecdotal reasoning. We build a simple model where the firm is a monopoly, consumers are homogeneous, and there is no demand uncertainty or capacity constraint. This model allows us to isolate the impact of consumer bounded rationality on the adoption of opaque selling. We find that although it is never optimal to use opaque selling when consumers have rational expectations, it can be optimal when consumers are boundedly rational. We show that opaque selling may soften price competition and increase the industry profits as a result of consumer bounded rationality. Our findings underscore the importance of consumer bounded rationality and show that opaque selling might be even more attractive than previously thought.

Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth

Abstract: We study how peers impact worker productivity growth among salespeople in the cosmetics department of a department store. We first exploit a shift assignment policy that creates exogenous variation in salespersons' peers each week to identify and quantify sources of worker learning. We find that peer-based learning is more important than learning-by-doing for individuals, and there is no evidence of forgetting. Working with high-ability peers substantially increases the long-term productivity growth of new salespeople. We then examine possible mechanisms behind peer-based learning by exploiting the multiple colocated firms in our setting that sell products with different task difficulties and compensate their sales forces using either team-based or individual-based compensation systems. The variation in incentives to compete and cooperate within and across firm boundaries, combined with variation in sales difficulty for different product classes, allows us to suggest two mechanisms behind peer-based learning: observing successful sales techniques of peers and direct teaching. Our paper advocates the importance of learning from one another in the workplace and suggests that individual peer-based learning is a foundation of both organizational learning curves and knowledge spillovers across firms.

Make, Buy, or Ally? Choice of and Payoff from Announcements of Alternate Strategies for Innovations

Abstract: Firms constantly grapple with the question of whether to make, buy, or ally for innovations. The literature has not, to our knowledge, analyzed the choice of and payoff from these alternate routes to innovation for the same firm. To address this issue, we collect, code, and analyze the choice of and payoff from 3,522 announcements of make, buy, and ally for 192 firms across 108 industries over five years. We find that announcements to make or ally generate positive and higher payoffs than announcements to buy, which generate negative payoffs. Nevertheless, firms continue to buy for two reasons. First, firms seem to have no memory of the payoff from buy, even though they have a memory of the payoff from make. Second, firms tend to buy when they lack commercializations, even though this strategy does not always seem to pay off. These results suggest that firms see buy as a signal to investors that they have a solution for what may be a deep strategic problem. Nevertheless, the negative returns to a buy can be mitigated if the acquirer is experienced, and the target is related and offers high customer benefit. We offer explanations for and implications of the results.

Why, When, and How Much to Entertain Consumers in Advertisements? A Web-Based Facial Tracking Field Study

Abstract: The presence of positive entertainment e.g., visual imagery, upbeat music, humor in TV advertisements can make them more attractive and persuasive. However, little is known about the downside of too much entertainment. This research focuses on why, when, and how much to entertain consumers in TV advertisements. We collected data in a large scale field study using 82 ads with various levels of entertainment shown to 178 consumers in their homes and workplaces. Using a novel web-based face tracking system, we continuously measure consumers' smile responses, viewing interest, and purchase intent. A simultaneous Bayesian hierarchical model is estimated to assess how different levels of entertainment affect purchases by endogenizing viewing interest. We find that entertainment has an inverted U-shape relationship to purchase intent. Importantly, we separate entertainment into that which comes before the brand versus that which comes after, and find that the latter is positively associated with purchase intent while the former is not.

Strategic Loyalty Reward in Dynamic Price Discrimination

Abstract: In a dynamic model with overlapping generations of consumers, we study duopolistic competition when firms can price discriminate, at each period, between their previous customers and the consumers that they have never served. Long-term contracts are not enforceable. In Markov-perfect equilibrium, one firm charges higher prices to its past customers than to its new customers, as past customers have revealed their strong preferences for the firm; the other firm, however, rewards its previous customers by charging lower prices to them than to its new customers. This loyalty reward strategy comes from the interplay between the firms' usual incentive to extract surplus from consumers with revealed strong preferences and their incentives to acquire information and to recognize their young loyal customers. The result also relies on the firms' inability a priori to tell different generations apart. It is the outcome of the unique equilibrium of a simplified two-period or T-period version of the game and holds with forward-looking consumers who are impatient enough.

Partner Selection in Brand Alliances: An Empirical Investigation of the Drivers of Brand Fit

Abstract: We investigate whether partners in a brand alliance should be similar or dissimilar in brand image to foster favorable perceptions of brand fit. Using a Bayesian nonlinear structural equation model and evaluations of 1,200 brand alliances, we find that the conceptual coherence in brand personality profiles predicts attitudes towards a brand alliance. More specifically, we find that similarity in Sophistication and Ruggedness and moderate dissimilarity in Sincerity and Competence result in more favorable brand alliance evaluations. Overall, we find that similarity effects are more pronounced than dissimilarity effects. Implications for brand alliance strategies and marketing managers are discussed.

