Description
Book SynopsisMarketing Research: Using Analytics to Develop Market Insights teaches students how to use market research to inform critical business decisions. Offering a practitioner's perspective, thisfully-updated edition covers both marketing research theory and practice to provide students with a comprehensive understanding of the subject. A unique applications-based approachgrounded in the authors' 50 years' combined experience in the marketing research industryfeatures real data, real people, and real research to prepare students for designing, conducting, analyzing, and integrating marketing research in their future business careers.
Already a standard text in marketing research courses, the twelfth edition contains thoroughly revised content that reflects the latest trends, practices, and research in the field. Numerous examples of companies and research firms, such as Twitter, ESPN, Ford, and General Motors, are featured throughout the text to illustrate how marketi
Table of Contents
Preface vii
Acknowledgments ix
1 Steps in Creating Market Insights and the Growing Role of Marketing Analytics 1
Marketing Research and Developing Market Insights 1
Marketing Research Defined 2
Importance of Marketing Research to Management 2
Understanding the Ever-Changing Marketplace 3
Social Media and User-Generated Content 3
Proactive Role of Marketing Research 4
Marketing Analytics Moves to the Forefront 4
The Research Process 4
Recognize the Problem or Opportunity 5
Find Out Why the Information is Being Sought 6
Understand the Decision-Making Environment with Exploratory Research 6
Use the Symptoms to Clarify the Problem 8
Translate the Management Problem into a Marketing Research Problem 9
Determine Whether the Information Already Exists 9
Determine Whether the Question Can Be Answered 10
State the Research Objectives 10
Research Objectives As Hypotheses 11
Marketing Research Process 11
Creating the Research Design 11
Choosing a Basic Method of Research 11
Selecting the Sampling Procedure 13
Collecting the Data 13
Analyzing the Data 13
Presenting the Report 14
Following Up 14
Managing the Research Process 14
The Research Request 14
Request for Proposal 15
The Marketing Research Proposal 16
What to Look for in a Marketing Research Supplier 17
Modifying the Research Process—Marketing Analytics, Big Data, and Unsupervised Learning 17
A Shifting Paradigm 18
What Motivates Decision Makers to Use Research Information? 18
Summary 19
Key Terms 19
Questions for Review & Critical Thinking 20
Working the Net 20
Real-Life Research 1.1: Can Anyone Be a Market Researcher? 21
2 Secondary Data: A Potential Big Data Input 23
Nature of Secondary Data 23
Advantages of Secondary Data 24
Limitations of Secondary Data 25
Internal Databases 27
Creating an Internal Database 27
First, Second, and Third Party Data 27
Behavioral Targeting 28
Big Data 29
The Big Data Breakthrough 29
Making Big Data Actionable in Traditional Marketing Research Environments 30
Battle over Privacy 31
The Federal Trade Commission 32
State Data Privacy Laws 32
The General Data Protection Regulation 32
Summary 33
Key Terms 34
Questions for Review & Critical Thinking 34
Working the Net 34
Real-Life Research 2.1: The GDPR and American Small Business 34
3 Measurement to Build Marketing Insight 36
Measurement Process 36
Step One: Identify the Concept of Interest 37
Step Two: Develop a Construct 38
Step Three: Define the Concept Constitutively 38
Step Four: Define the Concept Operationally 38
Step Five: Develop a Measurement Scale 40
Nominal Level of Measurement 41
Ordinal Level of Measurement 41
Interval Level of Measurement 42
Ratio Level of Measurement 42
Step Six: Evaluate the Reliability and Validity of the Measurement 43
Reliability 45
Validity 47
Reliability and Validity—A Concluding Comment 51
Attitude Measurement Scales 51
Graphic Rating Scales 52
Itemized Rating Scales 53
Traditional One-Stage Format 55
Two-Stage Format 55
Rank-Order Scales 56
Paired Comparisons 56
Constant Sum Scales 56
