Search

This study addresses the following question: For a given managerial, firm, and industry setting, which individual metrics are effective for making marketing-mix decisions that improve perceived performance outcomes? We articulate the key managerial takeaways based on testing a multi-stage behavioral framework that links decision context, metrics selection, and performance outcomes. Our statistical model adjusts for potential endogeneity bias in estimating metric effectiveness due to selection effects and differs from past literature in that managers can strategically choose metrics based on their ex-ante expected effectiveness. The key findings of our analysis of 439 managers making 1287 decisions are that customer-mindset marketing metrics such as awareness and willingness to recommend are the most effective metrics for managers to employ while financial metrics such as target volume and net present value are the least effective. However, relative to financial metrics, managers are more uncertain about the ex-ante effectiveness of customer-mindset marketing metrics, which attenuates their use. A second study on 142 managers helps provide detailed underlying rationale for these key results. The implications of metric effectiveness for dashboards and automated decision systems based on machine learning systems are discussed.

We propose a theory-based model of the shopper journey, incorporating the rich literature in consumer and marketing research and taking into account the evolving retailing landscape characterized by significant knowledge, lifestyle, technological, and structural changes. With consumer well-being at its core and shopper needs and motivations as the focus, our needs-adaptive shopper journey model complements and contrasts with existing models. In addition, we identify 12 shopper journey archetypes representing the paths that consumers commonly follow—archetypes that illustrate the workings and applications of our model. We discuss the nature of these archetypes, their relationships with one another, and the psychological states that consumers may experience on these shopper journeys. We also present exploratory empirical studies assessing the component states in the archetypes and mapping the archetypes onto dimensions of shopping motivations. Finally, we lay out a research agenda to help increase understanding of shopper behavior in the evolving retailing landscape.

© 2016 New York University This research extends information display board methods, currently employed to study information processing patterns in laboratory settings, to a field based setting that also yields managerially useful estimates of market preferences. A new model is proposed based on statistical, behavioral, and economic theories, which integrates three decisions consumers must make in this context: which product-attribute to inspect next, when to stop processing, and which, if any, product to purchase. Several theoretical options are considered on how to model product attribute selection and how to treat uninspected attributes. The modeling options are empirically tested employing datasets collected at a popular e-tailer's website, while customers were making product evaluation and purchase decisions. Subsequent to identifying the best model, we show how the resulting attribute preference estimates can be managerially employed to improve customer targeting of abandoned shopping carts for follow up communications aimed at improving sales conversions.

The recent surge in the importance of shopper marketing has led to an increased need to understand the drivers of unplanned purchases. The authors address this issue by examining how elements of the current shopping trip (e.g., lagged unplanned purchase, cumulative purchases) and previous shopping trips (e.g., average historical price paid by the shopper) determine unplanned versus planned purchases on the current trip. Using a grocery field study and frequent-shopper-program data, the authors estimate competing models to test behavioral hypotheses using a hierarchical Bayesian probit model with state dependence and serially correlated errors. The results indicate that shoppers with smaller trip budgets tend to exhibit behavior consistent with a self-regulation model (i.e., an unplanned purchase decreases the probability of a subsequent unplanned vs. planned purchase), but this effect reverses later in the trip. In contrast, shoppers with medium-sized trip budgets tend to exhibit behavior consistent with a cuing theory model (i.e., an unplanned purchase increases the probability of a subsequent unplanned vs. planned purchase), and this effect increases as the trip continues. The article concludes with a discussion of implications for research and practice.

We propose a theory-based model of the shopper journey, incorporating the rich literature in consumer research and marketing research. We also take into account the evolving retailing landscape, characterized by significant knowledge, lifestyle, technological, and structural changes. With consumer well-being at its core and shopper needs and motivations as the focus, our adaptive shopper journey model contrasts with other existing shopping journey models. After developing our model, we use it to identify 12 shopper journey archetypes representing the paths that consumers commonly follow – archetypes that illustrate the workings and potential applications of the needs-adaptive shopper journey model. We discuss the nature of these archetypes, their relationships with one another, and the psychological states that consumers may experience in these different shopper journeys. We also present exploratory empirical studies assessing the stages of the archetypes and mapping the archetypes onto dimensions of shopping motivations (e.g., buying versus browsing, goal-oriented versus non-goal oriented). Finally, drawing on our proposed needs-adaptive shopper journey model and shopper journey archetypes, we lay out a research agenda to help increase the understanding of shopper behavior in the evolving retailing landscape.

