1. Market Research is Not Delivering
   1. Response Bias and Non-response Bias
   2. Market Research not being Used
   3. Misapplications of many Techniques
2. Bar Raised for Market Research
   1. Insights
   2. Trusted Advisor
3. Model of Trusted Advisor
   1. Professional Trust
   2. Personal Trust
4. Impact of Trusted Advisor Paradigm on Market Research Process
   1. Problem Definition
   2. Research Design
   3. Implementation of Recommendations

1. Roles and Organizational Design
   1. Different types of expertise
   2. Roles
   3. Structure
2. Engagement and Dissemination Process
   1. The business engagement (project briefs/rfps, partner mapping/account management, funding, etc.)
   2. Dissemination processes (publication taxonomy, publication guidelines, templates and branding, persuasive communication, real-time reporting, organizational memory)
   3. Vendor management
3. Managing Effectively
   1. Managing with Authority (goals, goal mapping, professional team development, energizing for impact)
   2. Managing without authority
4. Budgeting

1. Description of the issue
   • Effective applications of research require much more than high quality information. It also requires skill in the art and science of putting information to work. An important part of this skill lies in the non research qualities of client and researcher interactions. Being a “really good” client and a “really good” researcher involves not only the management of interpersonal relationships but the management of thinking patterns as well.
2. Definition of what we mean by being (and not being) “really good”
   • We plan to conduct between 6 and 8 in depth interviews with people who are generally acknowledged to be really good clients. Criteria for defining “really good” will be specified. The interview itself, using ZMET, will focus on what these client side experts expect of “really good” researchers beyond technical competence. As appropriate, we will draw on our own years of experience and on other sources to define the dimensions of being a “really good” researcher as viewed by clients.
   • We will also address the issue of which comes first, really good clients or really good researchers. Of course, in the end, both are required in a partnership.
3. Guidelines
   • Drawing from the interviews and other sources of insight, we will develop several guidelines for establishing and maintaining the necessary conditions for providing actionable research and for putting such research into action. As we are likely to see, these guidelines will revolve around the interacting forces of Workable Knowledge and Workable Wondering. Clients and researchers are full partners in both activities.

Chapter 4. Deep Engagement with Consumer Experience: Listening and Learning with Qualitative Data

1. Introduction
2. Orientation, Scope and Aims
3. Qualitative Data in Your Research Portfolio
4. Fundamentals: Observational Data and Verbal Data
5. Applications: Interpretation & Strategic Insight
   1. Observation
   2. Photos & Videography
   3. Depth Interviews
   4. Focus Groups
   5. Participant Observation and Consumer Ethnography
   6. Extensions in Cyberspace: Netnography
   7. Projectives
6. How Do You Tell If It's Any Good?
7. Prospects

Note: The author hopes to illustrate many elements of the chapter with boxed exhibits offered by practitioners that summarize industry applications.

Chapter 5. Questionnaire Design and Scale Development

1. Importance of Questionnaire
   1. Questionnaire Definition
2. Questionnaire Design Process
3. Specify the Information Needed
4. Specify the Type of Interviewing Method
5. Determine the Content of Individual Questions
6. Design the Question to Overcome the Respondents Inability to Answer
7. Design the Questionnaire to Overcome the Respondents Unwillingness to Answer
8. Decide on the Question Structure
9. Types of Scales
10. Determine the Question Wording
11. Arrange the Questions in Proper Order
12. Choose the Form and layout
13. Reproduce the Questionnaire
14. Pretest the Questionnaire
15. Observational Forms

1. The importance of response biases in marketing research
2. Types of response bias
   1. General overview
   2. Social desirability bias
   3. Acquiescence bias
   4. Extreme responding
3. Psychological processes giving rise to response bias
4. Techniques for controlling response bias
5. Recommendations for controlling response bias

Chapter 9 . Sampling and Weighting

Daniel Mallett ( Daniel Mallett Associates)