Just the Faces: Exploring the Effects of Facial Features in Print Advertising

Abstract: Human faces are used extensively in print advertisements. In prior literature, researchers have studied spokespersons in general, but few have studied faces explicitly. This paper aims to answer three questions that are important to both researchers and practitioners: 1 Do faces affect how a viewer reacts to an advertisement on the metrics that advertisers care about? 2 If faces do have an effect, is it large enough to warrant careful selection of faces when constructing print advertisements? 3 If faces do have an effect and the effect is large, what facial features elicit such differential reactions on these metrics, and are such reactions different across individuals and/or product categories? Relying on the eigenface method, a holistic approach widely used in the computer science field for face recognition, we conducted an empirical study to answer these three questions. The results show that different faces do have an effect on people's attitude toward the advertisement, attitude toward the brand, and purchase intention and that the effect is nontrivial. Multiple segments were identified and substantial differences were found among people's reactions to the faces in the ads across those segments. We also found that the effect of faces interacts with product categories and is mediated by various facial traits such as attractiveness, trustworthiness, and competence. Implications and directions for future research are discussed.

Persuasive Puffery

Abstract: Sellers often make claims about product strengths without providing evidence. Even though such claims are mere puffery, we show that they can be credible because talking up any one strength comes at the implicit trade-off of not talking up another potential strength. Puffery pulls in some buyers who value product attributes that are talked up or emphasized while pushing away other buyers who infer that the attributes they value are relative weaknesses. When the initial probability of making a sale is low, there are more potential buyers to pull in than to push away, so puffery is persuasive overall. This persuasiveness requires that buyers have some privacy about their preferences so that the seller does not completely pander to them. More generally, the results show how comparative cheap talk by an expert to a decision maker can be credible and persuasive in standard discrete choice models used throughout marketing, economics, and other disciplines.

McDonald's and KFC in China: Competitors or Companions?

Abstract: In this paper, we study the entry and expansion decisions of McDonald's and KFC in China using an originally assembled data set on the two chains' expansion in the China market from their initial entry up to year 2007. We analyze how the presence of a rival affects each firm's strategies. The results indicate that a rival's presence has a net positive effect on a chain's expansion decision. We focus on testing two possible explanations for a positive rival impact: market learning and demand expansion. First, we derive a set of theoretical predictions on how a chain's optimal expansion decision would react to its rival's expansion patterns when market learning versus demand expansion is the driving force of the rival's positive influence. The empirical analysis based on these predictions consistently suggests that market learning is more likely to explain the positive effect of KFC on McDonald's and that demand expansion is more plausible with McDonald's positive spillover on KFC. In other words, the results are consistent with the presence of KFC signaling market demand potential and growth to McDonald's and the presence of McDonald's helping to cultivate consumer taste and generate demand for Western fast food, which benefits KFC.

Assessing the Influence of Economic and Customer Experience Factors on Service Purchase Behaviors

Abstract: Past studies have overlooked the joint effects of economic and customer experience factors on service purchase behaviors. Furthermore, service firms tend to make substantial investments in enhancing customer experience, mitigating the negative effects of service failures through recovery efforts and increasing overall customer satisfaction. Yet, largely due to a paucity of data, we know little about how the state of the economy influences the way in which customers use past service experiences to make future purchase decisions. We hypothesize that the state of the economy moderates the effects of customer experience factors on customers' service purchase behaviors. In addition, we examine how personal income influences the degree to which the aggregate economy influences service purchase decisions. We test the proposed model using panel survey and transaction data from an international airline carrier. Our findings demonstrate that, contrary to wisdom in the popular press, customer experience matters more when the economy is doing better, not worse. Furthermore, lower income consumers are more sensitive to changes in the economy than higher income consumers. We validate the hypothesized model using a controlled experiment and establish that aggregate measures of the economy can be used to predict individual perceptions and purchase intentions.

A Data Set of Brands and Their Characteristics

Abstract: Brands stand at the core of marketing. They are central to positioning, marketing communications, word of mouth, customer relationships, and firm profits. Brands have been studied from multiple perspectives using a variety of measures and scales. We offer a data set that contains 136 different measures of the brand characteristics for almost 700 of the top U.S. national brands across 16 categories measured by 2010. These measures cover a broad range of characteristics including brand personality, satisfaction, age, attributes related to Rogers' innovation scheme such as complexity, and the four brand equity pillars of Young and Rubicam's BrandAsset Valuator. The data were collected from a combination of sources including an original survey on 4,769 subjects. In addition, we provide quarterly data on the variables available from the BrandAsset Valuator for two and a half years between 2008 and 2010. These data can be used as a building block in research that aims to explore the antecedents of brand perceptions or connect brand characteristics with market and financial outcomes. This paper describes the data and some relevant research questions. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2014.0861 .

Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data

Abstract: When managers and researchers encounter a data set, they typically ask two key questions: 1 Which model from a candidate set should I use? And 2 if I use a particular model, when is it going to likely work well for my business goal? This research addresses those two questions and provides a rule, ie, a decision tree, for data analysts to portend the “winning model” before having to fit any of them for longitudinal incidence data We characterize data sets based on managerially relevant and easy-to-compute summary statistics, and we use classification techniques from machine learning to provide a decision tree that recommends when to use which model By doing the “legwork” of obtaining this decision tree for model selection, we provide a time-saving tool to analysts We illustrate this method for a common marketing problem ie, forecasting repeat purchasing incidence for a cohort of new customers and demonstrate the method's ability to discriminate among an integrated family of a hidden Markov model HMM and its constrained variants We observe a strong ability for data set characteristics to guide the choice of the most appropriate model, and we observe that some model features eg, the “back-and-forth” migration between latent states are more important to accommodate than are others eg, the inclusion of an “off” state with no activity We also demonstrate the method's broad potential by providing a general “recipe” for researchers to replicate this kind of model classification task in other managerial contexts outside of repeat purchasing incidence data and the HMM framework

Usage Experience with Decision Aids and Evolution of Online Purchase Behavior

Abstract: This study investigates how prior usage experience with various decision aids available in an Internet shopping environment contributes to online purchase behavior evolution. Four types of decision aids are examined: those for 1 nutritional needs, 2 brand preference, 3 economic needs, and 4 personalized shopping lists. We construct and estimate nonhomogeneous hidden Markov models of store-and category-level purchase decisions, in which parameters vary over time across hidden states as driven by usage experience with different decision aids. We find that consumers evolve through distinct behavioral states over time, and the evolution is attributable to their prior usage experience with various decision aids. Moreover, the impact varies by the specific decision aid, behavioral state, and category characteristics. In addition, consumers gravitate toward habitual decision processes in online grocery stores, and their average price and promotion sensitivities increase first and then decrease but the level of heterogeneity rises continuously. We identify beneficial versus potentially undesirable decision aids and demonstrate how the proposed research method can help online retailers improve their store environments, design customized promotions, and quantify the payoffs of these strategies.

Modeling Indivisible Demand

Abstract: Disaggregate demand in the marketplace exists on a grid determined by the package sizes offered by manufacturers and retailers. Although consumers may want to purchase a continuous-valued amount of a product, realized purchases are constrained by available packages. This constraint might not be problematic for high-volume demand, but it is potentially troubling when demand is small. Despite the prevalence of packaging constraints on choice, economic models of choice have been slow to deal with their effects on parameter estimates and policy implications. In this paper we propose a general framework for dealing with indivisible demand in economic models of choice, and we show how to estimate model parameters using Bayesian methods. Analyses of simulated data and a scanner-panel data set of yogurt purchases indicate that ignoring packaging constraints can bias parameter estimates and measures of model fit, which results in the inaccurate measures of metrics such as price elasticity and compensating value. We also show that a portion of nonpurchase in the data e.g., 2.27% for Yoplait Original reflects the restriction of indivisibility, not the lack of preference. The importance of demand indivisibility is also highlighted by the counterfactual study where the removal of the smallest package size i.e., 4 oz mainly results in nonpurchase in the yogurt category instead of switching to larger package sizes.

Why Do Salespeople Spend So Much Time Lobbying for Low Prices

Abstract: In business-to-business settings a company's sales force often spends considerable time lobbying internally for authorization to charge lower prices. These internal lobbying activities are time consuming, and divert attention from other tasks, such as interacting with customers. We explain why internal lobbying activities serve an important role. They help the firm elicit truthful reporting of demand information from the sales force. As a result, it may be profitable for the firm to require lobbying and make the requirement onerous, even though lobbying is a nonproductive activity that creates an additional administrative burden and imposes a deadweight loss.

Competitor Orientation and the Evolution of Business Markets

Abstract: Competitor orientation, i.e., the focus on beating the competition rather than maximizing profits, seems to thrive in business situations despite being, by definition, suboptimal for profit-maximizing firms. Our research explains how a competitor orientation can persist and even thrive in equilibrium in markets that reward only profits. We apply evolutionary game theory to business markets where reputation matters. We use three games that represent classic interactions in business marketing: Chicken to illustrate competition for product adoption, the Battle of the Sexes channel negotiations, and the Prisoners' Dilemma pricing battles. Initial populations are assumed to have both profit-maximizing managers and competitor-oriented managers i.e., those who gain additional utility from beating others. We demonstrate that a competitor orientation can survive in equilibrium despite selection that is based solely on profits. Using Chicken, we show that a competitor orientation thrives and can even overrun the population. We use the Battle of the Sexes to show that a competitor orientation will overrun one population in a two-sided negotiation e.g., all retailers in a retailer/manufacturer dyad. Last, using the Prisoners' Dilemma, we show that competitor orientation is not selected against. We conclude that evolutionary profit-driven selection pressures cannot be assumed to eliminate nonprofit-maximizing behavior even when selection is based purely on profitability.