Handbook of Market Research

Preface
List of Reviewers
Contents
About the Editors
Contributors
Part I: Data
	Experiments in Market Research
		Introduction
		Experimentation and Causality
		Experimental Design
			Definition of the Research Question
			Determination and Operationalization of the Sources of Variation
				Independent Variable(s)
				Extraneous Variables
			Definition and Operationalization of the Measured Response-Variables
				Operationalization of the Dependent Variable(s)
				Mediators
			Decision About the Environmental Setting
				Laboratory Experiments
				Field Experiments
				Online Experiments
			Determination of the Experimental Units and Assignment to Treatments
				Number of Participants and Sampling Procedure
				Incentivization of Participants
				Assigning Participants to Experimental Treatments
			Preliminary Testing
		Exemplary Experimental Study
		Ethical Issues in Experimental Research
		Conclusion
		References
	Field Experiments
		Introduction
			Motivation
			Defining a Field Experiment
				Defining an Experiment
				Lab Versus Field Experiments
				Key Features of Field Experiments
				Online Experiments
		Experimentation: Causal Inference and Generalizability
			Estimating the Causal Effect of a Treatment
				The Average Treatment Effect
				Causality and Internal Validity
			Generalizability of Findings and External Validity
			Sample Size
		Experimental Design and Multivariate Experiments
		Examples of Field Experiments
			Case Studies
				Field Experiments in Business
				Field Experiments in the Academic Literature
		Conclusions
		Cross-References
		References
	Crafting Survey Research: A Systematic Process for Conducting Survey Research
		Introduction: Relevance of Survey Research
		Understanding Survey Bias
			Fundamentals of Survey Research
			Psychology of Survey Response
			Measurement Theory
				Reliability and Validity: Fundamentals
				Reliability and Validity: Implications for Survey Research
			Sources of Systematic Errors in Survey Research: Measurement Errors
				Common Method Bias
				Key Informant Bias
				Social Desirability
				Response Styles
			Sources of Systematic Errors in Survey Research: Representation Errors
				Non-sampling Bias
				Non-response Bias
		Survey Research Process
			Selection of Research Variables
				Which Relationships Are of Particular Interest?
			Selection of Survey Method
				Should Data be Collected Personally, by Telephone, in Written Form, or as an Online Survey?
				How Can Data Be Collected in an Online Context?
			Questionnaire Design
				Decision About the Survey Content
					What Are the Key Phenomena that Should Be Addressed in the Survey?
					What Phenomena Should Be Measured for Control Purposes?
					How Many Phenomena Can Be Included?
				Decisions About the Question Content
					What Questions about a Certain Phenomenon Should Be Asked?
					Are the Respondents Sufficiently Knowledgeable to Answer the Questions?
					Is it Better to Ask One Question or Several about a Certain Phenomenon?
					Which Questions Should Be Selected?
				Decision About the Question Format
					Are Open-Ended or Closed Questions More Appropriate?
					For Closed Questions, What Answer Options Should Be Provided?
				Decisions about the Question Wording
				Decisions about the Question Sequence
					Does the Sequence of Questions Foster a Pattern of Certain Answers?
					Do the Introductory Questions Motivate Respondents to Participate in the Survey? Does the Questionnaire Seem Structured?
				Decisions About the Survey Layout and Pretest of the Questionnaire
					Does the Questionnaire Have a Professional Appearance? Does the Format of the Questionnaire Lead to Easy Completion?
				Pretest of the Questionnaire
					Do Respondents Understand the Questions? Do Respondents Engage in Specific Cognitive Patterns?
			Data Collection
				What Is the Ideal Structure of the Sample?
				How Large Should the Sample Be?
				How Can we Achieve a High Response Rate?
			Measurement Evaluation
				To What Extent Do the Survey Questions Measure What they Are Supposed to Measure?
			Data Analysis
				How Are the Examined Phenomena Related? How Can the Results Be Illustrated?