1. Probability Sampling – What is it and brief history.
2. Probability Sampling Methods.
   1. Simple Random – Systematic Element Sampling.
   2. Stratified Sampling – Proportionate - Disproportionate.
      1. Cluster Sampling.
      2. Multistage Sampling.
      3. Multiphase Sampling.
3. Sample Estimation and Weighting.
   1. Theory of Unbiased Estimation.
   2. Non-Response – Non Coverage Weighting.
   3. Post-Stratification.
   4. Weighting Methods.
      1. Cell Weighting.
      2. Ranking – Sample Balancing.
      3. Propensity and other Multivariate Weighting
4. Statistical Inference and Sampling Errors.
   1. Theories of Statistical Inference.
   2. Methods for Sampling Error Computation.
      1. Analytical – Taylor Series.
      2. Jack-Knife.
      3. Replication.
      4. Approximation Methods.
5. Alternatives to Full Probability Sampling.
   1. “Modified” last stage probability sampling.
   2. Quota Sampling.
   3. Mall Intercept Sampling.
   4. Internet and Panel Sampling.
6. Properties of Data
   1. Nominal, Ordinal, Interval and Ratio.
   2. Frequency Distribution.
   3. Central Tendencies – Mean, Median and Mode.
      1. When to use, conditions under which one can produce misleading results.
   4. Dispersion – Range, Variance.
7. Statistical Inference and Hypotheses Testing
   1. Sample Mean & Population Mean.
   2. Sample Characteristics and Population Characteristics.
   3. Standard Error, Sampling Distribution.
   4. The meaning behind statistical significance & the value of “directional” findings.
   5. Type I and II Errors.
   6. alpha and p-value.
8. Conclusions – When to Use.

Chapter 11. Basic Data Analysis

1. An Overview of the Analysis Process
   1. General comments on data tabulation
   2. Defining categories
   3. Editing and coding
   4. Tabulation for purposes of cleaning the data
2. Basic Concepts of Analyzing Associative Data
   1. Bivariate cross-tabulation
   2. Introducing a third variable
   3. Other possible relationships in cross-tabulation data
   4. Recaptiulation
3. Bivariate Analysis: Differences Between Sample Groups
   1. Bivariate cross-tabulation
   2. Differences in means and proportions
   3. Testing of group means: analysis of variance
4. Bivariate Analysis: Measures of Association
   1. Correlation analysis
   2. Introduction to bivariate regression
   3. Rank correlation
5. Nonparametric Analysis
   1. Other tests
   2. Indexes of agreement
6. Summary
7. References

1. The Marketing Engineering Approach
2. Tools for Marketing Engineering: Market Response Models
   1. Types of Response Models.
   2. Some Simple Market Response Models.
   3. Objectives.
   4. Multiple Marketing-Mix Elements: Interactions.
   5. Dynamic Effects.
   6. Market-Share Models and Competitive Effects.
   7. Response at the Individual Customer Level.
   8. Shared Experience and Qualitative Models.
   9. Choosing, Evaluating, and Benefiting from a Maketing Response Model.
3. From Promise to Realization
   1. Implementing Marketing Engineering.
   2. A Look Ahead.

Chapter 13. Regression Models

Donald R. Lehmann (George E. Warren Professor of Business, Columbia Business School )

1. Basic Logic
2. Basic Model
3. Model Specification
   1. Which variables to include.
   2. Functional form.
4. Estimation(very brief) and Interpretation
5. Model checks
   1. Nested Models.
   2. Mediators and moderators.
   3. Model Comparisons.
6. Complications and Special Applications
   1. ANOVA.
   2. Logical Consistency.
   3. Heterogeneity and Fixed Effects.
   4. Simultaneity.
7. Forecasting

1. Logistic Regression
   1. Model.
   2. Parameter Interpretation.
   3. Application.
2. Logit
   1. Model.
   2. Parameter Interpretation.
   3. Model Fit and Predictions.
   4. Aggregation and Sampling (e.g., sampling of alternatives).
   5. Application.
3. Probit
   1. Model.
   2. Parameter Interpretation.
   3. Application.
4. Tobit
   1. Model.
   2. Parameter Interpretation.
   3. Application.
5. Discriminant Analysis

