Conveniencia de marcas propias: Motivos de compra de marcas propias en productos de compra frecuente que son ofertados en establecimientos comerciales (supéreles y descuentos).

La oferta y demanda de marcas propias es un fenómeno del marketing de progresivo desarrollo en los últimos años. Desde tal punto de vista, existen diversos factores asociados a su comercialización desde el punto de vista del consumidor, los formatos de la distribución en donde predominan estas marcas, y la gestión que realizan las empresas para satisfacer las necesidades de los clientes. Desde tal punto de vista, la presente investigación tiene como principal objetivo analizar las marcas propias desde aquellos factores que motivan la compra de las mismas en establecimientos comerciales de conveniencia (supéreles y descuentos)

Should Optimal Designers Worry About Consideration?

Consideration set formation using non-compensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via non-compensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to the data, and then optimize design decisions using the estimated models. Model predictive power, design "error", and profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply better design decisions; that is, different model forms can provide "descriptive" rather than "predictive" information that is useful for design.Comment: 5 figures, 26 pages. In Press at ASME Journal of Mechanical Design (as of 3/17/15

Modeling and Measuring Scale Attraction Effects: An Application to Charitable Donations

Charities seeking donations typically employ an “appeals scale,” a roster of suggested amounts presented to potential donors, along with an “Other” category. Yet little is known about how the amounts comprising appeals scales affect whether a donation is made and, if so, jointly exert “pull” on its magnitude. Availing of multi-year panel data and a field experiment, we develop a model accounting for individual level donation incidence, amount, and appeals scale attraction effects. The model incorporates heterogeneity across donors in both upward and downward scale point attraction, as well as in donation patterns (e.g., seasonality), and accommodates multiple operationalizations of internal and external referents to summarize the effects of prior donation history and scale points, respectively. Overall results suggest that scale points do exert substantial attraction effects; that these vary markedly across donors; that they are in fact referent-based effects; that donors are more easily persuaded to give less than more; and that, while all scale points exert pull, influence wanes with distance. The modeling framework applies not only in donation contexts, but whenever an ordered categorical scale is used to collect data regarding an underlying latent response.https://deepblue.lib.umich.edu/bitstream/2027.42/142868/1/1380_Feinberg.pd

Multimodal and nested preference structures in choice-based conjoint analysis: a comparison of bayesian choice models with discrete and continuous representations of heterogeneity