		Endogeneity in Survey Research
		Conclusion
		Cross-References
		References
	Challenges in Conducting International Market Research
		Introduction
		Challenges in the Research Process
			Conceptual Framework (Phase 1)
			Research Units and Drivers of Differences (Phase 2)
				Definition of the Unit of Analysis
				Identifying Drivers of Differences Between Nations
			International Data Collection (Phase 3)
			Data Analysis (Phase 4)
			Interpretation (Phase 5)
		Summary
		References
	Fusion Modeling
		Introduction
			The Classic Data Fusion Problem in Marketing
			Mixed Levels of Data Aggregation
		Developing and Estimating Fusion Models
			Ex. 1: Fusing Data Using a Multivariate Normal Model
			Ex. 2: Fusing Data Using a Multivariate Probit Model
			Summary of the Process for Developing a Fusion Model
		Summary of Related Literature
			Literature on Data Fusion
			Related Missing Data Problems
		Conclusion
		Appendix
			R Code for Generating Synthetic Data and Running Ex. 1 with Stan
				R Commands for Ex. 1 (Requires Utility Functions Below to Be Sourced First)
				Utility Functions for Ex. 1
			Stan Model for Ex. 2 (Split Multivariate Probit Data)
			R Commands for Ex. 2
		References
	Dealing with Endogeneity: A Nontechnical Guide for Marketing Researchers
		Introduction
		What Is Endogeneity?
		Why and When Does Endogeneity Matter?
			Price Endogeneity
			Advertising Endogeneity
			Detailing Endogeneity.
			Firm Strategies
			CMO Presence
			Digital Piracy
			Summary
		How to Address Endogeneity in a Regression Model
		Implementing IV Estimation
		What Happens in an IV Regression When Using Poor IVs?
		Extensions of the Basic IV Approach
			Control Function
			Multiple Endogenous Regressors
			Interaction Terms
		The Benefit of Panel Data
		Conclusions
		References
Part II: Methods
	Cluster Analysis in Marketing Research
		Introduction
		An Overview of Clustering Methods
			Data Quality and Proximity Measures
			Distance-Based Cluster Algorithms
		Cluster Analysis of Market Basket Data
			Data Characteristics and Choice of Distance Measure
			Hierarchical Clustering
			K-Medoid Clustering
			K-Centroids Cluster Analysis
		Conclusions
		Cross-References
		References
	Finite Mixture Models
		Introduction
		Basic Idea of Finite Mixture Models
			Illustrative Example
			Finite Mixture Model and Likelihood Function
			Probability to Observe a Specific Value of the Segmentation Variable and Mixed Density Function
			Assignment of Consumers/Objects to Segments Within Finite Mixture Models
			Determining the Number of Segments
		Popular Applications of Finite Mixture Models in Multivariate Methods of Analysis
		Conclusion
		References
	Analysis of Variance
		Introduction
		Between-Subjects: One Observation per Person
			Two Means: One-Factorial ANOVA or Independent-Samples t-Test
			More Than Two Means: One-Factorial ANOVA
			Multiplicative Effects: Factorial ANOVA
		Within-Subjects: Two or More Observations per Person
			Two Means: One-Factorial RM-ANOVA or Paired-Samples t-Test
			More Than Two Means: One-Factorial RM-ANOVA
			Multiplicative Effects: Factorial RM-ANOVA/Mixed-ANOVA
		Extensions
			Analysis of Covariance (ANCOVA)
			Multivariate Analysis of Variance (MANOVA)
		Conclusion
		References
	Regression Analysis
		Introduction
		Statistical Explanation of the Method
			Problem Statement
			Objective Function and Estimation of Regression Coefficients
			Goodness of Fit
			Significance Testing
			Standardization of Coefficients
			Interpretation of Results
			Results of Numerical Example
			Assumptions
		Procedure
			Efficiency of Estimators
			Test for Multicollinearity
			Test for Autocorrelation
			Test for Heteroscedasticity
			Identification of Outliers
			Transformation of Variables
		Implications of the Analysis
		Endogeneity
		Further Topics
		Software
		Summary
		References
	Logistic Regression and Discriminant Analysis
		Introduction
		Discriminant Analysis
			Foundations and Assumptions of Discriminant Analysis
			Discriminant Analysis Procedure
				Classification Functions
				Distance Concept
				Probability Concept