1. What is Conjoint Analysis?
   1. Introduction
      1. The many flavors of CA.
      2. Individual vs. aggregate behavior.
   2. Conceptual framework for CA
      1. How markets evolve and how consumers evolve in these markets
      2. Simulating deci sion making using CA
   3. Behavioral theory and statistical theory
      1. Types of statistical models used in CA.
      2. Random Utility Theory as a behavioral basis for CA.
      3. Types of utility functions (e.g. main effects versus more complex models).
      4. Types of choice models
      5. Role of the scale parameter
   4. Key uses of Conjoint Analysis
      1. Attribute scaling.
      2. Product optimization.
      3. Market/competitive analyses
      4. Market share vs. demand models
      5. Multiple choice/allocation
2. Research Design
   1. Task design
      1. Number of attributes.
      2. Numbers of alternatives.
      3. Branded vs. non-branded alternatives.
      4. Ranking vs. rating.
      5. Forced choice vs. None alternative.
      6. Multiple choice/allocation
      7. Holdout tasks.
   2. Attribute design
      1. Numbers of levels.
      2. Wording of attributes.
      3. Restrictions.
      4. Linked/complex attributes.
   3. Experimental design
      1. Number of profiles/sets per person.
      2. Including interactions.
      3. Correlations among attributes in the design.
      4. Best practices in experimental designs.
      5. Adaptive vs. partial profile vs. fixed designs.
      6. Randomized designs vs. fixed designs.
3. Survey Design
   1. Method of data collection
      1. Traditional vs. Internet based surveys.
   2. Task layout
      1. Too much vs. too little information about attributes and alternatives.
      2. Multiple screens/pages/responses per task.
      3. Feature glossaries and example tasks.
      4. Graphics and text, pop-ups, multimedia.
   3. Task layout
      1. Multiple questions per task versus all-in-one layouts.
      2. Discrete choices vs. multiple choice vs. allocation.
4. Analysis
   1. Data coding
      1. Dummy vs. effects coding.
      2. Alternative specific constants.
      3. Complex attributes
   2. Model estimation
      1. OLS
      2. Discrete choice models.
      3. Latent class models.
      4. Hierarchical Bayes models.
      5. Model validity.
      6. Segmentation with CA.
      7. Combining market data and experimental data to predict choices
5. Reporting
   1. Simulation
      1. Aggregate vs. sample enumeration.
      2. First choice, randomized first choice, probabilistic prediction.
      3. Aggregate, segment-level, and individual-level reporting.
      4. Probabilities, market share, demand estimates.
      5. Calibration.
      6. Graphics.
      7. Sensitivity analyses.
      8. Forecasting with synthetic populations.
   2. Scenario Planning
      1. The base condition.
      2. Attribute importance/price sensitivity.
      3. Market conditions/scenarios.
      4. Market entry and competitive response.
      5. Rate of market change.
      6. Graphical results of market scenarios.
6. Understanding the complete picture
   1. Role of other sources of influence
   2. Market information
   3. Socio-demographics
   4. Quantity and volume choices
7. Wrap up and conclusions
   1. Summary of Do's and Don'ts
   2. Unresolved issues in CA
   3. A wish list of problems that need solutions

1. Designs for Alternative (Brand) Specific Models
   1. Specification of the design problem.
   2. Discrete choice designs from linear model designs.
   3. Properties of designs – balance and orthogonality.
   4. Coding for factorial effects.
   5. Estimation and design efficiency.
   6. IIA and violations of IIA.
2. Using Orthogonal Arrays to Construct Discrete Choice Designs
   1. Orthogonal arrays – where to find them.
   2. How to choose the orthogonal array.
   3. Randomization of factors and levels.
   4. Breaking down factors and collapsing levels.
   5. Examples.
   6. Computer generated designs (SAS).
3. Designs for Estimating Availability Cross Effects
   1. Specification of the design problem.
   2. Pure presence/absence designs.
   3. Designs for brand availability and price cross effects.
   4. More general availability designs.
4. Optimal Designs for Generic Effects Models
   1. Specifying the design problem.
   2. Some example constructs.
   3. Optimal designs for main effects.
   4. Optimal designs for two factor interactions.
5. Partial Profile Designs
   1. Specifying the design problem.
   2. Estimation and coding of effects.
   3. Some example constructs.
   4. Computer generated partial profile designs.

1. Introduction
   1. Using theory of planned behavior (TPB) as an example, illustrate the limitations of ordinary regression in testing complex theories.
   2. Introduce simultaneous equation model (path analysis), outline its advantages over regression, but also its limitations.
   3. Introduce a latent variable model for TPB, in diagram and equation form.
   4. Outline advantages of SEM.
2. Basic Concepts
   1. Assumptions of SEM.
   2. Estimation theory and practice.
   3. Model testing.
      1. Statistical fit.
      2. Practical fit .
   4. Parameter testing .
   5. Model modification .
3. Advanced Concepts
   1. Statistical alternatives for nonnormal data.
   2. Handling missing data.
   3. Including a mean structure, growth curve motivation.
   4. Multiple group models, multilevel models, and other more complicated models.
4. Things to watch out for
   (correlated errors, sample size, correlation matrix with MG, etc.)
5. Resources
   1. List of programs.
   2. References by advanced topics.

