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