Marketing expenditures in the form of pricing, product development, promotion, and channel development are made to maximize profits. A challenge in evaluating the effectiveness of these expenditures is that decisions such as whether to lower prices or run promotions are made based on managers' knowledge of how sensitive consumers are to these marketing activities. Although marketing control variables are explanatory of sales, they are often set in anticipation of a market response, which reflects strategic behavior on the part of a firm. A challenge in developing a model of strategic behavior is that the process by which marketing expenditures are made is often not directly observable. We propose tests for comparing supply-side model formulations in which input variables are strategically determined. In these models, the joint likelihood of demand (y) and supply (x) can be factored into a conditional factor of demand given supply and into a marginal factor of supply. We illustrate our approach using data from a services company that operates in multiple geographic regions.

Analysts fitting a hierarchical Bayesian model must specify the distribution of heterogeneity. There are several distributions to choose from, including the multivariate normal, mixture of normals, Dirichlet processes priors, and so forth. Although significant progress has been made, estimating the models and obtaining measures for model selection remain ongoing areas of research for more flexible distributions of heterogeneity. As a result, the multivariate normal remains the default choice for many researchers and software packages. This article proposes model-checking statistics that signal the adequacy of the multivariate normal assumption for the distribution of heterogeneity; these methods do not require the analyst to fit alternative models. The authors use posterior predictive model checking to determine whether a discrepancy exists between the individual-level parameters and those implied by the assumed distribution of heterogeneity. In simulated and real data sets, the results show that these statistics are useful for identifying when the multivariate normal distribution is adequate, when there is a departure in the tails of the distribution, and when a multimodal distribution of heterogeneity may be more appropriate.

This research examines how school administrators can motivate children to make more healthful food choices using incentives, pledges, and competitions as interventions. A six-month field study was conducted across 55 elementary and middle schools, and the authors analyzed the data using a two-level Bayesian hierarchical linear model. All three interventions increased the choice of fruits and vegetables (the proportion of children choosing additional servings increased 3 to 24 percentage points) ten weeks after the interventions ended. However, younger (Grades 1 and 2) and older (Grades 3–8) children responded differently to the interventions. Although both younger and older children responded more favorably to the competition intervention than to the pledge or incentive interventions, the effects of the competition and incentive interventions were more pronounced among the younger children. A second field study, also with schoolchildren, examined the role of pledge reminders on adherence to the pledge. The presence of a visible reminder of a pledge resulted in significantly better outcomes than no reminder of a pledge.

Choice-based conjoint analysis is a popular marketing research technique to learn about consumers' preferences and to make market share forecasts under various scenarios for product offerings. Managers expect these forecasts to be “realistic” in terms of being able to replicate market shares at some prespecified or “base-case” scenario. Frequently, there is a discrepancy between the recovered and base-case market share. This paper presents a Bayesian decision theoretic approach to incorporating base-case market shares into conjoint analysis via the loss function. Because defining the base-case scenario typically involves a variety of management decisions, we treat the market shares as constraints on what are acceptable answers, as opposed to informative prior information. Our approach seeks to minimize the adjustment of parameters by using additive factors from a normal distribution centered at 0, with a variance as small as possible, but such that the market share constraints are satisfied. We specify an appropriate loss function, and all estimates are formally derived via minimizing the posterior expected loss. We detail algorithms that provide posterior distributions of constrained and unconstrained parameters and quantities of interest. The methods are demonstrated using discrete choice models with simulated data and data from a commercial market research study. These studies indicate that the method recovers base-case market shares without systematically distorting the preference structure from the conjoint experiment.

In mixed price bundling, the consumer has the choice of buying the individual products separately, as part of a bundle with a discounted price, or not purchasing them at all. Framing effects refer to how the price of the bundle is presented to the consumer. Past studies have focused on perceptual measures and aggregate level results, and have only looked at a subset of different types of price framing in any one study. In this paper we use discrete choice data to investigate whether price framing affects choice in mixed price bundles. We find that the joint, integrated frame results in the highest proportion of respondents choosing the bundle and the fewest choosing “none.” When the prices of items in a bundle are itemized, some consumers are more likely to compare prices separately to their reference prices to evaluate the attractiveness of the deal, but this actually reduces the probability of purchasing the bundle. However, the majority of consumers do not use reference prices and instead follow a simple economic choice model.

