Efnisyfirlit

• Title Page
• Copyright Page
• Brief Contents
• Contents
• Preface
• Chapter 1 Introduction to Marketing Research
  • 1‐1 Marketing Research Is Part of Marketing
    • The Philosophy of the Marketing Concept Guides Managers’ Decisions
    • Creating the “Right” Marketing Strategy
  • 1‐2 What Is Marketing Research?
    • Is it Marketing Research or Market Research?
    • The Function of Marketing Research
  • 1‐3 What Are the Uses of ‐Marketing Research?
    • Identifying Market Opportunities and Problems
    • Generating, Refining, and Evaluating Potential Marketing Actions
      • Selecting Target Markets
      • Product Research
      • Pricing Research
      • Promotion Research
      • Distribution Research
    • Monitoring Marketing Performance
    • Improving Marketing as a Process
    • Marketing Research Is Sometimes Wrong
  • 1‐4 The Marketing Information System
    • Components of an MIS
      • Internal Reports System
      • Marketing Intelligence System
      • Marketing Decision Support System (DSS)
      • Marketing Research System
  • 1‐5 Job Skills
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 1.1 Starbucks and Tea Sales
  • Case 1.2 Integrated Case: Auto Concepts
  • Endnotes
• Chapter 2 The Marketing Research Industry
  • 2‐1 Evolution of an Industry
    • Earliest Known Studies
    • Why Did the Industry Grow?
    • The 20th Century Led to a “Mature Industry”
    • Marketing Research in the 21st Century
  • 2‐2 Who Conducts Marketing Research?
    • Client‐Side Marketing Research
    • Supply‐Side Marketing Research
  • 2‐3 The Industry Structure
    • Firm Size by Revenue
    • Types of Firms and Their Specialties
    • Industry Performance
  • 2‐4 Challenges to the Marketing Research Industry
    • The Need to Incorporate Innovative and Evolving Sources of Data and Methods
    • The Need to Effectively Communicate Insights
    • The Need to Hire Talented and Skilled Employees
  • 2‐5 Industry Initiatives
    • Best Practices
    • Maintaining Public Credibility of Research
    • Monitoring Industry Trends
    • Improving Ethical Conduct
  • 2‐6 Industry Standards and Ethics
    • Certification of Qualified Research Professionals
    • Continuing Education
  • 2‐7 A Career in Marketing Research
    • Where You’ve Been and Where You’re Headed!
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 2.1 Pinnacle Research
  • Endnotes
• Chapter 3 The Marketing Research Process and Defining the Problem and Research Objectives
  • 3‐1 The Marketing Research Process
    • The 11‐Step Process
    • Caveats to a Step‐by‐Step Process
      • Why 11 Steps?
      • Not All Studies Use All 11 Steps
      • Steps Are Not Always Followed in Order
    • Introducing “Where We Are”
    • Step 1: Establish the Need for Marketing Research
      • The Information Is Already Available
      • The Timing Is Wrong
      • Costs Outweigh the Value
    • Step 2: Define the Problem
    • Step 3: Establish Research Objectives
    • Step 4: Determine Research Design
    • Step 5: Identify Information Types and Sources
    • Step 6: Determine Methods of Accessing Data
    • Step 7: Design Data Collection Forms
    • Step 8: Determine the Sample Plan and Size
    • Step 9: Collect Data
    • Step 10: Analyze Data
    • Step 11: Communicate the Insights
  • 3‐2 Defining the Problem
    • 1. Recognize the Problem
      • Failure to Meet an Objective
      • Identification of an Opportunity
    • 2. Understand the Background of the Problem
      • Conduct a Situation Analysis
      • Clarify the Symptoms
      • Determine the Probable Causes of the Symptom(s)
    • 3. Determine the Decision Alternatives
    • 4. Formulate the Problem Statement
  • 3‐3 Research Objectives
    • Using Hypotheses
    • Defining Constructs
  • 3‐4 Action Standards
    • Impediments to Problem Definition
  • 3‐5 The Marketing Research Proposal
    • Ethical Issues and the Research Proposal
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 3.1 Aging Population in Malaysia
  • Case 3.2 Integrated Case: Auto Concepts
  • Endnotes
• Chapter 4 Research Design
  • 4‐1 Research Design
    • Why Is Knowledge of Research Design Important?