Die Choice-Based Conjoint-Analyse (CBC) ist heutzutage die am weitesten verbreitete Variante der Conjoint-Analyse, einer Klasse von Verfahren zur Messung von Nachfragerpräferenzen. Der Hauptgrund für die zunehmende Dominanz des CBC-Ansatzes in jüngerer Zeit besteht darin, dass hier das tatsächliche Wahlverhalten von Nachfragern sehr realistisch nachgestellt werden kann, indem die Befragten wiederholt ihre bevorzugte Alternative aus einer Menge mehrerer Alternativen (Choice Sets) auswählen. Im Rahmen der CBC-Analyse ist das Multinomiale Logit- (MNL) Modell das am häufigsten verwendete diskrete Wahlmodell. Das MNL-Modell weist jedoch zwei wesentliche Einschränkungen auf: (a) Es impliziert proportionale Substitutionsmuster zwischen den Alternativen, was als Independence of Irrelevant Alternatives- (IIA) Eigenschaft bezeichnet wird, und (b) es berücksichtigt keine Nachfragerheterogenität, da per Definition Teilnutzenwerte für alle Konsumenten als homogen angenommen werden. Seit den 1990er-Jahren werden hierarchisch bayesianische (HB) Modelle für die Teilnutzenwertschätzung in der CBC-Analyse verwendet. Solche HB-Modelle ermöglichen zum einen eine Schätzung individueller Teilnutzenwerte, selbst bei einer beschränkten Datenlage, zum anderen können sie aufgrund der Modellierung von Heterogenität die IIA-Eigenschaft stark abmildern. Der Schwerpunkt der vorliegenden Thesis liegt auf der Verwendung von HB-Modellen mit unterschiedlichen Darstellungen von Nachfragerheterogenität (diskret vs. kontinuierlich) für CBC-Daten sowie außerdem auf einem speziellen HB-Modell, das die IIA-Eigenschaft durch Berücksichtigung von unterschiedlichen Ähnlichkeitsgraden zwischen Teilmengen von Alternativen (Nestern) zusätzlich abschwächt. Insbesondere wird die statistische Performance von einfachen MNL-, Latent Class- (LC) MNL-, HB-MNL-, Mixture-of-Normals- (MoN) MNL-, Dirichlet Process Mixture- (DPM) MNL- und HB-Nested Multinomialen Logit- (NMNL) Modellen (unter experimentell variierenden Bedingungen) hinsichtlich der Recovery von Präferenzstrukturen, der Anpassungsgüte und der Prognosevalidität analysiert. Dazu werden zwei umfangreiche Monte-Carlo-Studien durchgeführt, ferner werden die verschiedenen Modelltypen auf einen empirischen CBC-Datensatz angewandt. In der ersten Monte-Carlo-Studie liegt der Fokus auf dem Vergleich zwischen dem HB-MNL und dem HB-NMNL bei multimodalen und genesteten Präferenzstrukturen. Die Ergebnisse zeigen, dass es keine wesentlichen Unterschiede zwischen beiden Modelltypen hinsichtlich der Anpassungsgüte und insbesondere hinsichtlich der Prognosevalidität gibt. In Bezug auf die Recovery von Präferenzstrukturen schneidet das HB-MNL-Modell zunehmend schlechter ab, wenn die Korrelation in mindestens einem Nest höher ist, während sich das HB-NMNL-Modell erwartungsgemäß an den Grad der Ähnlichkeit zwischen Alternativen anpasst. Die zweite Monte-Carlo-Studie befasst sich mit multimodalen und segmentspezifischen Präferenzstrukturen. Um Unterschiede zwischen den Klassen von Modellen mit unterschiedlichen Darstellungen von Heterogenität herauszuarbeiten, werden hier gezielt die Grade der Heterogenität innerhalb von Segmenten und zwischen Segmenten manipuliert. Unter experimentell variierenden Bedingungen werden die state-of-the-art Ansätze zur Modellierung von Heterogenität (einfaches MNL, LC-MNL, HB-MNL) mit erweiterten Wahlmodellen, die sowohl Nachfragerheterogenität zwischen Segmenten als auch innerhalb von Segmenten abbilden können (MoN-MNL und DPM-MNL), verglichen. Das zentrale Ergebnis dieser Monte-Carlo-Studie ist, dass sich das HB-MNL-Modell, welches eine multivariate Normalverteilung zur Modellierung von Präferenzheterogenität unterstellt, als äußerst robust erweist. Darüber hinaus kristallisiert sich der LC-MNL-Segmentansatz als der beste Ansatz heraus, um die „wahre“ Anzahl von Segmenten zu identifizieren. Abschließend werden die zuvor vorgestellten Wahlmodelle auf einen realen CBC-Datensatz angewandt. Die Ergebnisse zeigen, dass Modelle mit einer kontinuierlichen Darstellung von Heterogenität (HB-MNL, HB-NMNL, MoN-MNL und DPM-MNL) eine bessere Anpassungsgüte und Prognosevalidität aufweisen als Modelle mit einer diskreten Darstellung von Heterogenität (einfaches MNL, LC-MNL). Weiterhin zeigt sich, dass das HB-MNL-Modell für Prognosezwecke sehr gut geeignet ist und im Vergleich zu den anderen (erweiterten) Modellen mindestens ebenso gute, wenn nicht sogar wesentlich bessere Vorhersagen liefert, was für Manager eine zentrale Erkenntnis darstellt.Choice-Based Conjoint (CBC) is nowadays the most widely used variant of conjoint analysis, a class of methods for measuring consumer preferences. The primary reason for the increasing dominance of the CBC approach over the last 35 years is that it closely mimics real choice behavior of consumers by asking respondents repeatedly to choose their preferred alternative from a set of several offered alternatives (choice sets), respectively. Within the framework of CBC analysis, the multinomial logit (MNL) model is the most frequently used discrete choice model. However, the MNL model suffers from two major limitations: (a) it implies proportional substitution rates across alternatives, referred to as the Independence of Irrelevant Alternatives (IIA) property and (b) it does not account for unobserved consumer heterogeneity, as part-worth utilities are assumed to be equal for all respondents by definition. Since the 1990s, Hierarchical Bayesian (HB) models have been used for part-worth utility estimation in CBC analysis. HB models are able to determine part-worth utilities at the individual respondent level even with little individual respondent information on the one hand and, as a result of addressing consumer heterogeneity, can strongly soften the IIA property on the other hand. The focus of the present thesis is on CBC analysis using HB models with different representations of heterogeneity (discrete vs. continuous) as well as using a HB model which mitigates the IIA property to a further extent by allowing for different degrees of similarity between subsets (nests) of alternatives. In particular, we systematically explore the comparative performance of simple MNL, latent class (LC) MNL, HB-MNL, mixture-of-normals (MoN) MNL, Dirichlet Process Mixture (DPM) MNL and HB nested multinomial logit (NMNL) models (under experimentally varying conditions) using statistical criteria for parameter recovery, goodness-of-fit, and predictive accuracy. We conduct two extensive Monte Carlo studies and apply the different types of models to an empirical CBC data set. In the first Monte Carlo study, the focus lies on the comparative performance of the HB-MNL versus the HB-NMNL for multimodal and nested preference structures. Our results show that there seems to be no major differences between both types of models with regard to goodness-of-fit measures and in particular their ability to predict respondents’ choice behavior. Regarding parameter recovery, the HB-MNL model performs increasingly worse when correlation in at least one nest is higher, while the HB-NMNL model adapts to the degree of similarity between alternatives, as expected. The second Monte Carlo study deals with multimodal and segment-specific preference structures. More precisely, to carve out differences between the classes of models with different representations of heterogeneity, we specifically vary the degrees of within-segment and between-segment heterogeneity. We compare state-of-the-art methods to represent heterogeneity (simple MNL, LC-MNL, HB-MNL) and more advanced choice models representing both between-segment and within-segment consumer heterogeneity (MoN-MNL and DPM-MNL) under varying experimental conditions. The core finding from our Monte Carlo study is that the HB-MNL model appears to be highly robust against violations in its assumption of a single multivariate normal distribution of consumer preferences. In addition, the LC-MNL segment solution proves to be the best approach to recover the “true” number of segments. Finally, we apply the previously presented choice models to a real-life CBC data set. The results indicate that models with a continuous representation of heterogeneity (HB-MNL, HB-NMNL, MoN-MNL and DPM-MNL) perform better than models with a discrete representation of heterogeneity (simple MNL, LC-MNL). Further, it turns out that the HB-MNL model works extremely well for predictive purposes and provides at least as good if not considerably better predictions compared to the other (advanced) models, which is an important aspect for managers