		Logistic Regression
			Foundations and Assumptions of Logistic Regression
			Logistic Regression Procedure
		Applied Examples
			Research Question and Sample, Model, Estimation, and Model Assessment
			Discriminant Analysis
				Model Estimation and Model Assessment
				Interpretation of the Coefficients
			Logistic Regression
				Model Estimation and Model Assessment
				Interpretation of the Coefficients
		Conclusion
		References
	Multilevel Modeling
		Introduction: Relevance of Multilevel Modeling in Marketing Research
		Fundamentals of Multilevel Modeling
			The Conceptual Relevance of Multilevel Modeling
			The Statistical Relevance of Multilevel Modeling
			Types of Constructs and Models in Multilevel Modeling
		Process of Multilevel Modeling: The Two-Level Regression Model
			Step 1: Baseline Model
			Step 2: Adding Independent Variables at Level 1
			Step 3: Adding Independent Variables at Level 2
			Step 4: Testing for Random Slopes
			Step 5: Adding Cross-Level Interaction Effects
			Assumptions of Multilevel Modeling
			Model Estimation & Assessing Model Fit
			Variable Centering
			Sample Size Considerations
		Multilevel Structural Equation Modeling
		Software for Estimating Multilevel Models
		Example: Building and Estimating a Two-Level Model
		Conclusions
		Cross-References
		Appendix
		References
	Panel Data Analysis: A Non-technical Introduction for Marketing Researchers
		Introduction: Relevance of Panel Data for Marketing Research
		Process for Panel Data Analysis
			Define the Research Question
			Collect Panel Data
			Prepare Panel Data
				Transforming the Data Structure: Converting Wide Format to Long Format
				Combining Panel Datasets: Appending and Merging Datasets
				Preparing the Dataset: Length and Missingness of Panel Data
			Explore Panel Data
				Terminology
				Focal Challenge of Panel Data Analysis: Nonindependent Observations
				Dependent Variable: Between and Within Variance
				Independent Variables: Time-Constant and Time-Varying Variables
			Analyze Panel Data Models
				Pooled OLS Estimator: Ignoring the Panel Structure
				Modeling the Panel Structure
				Fixed Effects Estimator
				Random Effects Estimator
				Relationship between Pooled OLS, Fixed Effects, and Random Effects Estimators
				What Do Differences Between Pooled OLS, Fixed Effects, and Random Effects Estimators Imply?
				Hausman Test: Selecting Between the Fixed Effects and the Random Effects Estimator
			Interpret and Present Results
		Additional Methods in Panel Data Analysis
			Robust Inference
			Combining the Fixed Effects and Random Effects Estimators
				Between Effects Estimator
				Combined Approach
				Alternative Hausman Test
				Understanding How the Combined Approach Allows Consistent Estimation of Time-Varying Variables
			Hausman-Taylor Approach: Consistent Estimation of Time-Constant Effects in the Combined Approach
			Summary of the Discussed Estimators and Their Underlying Assumptions
			Modeling a Price-Response-Function in Differences
		Advanced Topics in Panel Data Analysis
			Dynamic Panel Data Estimation
				Dynamic Panel Models Without Cluster-Specific Effects
				Dynamic Panel Models With Cluster-Specific Effects
			Random Slope Models: A Multilevel Model Approach to Panel Data
			Addressing Measurement Error with Structural Equation Modeling Based on Panel Data
		Conclusion
		Cross-References
		References
	Applied Time-Series Analysis in Marketing
		Introduction
		Univariate Time-Series Treatments and Diagnostics
			Autoregressive (AR) and Moving Average (MA) Process
			Testing for Evolution Versus Stationarity
			ARIMA Models
				ACF and PACF Analysis
			Single Equation Time-Series Models with Exogenous Variables
		Multiple Time-Series Models: Dynamic Systems
		Granger Causality Tests
			Cointegration Test
			Vector Autoregressive and Vector Error-Correction Model
			Order of Lags in VAR Models
			Generalized Impulse Response Functions
			Generalized Forecast Error Variance Decomposition
			Volatility Models
		Conclusion
		Cross-References
		Appendix
			Software Application