  • 4‐2 Three Types of Research Design
    • Research Design: A Caution
  • 4‐3 Exploratory Research
    • Uses of Exploratory Research
      • Gain Background Information
      • Define Terms
      • Clarify Problems and Hypotheses
      • Establish Research Priorities
    • Methods of Conducting Exploratory Research
      • Secondary Data Analysis
      • Experience Surveys
      • Case Analysis
      • Focus Groups
  • 4‐4 Descriptive Research
    • Classification of Descriptive Research Studies
  • 4‐5 Causal Research
    • Experiments
    • Experimental Design
      • Before‐After Testing
      • A/B Testing
    • How Valid Are Experiments?
    • Types of Experiments
  • 4‐6 Test Marketing
    • Types of Test Markets
      • Standard Test Market
      • Controlled Test Markets
      • Simulated Test Markets
    • Selecting Test‐Market Regions
    • Pros and Cons of Test Marketing
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 4.1 Memos from a Researcher
  • Case 4.2 Analysis of Coffee Segments with Nielsen Panel Data
  • Endnotes
• Chapter 5 Secondary Data and Packaged Information
  • 5‐1 Big Data
  • 5‐2 Primary Versus Secondary Data
    • Uses of Secondary Data
  • 5‐3 Classification of Secondary Data
    • Internal Secondary Data
    • External Secondary Data
      • Published Sources
      • Official Statistics
      • Data Aggregators
  • 5‐4 Advantages and Disadvantages of Secondary Data
    • Advantages of Secondary Data
    • Disadvantages of Secondary Data
      • Incompatible Reporting Units
      • Mismatched Measurement Units
      • Unusable Class Definitions
      • Outdated Data
  • 5‐5 Evaluating Secondary Data
    • What Was the Purpose of the Study?
    • Who Collected the Information?
    • What Information Was Collected?
    • How Was the Information Obtained?
    • How Consistent Is the Information with Other Information?
  • 5‐6 What Is Packaged Information?
    • Syndicated Data
    • Packaged Services
  • 5‐7 Advantages and Disadvantages of Packaged Information
    • Syndicated Data
    • Packaged Services
  • 5‐8 Applications of Packaged Information
    • Measuring Consumer Attitudes and Opinions
    • Identifying Segments
    • Monitoring Media Usage and Promotion Effectiveness
    • Tracking Sales
  • 5‐9 Digital Tracking Data
  • 5‐10 Social Media Data
    • Types of Social Media Information
      • Reviews
      • Tips
      • New Uses
      • Competitor News
    • Advantages and Disadvantages of Social Media Data
    • Tools to Monitor Social Media
  • 5‐11 Internet of Things
  • 5‐12 Big Data and Ethics
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 5.1 The Men’s Market for Athleisure
  • Case 5.2 Analyzing the Coffee Category with POS ‐Syndicated Data
  • Endnotes
• Chapter 6 Qualitative Research Techniques
  • 6‐1 Quantitative, Qualitative, and Mixed Methods Research
    • Types of Mixed Methods
  • 6‐2 Observation Techniques
    • Types of Observation
      • Direct Versus Indirect
      • Covert Versus Overt
      • Structured Versus Unstructured
      • In Situ Versus Invented
    • Appropriate Conditions for the Use of Observation
    • Advantages of Observational Data
    • Limitations of Observational Data
  • 6‐3 Focus Groups
    • How Focus Groups Work
    • Online Focus Groups
    • Operational Aspects of Traditional Focus Groups
      • How Many People Should Be in a Focus Group?
      • Who Should Be in the Focus Group?
      • How Many Focus Groups Should Be Conducted?
      • How Should Focus Group Participants Be Recruited and Selected?
      • Where Should a Focus Group Meet?
      • When Should the Moderator Become Involved in the Research Project?
      • How Are Focus Group Results Used?
      • What Other Benefits Do Focus Groups Offer?
    • Advantages of Focus Groups
    • Disadvantages of Focus Groups
    • When Should Focus Groups Be Used?
    • When Should Focus Groups Not Be Used?