On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects

Random coefficients choice models are seeing widespread adoption in marketing research, partly because of their ability to generate household-level parameter estimates with limited data. However, the power of such models may tempt researchers to trust that they continue to produce reasonable estimates, when in fact either model misspecification or insufficient data limits the models' ability to recover household-level parameters successfully. If household-level choice behaviors are not recovered successfully, managerial decisions such as marketing-mix planning and targeting, direct marketing, segmentation, and forecasting may not produce the desired results. This study addresses the following questions. First, can random coefficients choice models correctly identify markets characterized by preference and response heterogeneity, state dependence, the use of alternative decision heuristics that result in reduced choice sets, and combinations of these effects? If so, how much data is required, and is this realistic given the size of data sets typically used in marketing analyses? Which model selection criteria should be used to identify these markets? When there is spurious market identification, which parameters contribute to the spurious result? An extensive simulation experiment is conducted wherein random coefficients logit models with varying specifications of parameter heterogeneity, state dependence effects, and choice set heterogeneity are applied to 128 experimental conditions. The results show which types of markets can be identified reliably and which cannot. Based on the results of the simulation, the authors develop a model selection heuristic that identifies the correct market in 81% of the experimental conditions. In contrast, strict application of the best model selection criterion alone results in correct market identification in at most 34% of experimental conditions. Interestingly, we find that the amount of data (number of households or number of purchases per household) does not affect our ability to identify the correct market type with this heuristic, so there is a good chance of identifying the correct market type even when little data is available.brand choice, consumer heterogeneity, consideration sets, state dependence