Preference heterogeneity is a major research stream in marketing aimed at quantifying and understanding the diversity of demand for product attributes and attribute levels. In experimental settings, in which consumers are presented with simple descriptions of product offerings, continuous distributions of heterogeneity, such as the multivariate normal, provide a useful representation of preference. However, in more complex cases in which respondents have value for only a few of the benefits associated with an offering or cognitive constraints that result in selective attention to a subset of the information available, continuous distributions of heterogeneity do not reflect the possibility that a subset of the variables has nonzero effect sizes for different respondents. Identifying which attributes are used in a brand choice decision is closely related to the statistical procedure of variable selection. This article extends variable selection methods to accommodate heterogeneity across consumers and data contexts, conditions frequently encountered in marketing studies. The authors apply the methods to a discrete-choice conjoint study in which data are collected in both full-profile and partial-profile formats.

Consumer choice in surveys and in the marketplace reflects a complex process of screening and evaluating choice alternatives. Behavioral and economic models of choice processes are difficult to estimate when using stated and revealed preferences because the underlying process is latent. This paper introduces Bayesian methods for estimating two behavioral models that eliminate alternatives using specific attribute levels. The elimination by aspects theory postulates a sequential elimination of alternatives by attribute levels until a single one, the chosen alternative, remains. In the economic screening rule model, respondents screen out alternatives with certain attribute levels and then choose from the remaining alternatives, using a compensatory function of all the attributes. The economic screening rule model gives an economic justification as to why certain attributes are used to screen alternatives. A commercial conjoint study is used to illustrate the methods and assess their performance. In this data set, the economic screening rule model outperforms the EBA and other standard choice models and provides comparable results to an equivalent conjunctive screening rule model.

The emergence of Bayesian methodology has facilitated respondent-level conjoint models, and deriving utilities from choice experiments has become very popular among those modeling product line decisions or new product introductions. This review begins with a paradox of why experimental choices should mirror market behavior despite clear differences in content, structure and motivation. It then addresses ways to design the choice tasks so that they are more likely to reflect market choices. Finally, it examines ways to model the results of the choice experiments to better mirror both underlying decision processes and potential market choices.

Responses to questions in a survey can reflect a behavior process that influences multiple response items. Respondent ratings of brand attributes, for example, can be affected by past purchases by making a brand more salient, or by respondents attributing higher performance to justify their purchases. When multiple response items are influenced by a common underlying process, there is simultaneity in the data. This paper proposes an approach to model the simultaneity in different survey responses by using common parameters and structural relationships motivated by behavioral theories on how consumers respond to surveys. Specifically, the proposed models show how brand usage and attribute perception responses are jointly determined by justification, order, and brand halo effects in two brand positioning studies. We detect a significant tendency for respondents to inflate their reported beliefs for particular brands as well as the selected brand across five countries in an international survey as well as in a domestic study.

Many theories of consumer behavior involve thresholds and discontinuities. In this paper, we investigate consumers' use of screening rules as part of a discrete-choice model. Alternatives that pass the screen are evaluated in a manner consistent with random utility theory; alternatives that do not pass the screen have a zero probability of being chosen. The proposed model accommodates conjunctive, disjunctive, and compensatory screening rules. We estimate a model that reflects a discontinuous decision process by employing the Bayesian technique of data augmentation and using Markov-chain Monte Carlo methods to integrate over the parameter space. The approach has minimal information requirements and can handle a large number of choice alternatives. The method is illustrated using a conjoint study of cameras. The results indicate that 92% of respondents screen alternatives on one or more attributes.

The recent surge in the importance of shopper marketing has led to an increased need to understand the drivers of unplanned purchases. This research addresses this issue by examining how elements of the current shopping trip (e.g., lagged unplanned purchase and cumulative purchases) and past purchases (e.g., average historical price paid by the shopper) determine unplanned versus planned purchases on the current trip. Using a grocery field study and frequent shopper data, we estimate competing models to test behavioral hypotheses using a hierarchical-Bayesian probit model with state dependence and serially correlated errors. Our results indicate that shoppers with smaller trip budgets tend to exhibit behavior consistent with a self-regulation model – an unplanned purchase decreases the probability of a subsequent unplanned purchase – but this effect reverses later in the trip. In contrast, shoppers with medium trip budgets tend to exhibit behavior consistent with a cueing theory model – an unplanned purchase increases the probability of a subsequent unplanned purchase – and this effect increases as the trip wears on. Further, factors from previous shopping trips predict unplanned purchases in the current trip, suggesting that retailers can use their frequent shopper program data to create customized shopping lists and improve the targeting of mobile app-based promotions.