Handbook of Market Research
- 2022
- Book
- Editors
- Prof. Dr. Christian Homburg
- Prof. Dr. Martin Klarmann
- Dr. Arnd Vomberg
- Publisher
- Springer International Publishing
About this book
In this handbook, internationally renowned scholars outline the current state-of-the-art of quantitative and qualitative market research. They discuss focal approaches to market research and guide students and practitioners in their real-life applications. Aspects covered include topics on data-related issues, methods, and applications. Data-related topics comprise chapters on experimental design, survey research methods, international market research, panel data fusion, and endogeneity. Method-oriented chapters look at a wide variety of data analysis methods relevant for market research, including chapters on regression, structural equation modeling (SEM), conjoint analysis, and text analysis. Application chapters focus on specific topics relevant for market research such as customer satisfaction, customer retention modeling, return on marketing, and return on price promotions. Each chapter is written by an expert in the field. The presentation of the material seeks to improve the intuitive and technical understanding of the methods covered.
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Table of Contents
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Frontmatter
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Data
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Frontmatter
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Experiments in Market Research
Torsten Bornemann, Stefan HattulaThe chapter begins by highlighting the success of A/B testing in Obama's 2008 presidential campaign, showcasing its potential in optimizing website design. It then delves into the fundamental principles of experimental design in marketing research, emphasizing the importance of determining factors, measuring outcomes, and selecting appropriate experimental settings. The text also discusses the advantages and challenges of different experimental environments, such as laboratory, field, and online experiments, and provides practical guidelines for conducting preliminary testing and assigning participants to treatments. Throughout the chapter, the authors emphasize the need for rigorous experimental design to ensure valid and reliable results.AI Generated
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AbstractThe question of how a certain activity (e.g., the intensity of communication activities during the launch of a new product) influences important outcomes (e.g., sales, preferences) is one of the key questions in applied (as well as academic) research in marketing. While such questions may be answered based on observed values of activities and the respective outcomes using survey and/or archival data, it is often not possible to claim that the particular activity has actually caused the observed changes in the outcomes. To demonstrate cause-effect relationships, experiments take a different route. Instead of observing activities, experimentation involves the systematic variation of an independent variable (factor) and the observation of the outcome only. The goal of this chapter is to discuss the parameters relevant to the proper execution of experimental studies. Among others, this involves decisions regarding the number of factors to be manipulated, the measurement of the outcome variable, the environment in which to conduct the experiment, and the recruitment of participants. -
Field Experiments
Veronica Valli, Florian Stahl, Elea McDonnell FeitField experiments are becoming increasingly vital in the digital age, offering new opportunities to measure and control business activities. This chapter explores the intersection of Big Data analytics and field experiments, highlighting their role in identifying causal relationships and improving business efficiency. It discusses the advantages of field experiments over lab experiments, particularly their high external validity and ease of explanation to business leaders. The chapter also provides a detailed definition of field experiments, emphasizing their authenticity, real-world context, and relevant outcome measures. Additionally, it delves into the challenges and best practices in designing and conducting field experiments, including the importance of randomization, treatment effects, and external validity. The chapter concludes by showcasing real-world examples of successful field experiments in business and academia, demonstrating their practical applications and the collaboration between firms and researchers. This comprehensive guide is essential for professionals seeking to leverage field experiments to inform business decisions and advance marketing practices.AI Generated
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AbstractDigitalization of value chains and company processes offers new opportunities to measure and control a firm’s activities and to make a business more efficient by better understanding markets, competitors, and consumers’ behaviors. Among other methodologies, field experiments conducted in online and offline environments are rapidly changing the way companies make business decisions. Simple A/B tests as well as more complex multivariate experiments are increasingly employed by managers to inform their marketing decisions.This chapter explains why field experiments are a reliable way to reveal and to prove that a business action results in a desired outcome and provides guidelines on how to perform such experiments step by step covering issues such as randomization, sample selection, and data analysis. Various practical issues in the design of field experiments are covered with the main focus on causal inference and internal and external validity. We conclude the chapter with a practical case study as well as a brief literature review on recent published articles employing field experiments as a data collection method, providing the reader with a list of examples to consider and to refer to when conducting and designing a field experiment. -
Crafting Survey Research: A Systematic Process for Conducting Survey Research
Arnd Vomberg, Martin KlarmannThe chapter 'Crafting Survey Research: A Systematic Process for Conducting Survey Research' highlights the critical role of surveys in decision-making and theoretical development. It discusses various types of survey research, their applications, and the decline in survey usage due to awareness of potential biases. The chapter offers a structured approach to survey design, including decisions about question content, format, and sequence, to mitigate biases such as common method bias, key informant bias, and social desirability. It also covers measurement theory, systematic errors, and procedural remedies to enhance survey reliability and validity. Additionally, the chapter provides insights into the survey research process, including selection of research variables, survey methods, and data analysis, making it a comprehensive guide for professionals aiming to conduct effective surveys.AI Generated
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AbstractSurveys represent flexible and powerful ways for practitioners to gain insights into customers and markets and for researchers to develop, test, and generalize theories. However, conducting effective survey research is challenging. Survey researchers must induce participation by “over-surveyed” respondents, choose appropriately from among numerous design alternatives, and need to account for the respondents’ complex psychological processes when answering the survey. The aim of this chapter is to guide investigators in effective design of their surveys. We discuss state-of-the-art research findings on measurement biases (i.e., common method bias, key informant bias, social desirability bias, and response patterns) and representation biases (i.e., non-sampling bias and non-response bias) and outline when those biases are likely to occur and how investigators can best avoid them. In addition, we offer a systematic approach for crafting surveys. We discuss key steps and decisions in the survey design process, with a particular focus on standardized questionnaires, and we emphasize how those choices can help alleviate potential biases. Finally, we discuss how investigators can address potential endogeneity concerns in surveys. -