			Data Visualizations
			ARIMA Modeling
				Log Transformation
				Stationary Tests
				ACF and PACF for Order of Lags
				Construct and Estimate an ARIMA Model
					Splitting the Data
					Train the Model
				Estimate a Multiple Regression Model
				Validation Set Assessment: ARIMA Versus MLR
			VAR Model Steps
				Estimating a VAR Model
				Forecast Error Variance Decomposition
				IRF Analysis
					Immediate and Long-Term Effects
				Optimal Allocation Between AdWords and Flyer Spending
		References
	Modeling Marketing Dynamics Using Vector Autoregressive (VAR) Models
		Introduction
		Vector Autoregressive (VAR) Modeling
		Unit Root and Cointegration Testing
			Testing for Evolution Versus Stationarity
			Cointegration Tests: Does a Long-Run Equilibrium Exist Between Evolving Series?
		Models of Dynamic Systems of Equations
			Vector Autoregressive Model with Exogenous Variables (VARX)
			Structural Vector-Autoregressive Model (SVAR)
			Vector Error Correction Model
		Policy Simulation Analysis
			Impulse Response Functions (IRF)
			Dynamic Multipliers
		Granger Causality Tests: Do We Need to Model a Dynamic System?
		Forecast Error Variance Decomposition (FEVD)
		Software Programs for VAR Estimation
		Illustrative Applications of VAR Modeling in Marketing
			Investor Response Models in the Marketing-Finance
			Marketing and Mindset Metrics Models
			Digital Marketing Models
		Conclusion
		Cross-References
		References
	Structural Equation Modeling
		Introduction
		The Core Structural Equation Model and Its Submodels
		Model Estimation
		Testing the Global Fit of Models
		Respecifying Models That Do Not Pass the Global Fit Test
		Assessing the Local Fit of Models
			Measurement Model
			Latent Variable Model
			The Problem of Endogeneity
		Extensions of the Core Structural Equation Model
			Measurement Model Extensions
			Latent Variable Model Extensions
			Models That Incorporate Population Heterogeneity
		Empirical Illustration of Structural Equation Modeling
			Conceptual Model
			Measurement Model
			Latent Variable Model
			Multi-Sample Analysis
		Concluding Comments
		Cross-References
		References
	Partial Least Squares Structural Equation Modeling
		Introduction
		Principles of Structural Equation Modeling
			Path Models with Latent Variables
			Structural Theory
			Measurement Theory
		Path Model Estimation with PLS-SEM
			Background
			The PLS-SEM Algorithm
			Additional Considerations when Using PLS-SEM
				Distributional Assumptions
				Statistical Power
				Model Complexity and Sample Size
				Goodness-of-Fit and Prediction
		Evaluation of PLS-SEM Results
			Procedure
			Stage 1.1: Reflective Measurement Model Assessment
			Stage 1.2: Formative Measurement Model Assessment
			Stage 2: Structural Model Assessment
		Research Application
			Corporate Reputation Model
			Data
			Model Estimation
			Results Evaluation
				Reflective Measurement Model Assessment
				Formative Measurement Model Assessment
				Structural Model Assessment
		Conclusions
		Cross-References
		References
	Automated Text Analysis
		Introduction
		Foundations of Text Analysis
			History
			Approaches to Text Analysis
			Dictionary-Based Methods
			Classification Methods
			Topic Modeling
		Market Research Applications of Text Analysis
			Sentiment Analysis
			Studying Word of Mouth Through Text Analysis
			Topic Discovery and Creating Positioning Maps from Online Text
			Measurement of the Organization and Firm Environment
			Issues in Working with Textual Data
		Extended Example: Word-Of-Mouth Differences Between Experts and Nonexperts to a Product Launch
			Purpose
			Stage 1: Develop a Research Question
			Stage 2: Data Collection
			Stage 3: Construct Definition
			Stage 4: Operationalization
			Stage 5: Interpretation and Analysis
			Stage 6: Validation
		Conclusion and Future Directions
		Cross-References
		References
	Image Analytics in Marketing
		Introduction
		Top Research Questions by Area Using Image Analytics
			Product Design
				Characterizing Designs: How Can Designs Be Characterized Above and Beyond Their Specific Visual Elements?