Semantic Differential Scales 58
Stapel Scales 59
Likert Scales 60
Purchase-Intent Scales 62
Scale Conversions 64
Net Promoter Score (NPS) 65
Considerations in Selecting a Scale 66
The Nature of the Construct Being Measured 66
Type of Scale 67
Balanced versus Nonbalanced Scale 67
Number of Scale Categories 67
Forced versus Nonforced Choice 68
Summary 68
Key Terms 69
Questions for Review & Critical Thinking 70
Working the Net 70
Real-Life Research 3.1: PNC Bank Considers Changing Its Customer Satisfaction Measurement Scale 71
4 Acquiring Data Via a Questionnaire 73
Role of a Questionnaire 73
Criteria for a Good Questionnaire 74
Does It Provide the Necessary Decision-Making Information? 74
Does It Consider the Respondent? 75
Does It Meet Editing Requirements? 75
Does It Solicit Information in an Unbiased Manner: Questionnaire Design Process 76
Step One: Determine Survey Objectives, Resources, and Constraints 77
Step Two: Determine the Data-Collection Method 78
Step Three: Determine the Question Response Format 78
Step Four: Decide on the Question Wording 81
Step Five: Establish Questionnaire Flow and Layout 84
Step Six: Evaluate the Questionnaire 87
Step Seven: Obtain Approval of All Relevant Parties 88
Step Eight: Pretest and Revise 88
Step Nine: Prepare Final Questionnaire Copy 88
Step Ten: Implement the Survey 88
Field Management Companies 89
Avoiding Respondent Fatigue 89
Intelligence Moves Into Questionnaire Coding 90
Conducting Surveys on Smartphones and Tablets 91
The Rapid Growth of Do-It-Yourself (DIY) Surveys 92
Summary 93
Key Terms 94
Questions for Review & Critical Thinking 94
Working the Net 95
Real-Life Research 4.1: Arrow Cleaners 95
5 Sample Design 99
Concept of Sampling 100
Population 100
Sample versus Census 101
Developing a Sampling Plan 101
Step One: Define the Population of Interest 101
Step Two: Choose a Data-Collection Method 104
Step Three: Identify a Sampling Frame 104
Step Four: Select a Sampling Method 104
Step Five: Determine Sample Size 106
Step Six: Develop Operational Procedures for Selecting Sample Elements 106
Step Seven: Execute the Operational Sampling Plan 106
Sampling and Nonsampling Errors 106
Probability Sampling Methods 107
Simple Random Sampling 107
Systematic Sampling 108
Stratified Sampling 109
Cluster Sampling 110
Nonprobability Sampling Methods 111
Convenience Samples 111
Judgment Samples 111
Quota Samples 112
Snowball Samples 112
Internet Sampling 112
Determining Sample Size 113
Determining Sample Size for Probability Samples 113
Budget Available 113
Rule of Thumb 114
Number of Subgroups Analyzed 114
Traditional Statistical Methods 115
Normal Distribution 115
General Properties 115
Basic Concepts 116
Making Inferences on the Basis of a Single Sample 118
Point and Interval Estimates 118
Sampling Distribution of the Proportion 119
Determining Sample Size 120
Problems Involving Means 120
Problems Involving Proportions 122
Determining Sample Size for Stratified and Cluster Samples 123
Sample Size for Qualitative Research 123
Population Size and Sample Size 124
Summary 125
Key Terms 126
Questions for Review & Critical Thinking 126
Working the Net 127
Real-Life Research 5.1: Insights Research Group (IRG) 127
6 Traditional Survey Research 129
Why Decision Makers Like Survey Research 129
Types of Errors in Survey Research 130
Sampling Error 130
Systematic Error 131
Types of Surveys 135
Door-to-Door Interviews 135
Executive Interviews 136
Mall-Intercept Interviews 136
Telephone Interviews 137
Self-Administered Questionnaires 138
Mail Surveys 139
Determination of the Survey Method 141
Sampling Precision 141
Budget 141
Requirements for Respondent Reactions 142
Quality of Data 142
Length of the Questionnaire 142
Incidence Rate 143
Structure of the Questionnaire 143
Time Available to Complete the Survey 143
Summary 144
Key Terms 144
Questions for Review & Critical Thinking 145
Real-Life Research 6.1: Do Consumers Like Chatbots? 145
7 Qualitative Research 146
Nature of Qualitative Research 146
Qualitative Research versus Quantitative Research 147