    • Some Objectives of Focus Groups
  • 6‐4 Ethnographic Research
    • Mobile Ethnography
    • Netnography
  • 6‐5 Marketing Research Online Communities
  • 6‐6 Other Qualitative Research Techniques
    • In‐Depth Interviews
    • Protocol Analysis
    • Projective Techniques
      • Word‐Association Test
      • Sentence‐Completion Test
      • Picture Test
      • Cartoon or Balloon Test
      • Role‐Playing Activity
    • Neuromarketing
      • Neuroimaging
      • Eye Tracking
      • Facial Coding
      • The Controversy
    • Still More Qualitative Techniques
  • 6‐7 The Analysis of Qualitative Data
    • Steps for Analyzing Qualitative Data
    • Using Electronic Tools to Analyze Qualitative Data
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 6.1 Mumuni Advertising Agency
  • Case 6.2 Integrated Case: Auto Concepts
  • Endnotes
• Chapter 7 Evaluating Survey Data Collection Methods
  • 7‐1 Advantages of Surveys
  • 7‐2 Modes of Data Collection
    • Data Collection and Impact of Technology
    • Person‐Administered Surveys
      • Advantages of Person‐Administered Surveys
      • Disadvantages of Person‐Administered Surveys
    • Computer‐Assisted, Person‐Administered Surveys
      • Advantages of Computer‐Assisted Surveys
      • Disadvantages of Computer‐Assisted Surveys
    • Self‐Administered Surveys
      • Advantages of Self‐Administered Surveys
      • Disadvantages of Self‐Administered Surveys
    • Computer‐Administered Surveys
      • Advantages of Computer‐Administered Surveys
      • Disadvantage of Computer‐Administered Surveys
    • Mixed‐Mode Surveys
      • Advantage of Mixed‐Mode Surveys
      • Disadvantages of Mixed‐Mode Surveys
  • 7‐3 Descriptions of Data Collection Methods
    • Person‐Administered/Computer‐Assisted Interviews
      • In‐Home Surveys
      • Mall‐Intercept Surveys
      • In‐Office Surveys
      • Telephone Surveys
    • Computer‐Administered Interviews
      • Fully Automated Survey
      • Online Surveys
    • Self‐Administered Surveys (Without Computer Presence)
      • Group Self‐Administered Survey
      • Drop‐Off Survey
      • Mail Survey
  • 7‐4 Working with a Panel Company
    • Advantages of Using a Panel Company
      • Fast Turnaround
      • High Quality
      • Database Information
      • Targeted Respondents
      • Integrated Features
    • Disadvantages of Using a Panel Company
      • Not Random Samples
      • Overused Respondents
      • Cost
    • Top Panel Companies
  • 7‐5 Choosing the Survey Method
    • How Fast Is the Data Collection?
    • How Much Does the Data Collection Cost?
    • How Good Is the Data Quality?
    • Other Considerations
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 7.1 Whale Watching Tourism in Australia
  • Case 7.2 Food Waste Research
  • Endnotes
• Chapter 8 Understanding Measurement, Developing Questions, and Designing the Questionnaire
  • 8‐1 Basic Measurement Concepts
  • 8‐2 Types of Measures
    • Nominal Measures
    • Ordinal Measures
    • Scale Measures
  • 8‐3 Interval Scales Commonly Used in Marketing Research
    • The Likert Scale
    • The Semantic Differential Scale
    • The Stapel Scale
    • Slider Scales
    • Two Issues with Interval Scales Used in Marketing Research
    • The Scale Should Fit the Construct
  • 8‐4 Reliability and Validity of Measurements
  • 8‐5 Designing a Questionnaire
    • The Questionnaire Design Process
  • 8‐6 Developing Questions
    • Four Do’s of Question Wording
      • The Question Should Be Focused on a Single Issue or Topic
      • The Question Should Be Brief
      • The Question Should Be Grammatically Simple
      • The Question Should Be Crystal Clear
    • Four Do Not’s of Question Wording
      • Do Not “Lead” the Respondent to a Particular Answer
      • Do Not Use “Loaded” Wording or Phrasing
      • Do Not Use a “Double‐Barreled” Question
      • Do Not Use Words That Overstate the Case
  • 8‐7 Questionnaire Organization
    • The Introduction
      • Who Is Doing the Survey?
      • What Is the Survey About?
      • How Did You Select Me?
      • Motivate Me to Participate
      • Am I Qualified to Take Part?
    • Question Flow
  • 8‐8 Computer‐Assisted Questionnaire Design
    • Question Creation
    • Skip and Display Logic
    • Data Collection and Creation of Data Files
    • Ready‐Made Respondents
    • Data Analysis, Graphs, and Downloading Data
  • 8‐9 Finalize the Questionnaire
    • Coding the Questionnaire
    • Pretesting the Questionnaire
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 8.1 Extreme Exposure Rock Climbing Center Faces The Krag
  • Case 8.2 Integrated Case: Auto Concepts
  • Endnotes
• Chapter 9 Selecting the Sample
  • 9‐1 Basic Concepts in Samples and Sampling
    • Population
    • Census
    • Sample and Sample Unit
    • Sample Frame and Sample Frame Error
    • Sampling Error
  • 9‐2 Why Take a Sample?