1. Factor Analysis
   1. Conceptual foundations and terminology.
   2. Uses of factor analysis or when should factor analysis be done?
   3. Difference between true score and common factor models.
   4. Types of factor analysis (e.g., common, Q-factor analysis, R-factor analysis, and confirmatory factor analysis.
   5. Assumptions of factor analysis.
   6. Difference between principal component analysis and factor analysis.
   7. Problems in common factor models (indeterminacy of factor analysis due to rotations of solution and factor score indeterminacy).
   8. Do's and don'ts of factor analysis.
   9. Empirical illustration.
2. Cluster Analysis
   1. Conceptual foundations and terminology of cluster analysis.
   2. Uses of cluster analysis (relate it to segmentation and positioning).
   3. Types of cluster analysis (e.g., hierarchical, non-hierarchical, and unrestricted latent class analysis).
   4. Similarity measures.
   5. Do's and don'ts of cluster analysis.
   6. Empirical illustration.
   7. Similarities and differences between cluster and factor analysis.

1. Scales of measurement and allowable arithmetic operations.
2. Various types of traditional regression models based on these scales of measurement for dependent and independent variables.
3. Problems with heterogeneity and traditional regression approaches.
4. Regression Models.
   1. Mixture regression models:
      1. Selection of dependent and independent variables.
      2. Picking a distribution for the dependent variable.
      3. Coding of the independent variables.
      4. Class descriptions.
   2. Pitfalls of mixture regression applications:
      1. Local optima.
      2. Identification.
      3. Erroneous assumptions.
5. Advanced topics.
6. Implementation issues:
   1. Software: GLIMMIX, Latent Gold, WinBugs.

1. Outline of the method
   1. Model written in hierarchical form
   2. Estimation by Bayesian methods
2. Underlying assumptions
   1. Analysis involves more than the observed data
      1. Uncovering what is behind the data
         1. Observations as censored realizations (e.g., choice)
         2. Models used to describe smooth process (e.g., binning the data versus fitting the data).
   2. Model assumptions
      1. Writing models in hierarchical form
      2. Concept of conditional independence
   3. Estimation assumptions
      1. Conditioning on the data versus conditioning on the model
         1. Example from medicine (laboratory testing versus clinician).
         2. Example from poker (enumeration/discrete math).
      2. Bayes theorem – accounting for uncertainty.
      3. Probability as a reasonable belief versus frequency
         1. Discussion of prior information in model specification
         2. Discussion of prior information in model parameters/coefficients.
3. How and when to use it
   1. Concept of estimation – finding the model parameters that are most probable for the data
   2. The HB revolution – Markov chain Monte Carlo methods
      1. Breakthrough in computation
      2. Estimation by exploring the posterior distribution
   3. Hierarchical models have parameters contained in multiple equations
      1. Bridging
      2. Shrinkage/sharing of information
   4. When to use it:
      1. Description of marketing data (many units, short histories per unit)
      2. Data are discrete – not normal.
4. How to evaluate and validate the results
   1. Convergence in distribution versus to a point.
   2. Using simulated value empirically to compute means, confidence intervals, functions of parameters
      1. Application to choice simulators
      2. Decision rules.
5. How and when NOT to use it
   1. Cooking the results – strong priors
   2. Plenty of data
6. Examples of successful applications
   1. Models of behavior (within unit analysis)
      1. Choice models
      2. Non-linear utility
      3. Consideration sets
   2. Models of heterogeneity (across unit analysis)
      1. Continuous versus discrete
      2. Inter-dependence
      3. Unit of analysis – the person-occasion
         1. Independent variables – demos versus
   3. Decision rules and strategically determined covariates (loss functions, systems of equations)
      1. Loss functions
      2. Evaluation of marketing effectiveness
      3. Endogeneity – multiple response items (e.g, analysis of surveys)
7. Miscellaneous
   1. Future research and applications
      1. State-space models and temporal dynamics
      2. Quantitative psychology
      3. Economic models

1. Overview of methodology
   1. Continuous time.
   2. Discrete time.
2. Overview of estimation
3. "Micro" applications in marketing (individual / household level models)
4. "Macro" applications in marketing (firm strategy models)
5. Future directions