The Use of Qualitative Research 147
Limitations of Qualitative Research 148
Focus Groups 149
Popularity of Focus Groups 149
Conducting Focus Groups 150
Focus Group Trends 157
Benefits and Drawbacks of Focus Groups 158
Other Qualitative Methodologies 159
Individual Depth Interviews 159
Projective Tests 163
Summary 167
Key Terms 167
Questions for Review & Critical Thinking 167
Working the Net 168
Real-Life Research 7.1: A Sound Approach for the Sound 168
8 Online Marketing Research: The Growth of Mobile and Social Media Research 171
Using the Internet for Secondary Data 172
Online Qualitative Research 172
Online Bulletin Boards 172
Webcam and Streaming Technology Focus Groups 173
Using the Internet to Find Online Participants 174
Online Individual Depth Interviews (IDIs) 175
Online Survey Research 175
Advantages of Online Surveys 175
Disadvantages of Online Surveys 176
Tools for Conducting Online Surveys 177
Commercial Online Panels 178
Panel Recruitment 178
Open Recruitment 178
Closed Recruitment 179
Respondent Participation 179
Panel Management 180
Mobile Internet Research—The Future is Now 180
Advantages of Mobile 181
Designing a Mobile Survey 181
Social Media Marketing Research 182
Summary 182
Key Terms 183
Questions for Review & Critical Thinking 183
Working the Net 183
Real-Life Research 8.1: Shoppers Spending More In-Store Than Online 183
9 Primary Data Collection: Observation 185
Nature of Observation Research 185
Conditions for Using Observation 186
Approaches to Observation Research 186
Advantages of Observation Research 188
Disadvantages of Observation Research 189
Human Observation 189
Ethnographic Research 189
Mobile Ethnography 192
Mystery Shoppers 192
One-Way Mirror Observations 194
Machine Observation 194
Neuromarketing 194
Facial Action Coding Services (FACS) 197
Gender and Age Recognition Systems 199
In-Store Tracking 199
Television and Video Audience Measurement and Tracking 200
Symphony IRI Consumer Network 200
Tracking 201
Magazines Track Online Readers and Apply It Also to Print 201
Social Media Tracking 202
Virtual Reality and Augmented Reality Marketing Research 204
Summary 204
Key Terms 205
Questions for Review & Critical Thinking 205
Working the Net 206
Real-Life Research 9.1: Bausch & Lomb Fine-Tune the Details 206
10 Marketing Analytics 208
What is Marketing Analytics? 209
The Marketing Analytics Process 210
Getting the Data 210
Big Data Sources 210
Data from Traditional Sources 211
Organizing, Merging, and Using Big Data 212
Acting on Results of Analysis 212
Big Data 212
Background on Big Data Issues 212
How Does It Work? 213
Analyzing Data: Descriptive, Predictive, and Prescriptive Analytics 214
Descriptive Analytics 214
Predictive Analytics 214
Prescriptive Analytics 215
Advanced Analytical Methods 216
Data Mining 216
Machine and Deep Learning 219
Artificial Intelligence or AI 220
Data Visualization 224
Infographics 225
Marketing Dashboards 225
Privacy Issues 226
Privacy versus Customization 226
Summary 228
Key Terms 229
Questions for Review & Critical Thinking 229
Working the Net 230
Real-Life Research 10.1: Affiliated Parking Systems Looks to New Pricing Approach 230
11 Primary Data: Experimentation and Test Markets 231
What is an Experiment? 232
Demonstrating Causation 232
Concomitant Variation 233
Appropriate Time Order of Occurrence 233
Elimination of Other Possible Causal Factors 233
Experimental Setting 234
Laboratory Experiments 234
Field Experiments 234
Experimental Validity 234
Experimental Notation 235
Extraneous Variables 235
Examples of Extraneous Variables 236
Controlling Extraneous Variables 237
Experimental Design, Treatment, and Effects 238
Limitations of Experimental Research 239
High Cost 239
Security Issues 239
Implementation Problems 239
Selected Experimental Designs 240
Preexperimental Designs 240
True Experimental Designs 241
Quasi-Experiments 242
Test Markets 244
Types of Test Markets 245
Decision to Conduct Test Marketing 248
Steps in a Test Market Study 249
Summary 252
Key Terms 252
