Marketing Research

Cover
A BRIEF GUIDE TO GETTING THE MOST FROM THIS BOOK
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: Good Food Institute
	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
		Identitying 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: Machu Picchu National Park Survey
	Case 7.2: Advantage Research, Inc.
	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 : 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 Differences Between Means Test with SPSS
	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 Differences Between Means Test with SPSS
	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 z2 Value
		The Chi‐Square Distribution
		How to Interpret a Chi‐Square Result
		Integrated Case Auto Concepts: Analyzing Cross‐Tabulations for Significant Associations by Performing Chi‐Square Analysis with SPSS
	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
Subject Index
Selected Formulas