1. Introduction: Scope of Data Mining
2. Characteristics of Data Mining Problems
   1. Big versus small data.
   2. Data quality.
   3. Data Analysis vs. Data Mining.
   4. Multi-perspective versus single-perspective methods.
   5. Actionable output - who's doing the analysis?
   6. Statistical reliability, statistical similarity between data sets.
   7. Importance of time/space dependence in the data.
3. Profiling versus Predictions
   1. Pros and cons of different model classes - trade off between accuracy, interpretability, actionability etc.
   2. Supervised versus unsupervised learning.
   3. Models for Profiling.
   4. Models for Predicting temporal data or cross-sectional.
   5. Time series analysis versus "static" classification.
   6. Pros and cons of using one model versus many (single versus
      multi-perspective)
   7. Learning and adaptation.
   8. Search techniques in high-dimensional spaces Classifiers - Bayesian, naive Bayesian ...
      When is a classifier "fit"?
4. Insurance problem as a concrete illustration of the comments and analysis of the previous two sections
5. Conclusions, take away points, dos and don'ts

PART IV: CONCEPTUAL APPLICATIONS

1. Historical Context
   1. Growth of the Media
   2. Evolution of Research Philosophy and Techniques
2. The Roles for Research
   1. The Media
   2. Advertising Content and Effectiveness
3. Models
   1. Media Models
   2. Advertising Effect Models
4. The Stages of Advertising Research
   1. Developmental Research and the Qualitative Role
   2. Evaluative Pre-Testing
   3. Post On-Air Ad Tracking
5. The Key Quantitative Metrics
   1. Media Ratings
   2. Recall of Brands and Sales Points
   3. Persuasion
   4. Commercial Likeability
   5. Eye Movement
   6. Diagnostics/Content Analyses/ Second-by-second measures
   7. Sales Effects
6. Roles and Responsibilities
   1. The Client
   2. The Ad Agency
   3. The Research Company
   4. The Media
7. Technique Variations by Media Type
   1. TV Commercial Testing
   2. Radio
   3. Print
   4. Newspapers
   5. Billboards
   6. Online
8. Innovation and Future Challenges
   1. The Internet
   2. Technology and Commercial Avoidance
   3. An Integrated Research Approach

1. Econometric Models
2. Modeling Advertising Response
3. Simple Models
   1. Linear Model.
   2. Multiplicative Model.
   3. Exponential Model.
   4. Conclusion.
4. Modeling Competition
   1. Multiplicative Attraction Model.
   2. Exponential Attraction Model.
   3. Classification of Advertising Models.
5. Modeling Consumer Choices
   1. Modeling Brand Choice.
   2. Modeling Purchase Incidence.
   3. Modeling Quantity.
   4. Modeling Interdependence Between Choices.
6. Conclusion
7. Modeling Dynamics
   1. Definitions.
   2. Ad Stock Model.
   3. Koyck Model.
   4. Distributed Lag Model.
   5. Summary.
8. Modeling Contingencies
   1. Hierarchical Model.

1. Introduction: What We Hope to Accomplish
2. Why Segment?
   1. Traditional vs. Modern Perspectives
   2. Pitfalls and Challenges
3. Segmentation Methodology
   1. Segmentation vs. Classification
   2. Methodology Spectrum
   3. A priori Methodology
      1. Overview
      2. Comparison
   4. Post-hoc Methodology
      1. Overview
      2. Comparison
4. Segmentation: Typical Steps
   1. Step 1: Hypothesize segment dimensions
   2. Step 2: Identify and refine preliminary variables to use for each dimension
   3. Step 3: Conduct segmentation analysis
   4. Step 4: Evaluate segment solution
5. Classification Models
   1. Why Classify?
   2. Classification Models
      1. Overview
      2. Comparison
   3. Typical Steps
   4. Key Considerations
6. Reporting and Profiling Segments
   1. Key Issues
   2. Reporting Templates
7. Implementation and Validation
8. A Case Study
9. Concluding Remarks