				Quantifying the Value of Designs: How Can We Assess and Predict Consumer Attitudes Toward Various Product Designs?
			Advertising
				Assessing Ad Creativity: How Can Print and Video Advertisements Be Rated According to Their Level of Creativity? What Are the ...
				Linking Visuals to Emotional and Cognitive Effects: What Visuals Should Be Included in an Ad in Order to Achieve a Desired Out...
				Monetizing the Value of Images: What Is the Value of Images in Various Stages of the Customer Journey?
			Branding
				Visual Brand Representation: What Is the Visual Representation of Brand Associations? How Does It Align with Brand Characteris...
				Brand Hierarchy: What Are the Optimal Relationships Between the Visual Elements of Brands in a Brand Portfolio?
				Brand Strategic Collaborations: When Brands Collaborate with Each Other, What Is the Right Mix of Their Visual Elements Which ...
			Online Shopping Experience
				The Role of Visuals in Online Product Display: How Does the Composition of Visual Elements, Objects, Size, Background, and Rel...
				The Role of Visuals in Ecommerce Website Design: How Do the Visual Components of an Ecommerce Website Contribute to Profitabil...
			Consumer Perspective
				Uncovering Consumer Attitudes: What Are the Hidden Consumer Traits and Attitudes That Can Be Revealed Through Images and Go Be...
		Data: Consumer Vs. Firm Images
			Consumer-Generated Images
				Images from Internet and Social Media
				Directly Elicited Images
			Firm-Generated Images
				Product Images on Retail Websites
				Images on the Firm Social Media Pages
				The Firm Brand Communications
				Advertising Databases
		Methods
			Feature Extraction
			Model Training
			Model Evaluation and Validation
			Model Application
		Integrating It All Together
		Conclusion
		Cross-References
		References
	Social Network Analysis
		Introduction
		The Relevance of Network Analyses for Marketing Purposes
		Network Metrics
		Network
			Basic Notation
			Actor Level
				Degree Centrality
				Betweenness Centrality
				Closeness Centrality
				Eigenvector Centrality
			Tie Level
				Tie Strength
				Homophily
			Network Level
				Size
				Density
				Degree Distribution
		Network Data and Sampling Methods
			Data Collection
			Network Sampling
		Social Network Analysis in R
			Data
			Calculating Actor Level Metrics
			Calculating Tie Level Metrics
			Modeling Social Contagion
			A Word of Caution
		Conclusion
		Cross-References
		References
	Bayesian Models
		Introduction: Why Use Bayesian Models?