State Space Modelling of Dynamic Choice Behavior with Habit Persistence

In this dissertation, I present a new approach to capturing dependence across time in dynamic choice data. To achieve this, I develop a state space dynamic choice model and a novel algorithm to fit the data. Instead of capturing dependence in outcomes through lagged response variables, referred to as state dependence, I introduce a lagged utility term through the latent state equation. The lagged utility term captures habit persistence, which has not been explored directly in earlier models (Heckman, 1981b). The autoregressive nature of the lagged utility provides a significantly richer summary of prior utility than a lagged outcome variable. The fitting algorithm combines a non-linear particle filter with a standard Metropolis-Hastings step to compute Bayesian posterior estimates of the parameters. The model can capture habit persistence (inertia), variety seeking, serial correlation, and unobserved heterogeneity. Through simulation analysis, I demonstrate that while the proposed method is effective in estimating the parameters, both a large sample size and the number of simulated particles are critical. Misspecification in serial correlation in the random component of the utility function is shown to result in biased estimates for certain coefficients, although not the habit persistence term. This method avoids the initial conditions problem common with lagged variables (Wooldridge, 2010). From the perspective of a marketer, the value of the proposed model stems from its ability to distinguish the effects of habit, variety seeking, and heterogeneity. The algorithm is applied to case studies involving the sales of fast-moving consumer goods, as recorded in scanner data furnished by a major grocery store. The studies demonstrate the wide-ranging variation in purchasing habits and price sensitivity across customers; this variation highlights the value of the individual-level models applied in this study. Specifically, we find the existence of habitual purchasing behavior in utilitarian goods (e.g., cereal and soft drinks). However, in hedonic goods (e.g., beer), we find no evidence of habit persistence, which is in agreement with earlier studies

Consideration behavior and design decision making

Over the past decade, design engineering has developed a systematic framework to coordinate with consumer behavior models. Traditional consumer models applied in the past has mainly focused on the preference of compensatory trade-offs in the choice decisions. Recent marketing research has become interested in developing consumer models that are representative in that they reflect realistic human decision processes. One important example is consideration : the process of quickly screening out many available alternatives using non-compensatory rules before trading off the value of different feature combinations. Is capturing consideration important for design? This research investigates the impact of modeling consideration behavior to design engineering, aiming at constructing consideration models that can inform strategic decisions. The study includes several features absent in existing research: quantifying the mis-specifications of the underlying choice process, tailoring survey instruments for particular models, and exploring the models\u27 strategic value on product profitability and design feature differences. First, numerical methods are explored to address the discontinuity in the profit-oriented optimization problem introduced by the consideration models. Methods based on complementarity constraints, smoothing functions and genetic algorithms are implemented and evaluated with a vehicle design case study. Second, a simulation experiment based on synthetic market data compares consideration models and a variety of conventional choice models in the process of model estimation and design optimization. The simulation finds that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. The synthetic experiment framework then further extends the comparison to include the survey design process guided by the different assumptions behind considerations and traditional models. A product line design case study reveals that even though both compensatory models and consideration models show robustness in profitability, using consideration models leads to optimal portfolios with higher feature diversity while reducing the risk of overestimating profits. Finally, the research explores how to use consideration models to analyze the market penetration of newly designed product in a case study of a consideration maximization problem. It is the hope that this research will arouse the attention of designers to the informative power of consideration models, expand the understanding of consumer behavior modeling from the predictive power in the marketing field to the strategic impacts to design decisions, and provide technical support to the future application of consideration models in design engineering