Questions for Review & Critical Thinking 253
Working the Net 254
Real-Life Research 11.1: Los Lobos Beer 254
12 Data Processing and Basic Data Analysis 255
Overview of Data Analysis Procedure for Survey Research 256
Step One: Validation and Editing of Paper Surveys 256
Validation 256
Quality Assurance for Internet Panels 257
Quality Assurance—Respondent Cooperation and Attention Issues 258
Special Issues with Big Data 260
Editing 260
Step Two: Coding 264
Coding Process 265
Automated Coding Systems and Text Processing 266
Intelligent Capture Systems 267
The Data Capture Process 268
Scanning 268
Step Four: Logical Cleaning of Data 269
Step Five: Tabulation and Statistical Analysis 269
One-Way Frequency Tables 269
Cross Tabulations 272
Death of Crosstabs? 274
Graphic Representations of Data 274
Line Charts 275
Pie Charts 275
Bar Charts 275
Descriptive Statistics 278
Measures of Central Tendency 278
Measures of Dispersion 279
Percentages and Statistical Tests 280
Summary 281
Key Terms 281
Questions for Review & Critical Thinking 282
Working the Net 284
Real-Life Research 12.1: Buzzy’s Tacos 284
13 Statistical Testing of Differences and Relationships 285
Evaluating Differences and Changes 286
Statistical Significance 286
Hypothesis Testing 287
Steps in Hypothesis Testing 288
Types of Errors in Hypothesis Testing 290
Accepting H0 versus Failing to Reject (FTR) H0 292
One-Tailed versus Two-Tailed Test 292
Example of Performing a Statistical Test 292
Commonly Used Statistical Hypothesis Tests 295
Independent versus Related Samples 295
Degrees of Freedom 295
Goodness of Fit 296
Chi-Square Test 296
Hypotheses about One Mean 299
t Test 299
Hypotheses about Two Means 300
Hypotheses about Proportions 302
Proportion in One Sample 302
Two Proportions in Independent Samples 303
Analysis of Variance (ANOVA) 305
p Values and Significance Testing 308
Summary 309
Key Terms 309
Questions for Review & Critical Thinking 310
Working the Net 311
Real-Life Research 13.1: Analyzing William D. Scott (WDS) Segmentation Results 312
14 More Powerful Statistical Methods 313
Data Scientist—Hot New Career 313
Bivariate Statistical Analysis 314
Bivariate Analysis of Relationships 314
Bivariate Regression 314
Nature of the Relationship 315
Example of Bivariate Regression 316
Correlation for Metric Data: Pearson’s Product–Moment Correlation 322
Multivariate Analysis Procedures 323
Multivariate Software 324
Multiple Regression Analysis 324
Applications of Multiple Regression Analysis 325
Multiple Regression Analysis Measures 326
Dummy Variables 327
Potential Use and Interpretation Problems 327
Multiple Discriminant Analysis 328
Applications of Multiple Discriminant Analysis 329
Cluster Analysis 330
Procedures for Clustering 330
Applications of Cluster Analysis 331
Factor Analysis 332
Factor Scores 332
Factor Loadings 334
Naming Factors 334
Number of Factors to Retain 335
Conjoint Analysis 335
Simulating Buyer Choice 335
Limitations of Conjoint Analysis 336
Neural Networks 337
Description of a Neural Network 337
How Neural Networks “Learn” 338
When Neural Networks Are Appropriate 338
Limitations of Neural Networks 338
Predictive Analytics 339
Using Predictive Analytics 339
Privacy Concerns and Ethics 341
Commercial Predictive Modeling Software and Applications 341
Summary 341
Key Terms 342
Questions for Review & Critical Thinking 343
Working the Net 345
Real-Life Research 14.1: Satisfaction Research for Pizza Pronto 345
15 Communicating Analytics and Research Insights 347
The Research Report 347
Organizing the Report 348
Format of the Report 349
Formulating Recommendations 349
Presenting the Results 355
Making a Presentation 356
Infographics 356
Presentations by Internet 358
Summary 358
Key Terms 359
Questions for Review & Critical Thinking 359
Working the Net 359
Real-Life Research 15.1: TouchWell Storefront Concept and Naming Research 359
Appendix A A-1
[Appendix B and C are available online at www.wiley.com/go/mcdaniel/marketingresearch12e]
Endnotes N-1
Glossary G-1
QSR Survey QS-1
Index I-1