		Bayesian Essentials
		Bayesian Estimation
			Examples of Posterior Distributions in Closed Form
			Posterior Distributions Not in Closed Form
			Model comparison
		Numerical Illustrations
			A Brief Note on Software Implementation
			A Hierarchical Bayesian Multinomial Logit Model
			Mediation Analysis: A Case for Bayesian Model Comparisons
		Conclusion
		Cross-References
		Appendix
			MCMC for Binomial Probit Without Data Augmentation
			HB-Logit Example
		References
	Choice-Based Conjoint Analysis
		Introduction
		Model
			Utility Model
				Evaluation Function for Attribute Levels
				Function to Combine Partworth Utilities Across Attributes
			Choice Model
		Procedure for Conducting Discrete Choice Experiments
			Identification of Attributes and Attribute Levels
			Creating the Experimental Design
				Factorial Design
				Choice Design
				Decision Parameters
			Implementation into Questionnaire
				Presentation of Stimuli
				No-Choice Option
				Collecting Additional Choices per Choice Set
				Incentive Alignment
				Holdout Choice Sets
			Estimation
				Coding
				Maximum Likelihood Estimation
				Relative Attribute Importance
				Willingness-to-Pay
				Market Simulations
				Modeling Alternatives
			Advanced Estimation Techniques
				Segment-Level Estimation
				Individual-Level Estimation
		Outlook
		Appendix: R Code
		References
	Exploiting Data from Field Experiments
		Introduction
		Motivation
		Field Experiments
		Difference-in-Differences Method
			Introduction
			Core Area of Application
			Critical Assumptions
			Application in Goldfarb and Tucker (2011)
		Regression Discontinuity Designs
			Introduction
			Core Area of Application
			Critical Assumptions
			Application in Flammer (2015)
		Instrumental Variables
			Introduction
			Core Area of Application
			Critical Assumptions
			Application in Bennedsen et al. (2007)
		Application of Methods in Standard Software
		Conclusions
		Cross-References
		References
	Mediation Analysis in Experimental Research
		Introduction
		Conceptual and Statistical Basics of Mediation Analysis
			The Single Mediator Model
				Conceptual Description of the Single Mediator Model
				Statistical Description of the Single Mediator Model
				Statistical Inference for the Single Mediator Model
				Assumptions of the Single Mediator Model
				Classifying Mediation
				Effect Size
				Variable Metrics
			Mediation Models Including More Than One Mediator: The Parallel and Serial Multiple Mediator Model
				Conceptual Description of the Parallel Multiple Mediator Model
				Statistical Description of the Parallel Multiple Mediator Model
				Statistical Inference for the Parallel Multiple Mediator Model
				Conceptual Description of the Serial Multiple Mediator Model
				Statistical Description of the Serial Multiple Mediator Model
				Statistical Inference for the Serial Multiple Mediator Model
				How to Interpret Results from Multiple Mediator Models
			Mediation Models Including a Moderator: Conditional Process Models
				Conceptual Description of Conditional Process Models
				Statistical Description of Conditional Process Models
				Statistical Inference for Conditional Process Models: Conditional Process Analysis
				Variable Metrics
			Further Mediation Models
				Mediation Models with Multiple Mediators and Moderators
				Mediation Models with Multiple Predictors and Outcomes
				Incorporating Time and Nested Data in Mediation Analysis
		Strengthening Causal Inference in Mediation Analysis
			Strengthening Causal Inference Through Design
			Strengthening Causal Inference Through the Collection of Further Evidence
			Strengthening Causal Inference Through Statistical Methods
		Questions Arising When Implementing Mediation Analysis
			Sample Size and Power in Mediation Analysis
			Mean Centering in Conditional Process Analysis
			Coding of Categorical Independent Variables
			Regression Analysis Versus Structural Equation Modeling
			Software Tools for Mediation Analysis
		Summary
		Cross-References
		References
Part III: Applications
	Measuring Customer Satisfaction and Customer Loyalty
		Introduction
		Conceptual Background
			The Relationship of Customer Satisfaction and Loyalty
			Conceptualizing Customer Satisfaction and Loyalty
		Measuring Customer Satisfaction
			Survey Scales
			Other Measurement Approaches
		Measuring Customer Loyalty
			Overview