A Descriptive and Normative Analysis of Marketing Budgeting

Marketing budgeting is one of the most important aspects of management and of highly relevance for business success. Due to rising competitive pressure and a considerable increase in marketing investments the importance of this subject has additionally grown in the last years. For this reason, marketing budgeting receives a huge amount of attention by research and practitioners alike. Accordingly, it is stated in the CMO Council Report of 2007: „The number-one challenge for most chief marketing officers is to quantify, measure, and improve the value of marketing investments and resource allocations“. The Marketing Science Instituteset this issue as top research priority for the time period 2010-2012: „How should firms determine the absolute level of marketing spending and how should spending be allocated at the strategic level - that is, across products, customer groups, and geographies?” The academic literature has been dealing with questions regarding the marketing budget process for a long time and therefore this issue has been discussed and analyzed in multiple ways. The focus of this literature has been on the allocation of budgets as previous research has shown that profit improvement from better allocation is much higher than from improving the overall budget. To give an overview of the existing literature we may distinguish between two main research streams: (1) the descriptive and (2) the normative analysis of marketing budgeting. Descriptive literature discusses the status quo of the marketing budgeting process in companies, i.e. it identifies how marketing budgets are actually determined and allocated by managers. Two types of descriptive studies have emerged in the literature. The first type covers a broad range of manager surveys about budgeting behavior. They indicate that budget decisions are mainly based on the application of some simple budgeting rules, such as the “Percentage of Sales” or “Competitive Parity” method, which are easy to apply and therefore be preferred by manager. But these studies ignore for the high complexity of the budgeting process and are exposed to several biases of survey studies. Therefore insights on the budgeting process based on survey results are quite limited. The second type of descriptive studies try to explain budgeting behavior by estimating the impact of relevant factors on the observable size and allocation of the marketing budget to identify determinants of budget setting. But as all of these studies apply highly different approaches in model design results across studies about the impact of determinants on budgeting are characterized by high heterogeneity. So in summary, literature may only provide a fuzzy and fragmented picture on how manager determine their marketing budget. Normative literature discusses how the marketing budget should be determined. A large body of work assists practitioners by developing diverse approaches for allocation optimization, covering several aspects of resource allocation. All of these solutions offer important general insights into the budgeting problem but generally are not implemented in the marketing practice as they cover only some aspects of the budget allocation problem and/or give suggestions on budget allocation which are not understood and therefore are not accepted by manager. For this reason, researcher developed several heuristics or decision calculus models which address the problem that optimization models cannot be well implemented in companies and offer easy to understand and close to optimum solutions for the complex allocation problem. But while all of the heuristics are focused on short-term profit maximization and thus ignore for dynamic effects which are highly important for budget allocation, the decision calculus models may only give imprecise implications for budget allocation. This explains why the application of scientific models for resource allocation by practitioners is quite rare. The objective of this dissertation is to offer a comprehensive analysis of marketing budgeting. Therefore this work contributes to descriptive as well as normative research by addressing two main research gaps which exist in the literature. In terms of descriptive analysis the existing literature provides only a fragmented picture about influential factors in the budgeting process. In terms of normative literature no method has been developed which address the complexity of the budget allocation task for a multi-country, multi product-firm as well as the need of practitioners for simple allocation rules. The first two papers of this dissertation address the descriptive analysis issues by (1) reviewing and structuring the fragmented literature of marketing budgeting behavior, and (2) developing an innovative approach to analyze empirically the application of budgeting methods in pharmaceutical companies. The last two papers of this dissertation address the normative analysis issues by (3) introducing and implementing an innovative solution to the dynamic marketing allocation budget problem for multi-product, multi-country firms, and (4) analyzing and comparing the performance of different allocation rules by simulation analysis. In summary, the dissertation’s focus is to understand how marketing budgets are set by practitioners, and how the allocation decision process can be improved