  • 9‐3 Probability Versus Nonprobability Sampling Methods
  • 9‐4 Probability Sampling Methods
    • Simple Random Sampling
      • The Random Device Method
      • The Random Numbers Method
      • Advantages and Disadvantages of Simple Random Sampling
      • Simple Random Sampling Used In Practice
    • Systematic Sampling
      • Why Systematic Sampling Is “Fair”
      • Disadvantage of Systematic Sampling
    • Cluster Sampling
      • Area Sampling as a Form of Cluster Sampling
      • Disadvantage of Cluster (Area) Sampling
    • Stratified Sampling
      • Working with Skewed Populations
      • Accuracy of Stratified Sampling
      • How to Apply Stratified Sampling
  • 9‐5 Nonprobability Sampling Methods
    • Convenience Samples
    • Chain Referral Samples
    • Purposive Samples
    • Quota Samples
  • 9‐6 Online Sampling Techniques
    • Online Panel Samples
    • River Samples
    • Email List Samples
  • 9‐7 Developing a Sample Plan
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 9.1 Peaceful Valley Subdivision: Trouble in Suburbia
  • Case 9.2 Jet’s Pets
  • Endnotes
• Chapter 10 Determining the Size of a Sample
  • 10‐1 Sample Size Axioms
  • 10‐2 The Confidence Interval Method of Determining Sample Size
    • Sample Size and Accuracy
    • P and Q: The Concept of Variability
    • The Concept of a Confidence Interval
    • How Population Size (N) Affects Sample Size
  • 10‐3 The Sample Size Formula
    • Determining Sample Size via the Confidence Interval Formula
      • Variability: p X q
      • Acceptable Margin of Sample Error: e
      • Level of Confidence: z
  • 10‐4 Practical Considerations in Sample Size Determination
    • How to Estimate Variability in the Population
    • How to Determine the Amount of Acceptable Sample Error
    • How to Decide on the Level of Confidence
    • How to Balance Sample Size with the Cost of Data Collection
  • 10‐5 Other Methods of Sample Size Determination
    • Arbitrary “Percent Rule of Thumb” Sample Size
    • Conventional Sample Size Specification
    • “Credibility Interval” Approach to Sample Size
    • Statistical Analysis Requirements in Sample Size Specification
    • Cost Basis of Sample Size Specification
  • 10‐6 Three Special Sample Size Determination Situations
    • Sampling from Small Populations
    • Sample Size Using Nonprobability Sampling
    • Sampling from Panels
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 10.1 Target: Deciding on the Number of Telephone Numbers
  • Case 10.2 Bounty Paper Towels
  • Endnotes
• Chapter 11 Dealing with Fieldwork and Data Quality Issues
  • 11‐1 Data Collection and Nonsampling Error
  • 11‐2 Possible Errors in Field Data Collection
    • Intentional Fieldworker Errors
    • Unintentional Fieldworker Errors
    • Intentional Respondent Errors
    • Unintentional Respondent Errors
  • 11‐3 Field Data Collection Quality Controls
    • Control of Intentional Fieldworker Error
    • Control of Unintentional Fieldworker Error
    • Control of Intentional Respondent Error
    • Control of Unintentional Respondent Error
    • Final Comment on the Control of Data Collection Errors
  • 11‐4 Nonresponse Error
    • Refusals to Participate in the Survey
    • Break‐Offs During the Interview
    • Refusals to Answer Specific Questions (Item Omission)
    • What Is a Completed Interview?
