Handbook of Market Research 1st Edition by Christian Homburg, Martin Klarmann, Arnd Vomberg ISBN 9783319574134 3319574132

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ISBN 10: 3319574132
ISBN 13: 9783319574134
Author: Christian Homburg, Martin Klarmann, Arnd Vomberg

Handbook of Market Research 1st Edition Table of contents:

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

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Tags: Christian Homburg, Martin Klarmann, Arnd Vomberg, Market Research