Challenges in Conducting International Market Research
Andreas Engelen, Monika Engelen, C. Samuel CraigThe chapter delves into the growing importance of international market research for multinational companies seeking expansion. It discusses the need for multi-country studies to identify generalizable marketing phenomena and the challenges involved in ensuring data equivalence across different nations. The text highlights the importance of conceptual frameworks, research units, and data collection methods that account for cultural and contextual differences. It also emphasizes the role of national cultural dimensions in explaining variations between nations and the need for rigorous data analysis and interpretation to avoid misleading conclusions. The chapter provides practical advice and state-of-the-art approaches to conducting sound international marketing research, making it an invaluable resource for professionals in the field.AI Generated
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AbstractThis chapter explains the need to conduct international market research, identifies the main challenges researchers face when conducting marketing research in more than one country and provides approaches for addressing these challenges. The chapter examines the research process from the conceptual design of the research model to the choice of countries for data collection, the data collection process itself, and the data analysis and interpretation. Challenges identified include differentiating between etic and emic concepts, assembling an adequate research unit, ensuring data collection equivalence, and reducing ethnocentrism of the research team. We draw on the extant literature to determine methods that address these challenges, such as an adapted etic or linked emic approach, to define the concept of the culti-unit, and to identify prominent approaches to cultural dimensions and collaborative and iterative translation and statistical methods for testing equivalence. This chapter provides researchers with the methods and tools necessary to derive meaningful and sound conclusions from research designed to guide international marketing activities. -
Fusion Modeling
Elea McDonnell Feit, Eric T. BradlowThe chapter addresses the classic data fusion problem in marketing, where media consumption and product purchase data are often collected by different entities, making it challenging to analyze them together. It introduces the concept of data fusion as a Bayesian missing data problem and discusses various methods to handle this issue. The chapter emphasizes the importance of linking variables, such as demographics, that are observed in both data sets. It provides detailed examples and step-by-step guides to develop and estimate fusion models using Bayesian methods, such as data augmentation and Markov Chain Monte Carlo (MCMC) sampling. The chapter also explores the application of these methods to different types of data, including continuous and binary variables, and highlights the need for careful consideration of the missing data mechanism. By illustrating practical examples and offering a clear roadmap for data fusion, this chapter equips professionals with the tools to effectively integrate disparate data sources for better consumer insights.AI Generated
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AbstractThis chapter introduces readers to applications of data fusion in marketing from a Bayesian perspective. We will discuss several applications of data fusion including the classic example of combining data on media viewership for one group of customers with data on category purchases for a different group, a very common problem in marketing. While many missing data approaches focus on creating “fused” data sets that can be analyzed by others, we focus on the overall inferential goal, which, for this classic data fusion problem, is to determine which media outlets attract consumers who purchase in a particular category and are therefore good targets for advertising. The approach we describe is based on a common Bayesian approach to missing data, using data augmentation within MCMC estimation routines. As we will discuss, this approach can also be extended to a variety of other data structures including mismatched groups of customers, data at different levels of aggregation, and more general missing data problems that commonly arise in marketing. This chapter provides readers with a step-by-step guide to developing Bayesian data fusion applications, including an example fully worked out in the Stan modeling language. Readers who are unfamiliar with Bayesian analysis and MCMC estimation may benefit by reading the chapter in this handbook on Bayesian Models first. -
Dealing with Endogeneity: A Nontechnical Guide for Marketing Researchers
P. Ebbes, D. Papies, H. J. van HeerdeThis chapter addresses the challenge of endogeneity in marketing research, where independent variables are correlated with the error term in regression models. It provides a nontechnical guide on using instrumental variables (IVs) to estimate causal effects accurately. The text discusses common scenarios where endogeneity arises, such as with price and advertising strategies, and highlights the importance of IVs in overcoming these challenges. Practical examples and a step-by-step approach to IV estimation, including the two-stage least squares method, are presented. The chapter also explores when endogeneity matters and when it does not, emphasizing the critical importance of understanding and addressing endogeneity for optimal decision-making in marketing.AI Generated
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AbstractThis chapter provides a nontechnical summary of how to deal with endogeneity in regression models for marketing research applications. When researchers want to make causal inference of a marketing variable (e.g., price) on an outcome variable (e.g., sales), using observational data and a regression approach, they need the marketing variable to be exogenous. If the marketing variable is driven by factors unobserved by the researcher, such as the weather or other factors, then the assumption that the marketing variable is exogenous is not tenable, and the estimated effect of the marketing variable on the outcome variable may be biased. This is the essence of the endogeneity problem in regression models. The classical approach to address endogeneity is based on instrumental variables (IVs). IVs are variables that isolate the exogenous variation in the marketing variable. However, finding IVs of good quality is challenging. We discuss good practice in finding IVs, and we examine common IV estimation approaches, such as the two-stage least squares approach and the control function approach. Furthermore, we consider other implementation challenges, such as dealing with endogeneity when there is an interaction term in the regression model. Importantly, we also discuss when endogeneity matters and when it does not matter, as the “cure” to the problem can be worse than the “disease.”
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- Title
- Handbook of Market Research
- Editors
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Prof. Dr. Christian Homburg
Prof. Dr. Martin Klarmann
Dr. Arnd Vomberg
- Copyright Year
- 2022
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-319-57413-4
- Print ISBN
- 978-3-319-57411-0
- DOI
- https://doi.org/10.1007/978-3-319-57413-4
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