			Loyalty Intentions
			Loyalty Behavior
				General Databases
				Loyalty Programs
		The Future of Managing Customer Satisfaction and Loyalty
		Concluding Remarks
		References
	Market Segmentation
		Introduction to the Concept
		Market Segmentation: Key Considerations
			Heterogeneity and Homogeneity
			Segment-of-One
			Concluding Thoughts
		Market Segmentation: Process
			Step 1: Characterizing the Ideal Market Segment
			Step 2: Determining the Segmentation Criteria
			Step 3: Collecting and Evaluating Data
			Step 4: Forming Segments
			Step 5: Evaluating the Final Segment Solution
			Step 6: Implementing the Market Segmentation
		Conclusions and Managerial Implications
		Cross-References
		References
	Willingness to Pay
		Introduction
		Conceptual Definitions of WTP
		Methods for Measuring Willingness to Pay
			Stated Preference Methods
				Direct Stated Preference Methods
				Experimental Stated Preference Methods
				Hypothetical Bias in Stated Preference Methods
			Revealed Preference Methods
				Direct Revealed Preference Methods
				Experimental Revealed Preference Methods
			Summary of Methods for Measuring WTP
		Drivers of WTP
			Situational Factors
			Individual Factors
			Information-Related Factors
		Market Research Application
			Elicitation of Consumers´ WTP
				Measurement 1: Open-Ended Questions
				Measurement 2: Dichotomous Choice Method
				Measurement 3: BDM Mechanism
				Results of the Measurements
			Application 1: Price Bundling
			Application 2: Personalized Pricing
			Application 3: Nonlinear Pricing
		Conclusion
		References
	Modeling Customer Lifetime Value, Retention, and Churn
		Introduction
		A Taxonomy of Customer Lifetime Value Measurement Models
			Retention Models for CLV Measurement
				Deterministic Models
				Probabilistic Models
					Parametric Models
					Semi-Parametric Models
			Migration Models for CLV Measurement
				Deterministic Models
				Probabilistic Models
					Markov Processes
					Combinations of Models
			Continuous Mixed Models: The Family of NBD Models to Measure CLV
				The Pareto/NBD and BG/NBD Model
				The Explanatory Pareto/NBD and BG/NBD Model
		An Application of Stochastic Pareto/NBD and BG/NBD Models for Customer Base Management
			Data and Methodology
			Estimation
				Estimation of the Purely Stochastic Parameters
				Estimation of the Parameters Including Explanatory Variables
			Results
			Parameter Estimations
			Purchase Prediction Validity
		Conclusion
		References
	Assessing the Financial Impact of Brand Equity with Short Time-Series Data
		Introduction
		Marketing Academics´ Views on the Measurement of Brand Equity
			Customer Mindset Brand Equity
			Product Market-Based Brand Equity
			Financial Market-Based Brand Equity
				Accounting Valuation of Brands
				Financial Market Value-Relevance of Brands
		Assessing Long-Term Impact of Brand Equity
			Empirical Illustration
				Calculating Abnormal Stock Returns
				Calculating Unanticipated Components of ROA and Brand Asset Index
				Assessing the Presence of Simultaneity
			Total Financial Impact of Brand Asset
			Heterogeneity in Brand Equity Impact
				Sector-Specific Differences in the Impact of Brand Asset Index
				Component-Specific Differences in the Impact of Brand Perceptions
		Conclusion
		References
	Measuring Sales Promotion Effectiveness
		Introduction
		Sales Promotion Tools and Their Effects
		Data for Measuring Sales Promotion Effectiveness
			Non-experimental Data on Observed Behavior
			Further Sources of Data
		Measuring Promotion Effectiveness with Panel Data
			SCAN*PRO
			PROMOTIONSCAN
			Decomposition Based on Single-Source Data
		Summary
		References
	Return on Media Models
		Introduction
		The Importance of Reference Points in Media Return Calculations
		Fundamental Advertising Response Phenomena
		Estimating Media Response Parameters
			The Shape of the Advertising-Response Function
			Advertising-Response Dynamics
			Data-Interval Bias
			Asymmetric Response
			Dealing with Reverse Causality
			Differences Across Media
			Advertising Copy and Creative Effects
			Intermediate Performance Metrics
		Deriving Media Returns from the Estimated Response Parameters
		Path-to-Purchase and Attribution Models for Digital Media
		Media Advertising and Asset Creation
			Brand Equity
			Customer Equity
			Other Response Metrics
		Conclusion
		References
Index