    • Measuring Response Rate in Surveys
  • 11‐5 Ways Panel Companies Control Error
  • 11‐6 Dataset, Coding Data, and the Data Code Book
  • 11‐7 Data Quality Issues
    • What to Look for in Raw Data Inspection
      • Incomplete Response
      • Nonresponses to Specific Questions (Item Omissions)
      • Yea‐ or Nay‐Saying Patterns
      • Middle‐of‐the‐Road Patterns
      • Other Data Quality Problems
      • How to Handle Data Quality Issues
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 11.1 Alert! Squirt
  • Case 11.2 Sony Televisions LED 4K Ultra HD HDR Smart TV Survey
  • Endnotes
• Chapter 12 Using Descriptive Analysis, Performing Population Estimates, and Testing Hypotheses
  • 12‐1 Types of Statistical Analyses Used in Marketing Research
    • Descriptive Analysis
    • Inference Analysis
    • Difference Analysis
    • Association Analysis
    • Relationships Analysis
  • 12‐2 Understanding Descriptive Analysis
    • Measures of Central Tendency: Summarizing the “Typical” Respondent
      • Mode
      • Median
      • Mean
    • Measures of Variability: Relating the Diversity of Respondents
      • Frequency and Percentage Distribution
      • Range
      • Standard Deviation
  • 12‐3 When to Use Each Descriptive Analysis Measure
  • 12‐4 The Auto Concepts Survey: Obtaining Descriptive Statistics with SPSS
    • Integrated Case The Auto Concepts Survey: Obtaining Descriptive Statistics with SPSS
    • Use SPSS to Open Up and Use the Auto Concepts Dataset
    • Obtaining a Frequency Distribution and the Mode with SPSS
    • Finding the Median with SPSS
    • Finding the Mean, Range, and Standard Deviation with SPSS
  • 12‐5 Reporting Descriptive Statistics to Clients
    • Reporting Scale Data (Ratio and Interval Scales)
    • Reporting Nominal or Categorical Data
  • 12‐6 Statistical Inference: Sample Statistics and Population Parameters
  • 12‐7 Parameter Estimation: Estimating the Population Percentage or Mean
    • Sample Statistic
    • Standard Error
    • Confidence Interval
    • How to Interpret an Estimated Population Mean or Percentage Range
  • 12‐8 The Auto Concepts Survey: How to Obtain and Use a Confidence Interval for a Mean with SPSS
  • 12‐9 Reporting Confidence Intervals to Clients
  • 12‐10 Hypothesis Tests
    • Test of the Hypothesized Population Parameter Value
    • Auto Concepts: How to Use SPSS to Test a Hypothesis for a Mean
  • 12‐11 Reporting Hypothesis Tests to Clients
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 12.1 L’Experience Restaurant Survey Descriptive and ‐Inference Analysis
  • Case 12.2 Integrated Case: Auto Concepts Descriptive and ‐Inference Analysis
  • Endnotes
• Chapter 13 Implementing Basic Differences Tests
  • 13‐1 Why Differences Are Important
  • 13‐2 Small Sample Sizes: The Use of a t Test or z Test and How SPSS Eliminates the Worry
  • 13‐3 Testing for Significant Differences Between Two Groups
    • Differences Between Percentages with Two Groups (Independent Samples)
    • How to Use SPSS for Differences Between Percentages of Two Groups
    • Differences Between Means with Two Groups (Independent Samples)
    • Integrated Case The Auto Concepts Survey: How to Perform an Independent Sample Significance of Dif
  • 13‐4 Testing for Significant Differences in Means Among More Than Two Groups: Analysis of Variance
    • Basics of Analysis of Variance
    • Post Hoc Tests: Detect Statistically Significant Differences Among Group Means
    • Integrated Case Auto Concepts: How to Run Analysis of Variance on SPSS
    • Interpreting ANOVA (Analysis of Variance)
  • 13‐5 Reporting Group Differences Tests to Clients
  • 13‐6 Differences Between Two Means Within the Same Sample (Paired Sample)
    • Integrated Case The Auto Concepts Survey: How to Perform a Paired Samples t Test Significance of D
  • 13‐7 Null Hypotheses for Differences Tests Summary
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 13.1 L’Experience Restaurant Survey Differences Analysis
  • Case 13.2 Integrated Case: The Auto Concepts Survey ‐Differences Analysis
  • Endnotes
• Chapter 14 Making Use of Associations Tests
  • 14‐1 Types of Relationships (Associations) Between Two Variables
    • Linear and Curvilinear Relationships
    • Monotonic Relationships
    • Nonmonotonic Relationships
  • 14‐2 Characterizing Relationships Between Variables
    • Presence
    • Pattern
    • Strength of Association
  • 14‐3 Correlation Coefficients and Covariation