1. What is Brand Equity?
   1. Definitions
2. Measuring Sources of Brand Equity
   1. Qualitative Research Techniques
      1. Free Association
      2. Adjective Ratings and Check Lists
      3. Projective Techniques
      4. Photo Sorts
      5. Bubble Drawings
      6. Story-Telling
      7. Personification Exercises
      8. Role-Playing
   2. Quantitative Research Techniques
      1. Brand Awareness
         1. Direct and indirect measures of brand recognition
         2. Aided and unaided measures of brand recall  
      2. Brand Image
         1. Open-ended and scale measures of specific attributes and benefits
            1. Strength
            2. Favorability
            3. Uniqueness
      3. Overall attitudes, intentions, and behavior
3. Measuring Outcomes of Brand Equity
   1. Comparative Methods
      1. Brand-based comparative approaches : Experiments in which one group of consumers respond to an element of the marketing program when it is attributed to the brand and another group responds to that same element when it is attributed to a competitive or fictitiously named brand.
         1. Examples
      2. Marketing-based comparative approaches : Experiments where consumers respond to changes in elements of the marketing program for the brand or competitive brands.
         1. Examples
      3. Conjoint analysis
   2. Holistic Methods
      1. Residual approach: Examines the value of the brand by subtracting out consumers' preferences for the brand based on physical product attributes alone from their overall brand preferences.
      2. Valuation approach : attempts to place a financial value on the brand for accounting purposes, mergers and acquisitions, or other such reasons.
         1. Interbrand
4. Putting It All Together: The Brand Value Chain

1. What is Customer Satisfaction?
   1. Definition.
   2. Related Concepts.
   3. Dissatisfaction.
   4. Comparison Operators.
   5. Satisfaction Scales.
   6. Scale Points.
   7. A Satisfaction Model.
2. The Performance of Features, Attributes, and Benefits/Failings.
   1. Descriptive Statistics.
   2. Importance-Performance (IP) Analysis.
   3. The Shortcomings of IP Analysis.
   4. Derived Importances Using Regression.
   5. What Features?
   6. Satisfaction Drivers vs. Choice Criteria.
   7. Level of Abstraction.
   8. Scaling Performance.
   9. Temporal Survey Issues.
3. Expectations and Their Measurement.
   1. Expectations Defined.
   2. Multiple Standards.
   3. Predictive vs. Retrospective Expectations.
   4. Updating Problems.
   5. Why Measure Expectations?
4. Disconfirmation and Its Measurement.
   1. Disconfirmation Defined.
   2. Objective vs. Subjective Disconfirmation.
   3. Measuring Disconfirmation.
   4. Operation of Disconfirmation in the Satisfaction Model.
   5. Why Measure Disconfirmation?
5. Short-term Consequences of Satisfaction.
   1. Intentions and their Measurement.
   2. Complaining/Praising and their Measurement.
   3. Word-of-Mouth and Its Measurement.
6. Loyalty: The Holy Grail.
   1. Definitions of Loyalty.
   2. Loyalty as a Behavior.
   3. Loyalty as an Attitude-Like Concept.
   4. Levels of Loyalty.
   5. Measures of Loyalty.

Chapter 29. Customer Life-Time Value

Chapter 30. International Marketing Research

1. Start with an example
2. Define International Marketing Research (IMR)
3. How is it different from Marketing Research in the domestic environment? (cite an example)
   1. National Differences.
   2. Comparability of tasks.
   3. Centralized vs. Decentralized research.
   4. Sequencing of Research across countries.
   5. Various ways of classifying IMR.
   6. How the IMR information is used in International Marketing decisions.
   7. Big Blunders in IMR.
   8. Issues specific to IMR.
   9. Something about the IMR industry.
4. Discuss the issues in conducting Global Research
   1. What methodology do you use?
   2. Focusing on Focus Groups Overseas.
   3. Is English the universal language for asking questions?
   4. How to avoid seven incompatible studies from seven different countries?
   5. How do you interpret data from one country to another?
   6. Is random sampling feasible?
   7. Global Research calls for Specialists and not Generalists.
   8. How do you deal with Equivalences issues in primary data collection.
   9. How do you deal with the types and sources of bias in Qualitative methods.
   10. Is the global survey process universal?
   11. Can you use the same scales across all countries?
   12. How to avoid the key pitfalls in conducting IMR?
5. Linking the research findings to the defined problem

Chapter 31. Marketing Management Support Systems

1. Introduction
2. Developments in the marketing decision making environment
3. Characteristics and demands of decision makers
4. “Traditional” data-based marketing information and decision support systems
5. New marketing management support systems (including one or two small case studies)
6. Towards a successful implementation and use of marketing management support systems