    • Rules of Thumb for Correlation Strength
    • The Correlation Sign: The Direction of the Relationship
    • Visualizing Covariation using Scatter Diagrams
  • 14‐4 The Pearson Product Moment Correlation Coefficient
    • Integrated Case Auto Concepts: How to Obtain Pearson Product Moment Correlation(s) with SPSS
  • 14‐5 Reporting Correlation Findings to Clients
  • 14‐6 Cross‐Tabulations
    • Cross‐Tabulation Analysis
    • Types of Frequencies and Percentages in a Cross‐Tabulation Table
  • 14‐7 Chi‐Square Analysis
    • Observed and Expected Frequencies
    • The Computed x2 Value
    • The Chi‐Square Distribution
    • How to Interpret a Chi‐Square Result
    • Integrated Case Auto Concepts: Analyzing Cross‐Tabulations for Significant Associations by Perfo
  • 14‐8 Chi‐Square Test of Proportions: A Useful Variation of Cross‐Tabulation Analysis
  • 14‐9 Communicating Cross‐Tabulation Insights to Clients: Use Data Visualization
  • 14‐10 Special Considerations In Association Procedures
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 14.1 L’Experience Restaurant Survey Associative Analysis
  • Case 14.2 Integrated Case: The Auto Concepts Survey Associative Analysis
  • Endnotes
• Chapter 15 Understanding Regression Analysis Basics
  • 15‐1 Bivariate Linear Regression Analysis
    • Basic Concepts in Regression Analysis
      • Independent and Dependent Variables
      • Computing the Slope and the Intercept
    • How to Improve a Regression Analysis Finding
  • 15‐2 Multiple Regression Analysis
    • An Underlying Conceptual Model
    • Multiple Regression Analysis Described
      • Basic Assumptions in Multiple Regression
    • Integrated Case Auto Concepts: How to Run and Interpret Multiple Regression Analysis on SPSS
    • “Trimming” the Regression for Significant Findings
  • 15‐3 Special Uses of Multiple Regression Analysis
    • Using a “Dummy” Independent Variable
    • Using Standardized Betas to Compare the Importance of ‐Independent Variables
    • Using Multiple Regression as a Screening Device
    • Interpreting the Findings of Multiple Regression Analysis
  • 15‐4 Stepwise Multiple Regression
    • How to Do Stepwise Multiple Regression with SPSS
    • Step‐by‐Step Summary of How to Perform Multiple Regression Analysis
  • 15‐5 Warnings Regarding Multiple Regression Analysis
  • 15‐6 Communicating Regression Analysis Insights to Clients
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 15.1 L’Experience Restaurant Survey Regression Analysis
  • Case 15.2 Integrated Case: Auto Concepts Segmentation Analysis
  • Endnotes
• Chapter 16 Communicating Insights
  • Use Effective Communication Methods
  • Communicate Actionable, Data‐Supported Strategies
  • Disseminate Insights Throughout the Organization
  • 16‐1 Characteristics of Effective Communication
    • Accuracy
    • Clarity
    • Memorability
    • Actionability
    • Style
  • 16‐2 Avoid Plagiarism!
  • 16‐3 Videos, Infographics, and Immersion Techniques
    • Videos
    • Infographics
    • Immersion Techniques
  • 16‐4 The Traditional Marketing Research Report
  • 16‐5 Know Your Audience
  • 16‐6 Elements of the Marketing Research Report
    • Front Matter
      • Title Page
      • Letter of Authorization
      • Letter/Memo of Transmittal
      • Table of Contents
      • List of Illustrations
      • Abstract/Executive Summary
    • Body
      • Introduction
      • Research Objectives
      • Method
      • Method or Methodology?
      • Results
      • Limitations
      • Conclusions and Recommendations
    • End Matter
  • 16‐7 Guidelines and Principles for the Written Report
    • Headings and Subheadings
    • Visuals
    • Style
  • 16‐8 Using Visuals: Tables and Figures
    • Tables
    • Pie Charts
    • Bar Charts
    • Line Graphs
    • Flow Diagrams
    • Producing an Appropriate Visual
  • 16‐9 Presenting Your Research Orally
  • 16‐10 Data Visualization Tools and Dashboards
  • 16‐11 Disseminating Insights Throughout an Organization
  • Summary
  • Key Terms
  • Review Questions/Applications
  • Case 16.1 Integrated Case: Auto Concepts: Report Writing
  • Case 16.2 Integrated Case: Auto Concepts: Making a PowerPoint Presentation
  • Case 16.3 How Marketing Research Data Can Begin with a Sketch
  • Endnotes
• Name Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • V
  • W
  • X
  • Y
  • Z
• Subject Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z
• Selected Formulas

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