Marketing Research

Preface x

About the authors xi

Chapter 1

Steps in creating market insights and the growing role of marketing analytics 1

Marketing research and developing market insights 2

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 3

Marketing analytics moves to the forefront 4

The research process 4

Recognise 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 9

State the research objectives 10

Research objectives as hypotheses 10

Marketing research process 10

Creating the research design 11

Choosing a basic method of research 11

Selecting the sampling procedure 11

Collecting the data 12

Analysing the data 12

Presenting the report 12

Following up 12

Managing the research process 13

The research request 13

Request for proposal 13

The marketing research proposal 14

What to look for in a marketing research supplier 14

Modifying the research process — marketing analytics, big data and unsupervised learning 15

A shifting paradigm 15

What motivates decision makers to use research information? 16

Summary 17

Key terms 18

Questions for review and critical thinking 19

Working the net 19

Real-life research 20

Endnotes 20

Acknowledgements 21

Appendix 1A: A marketing research proposal 22

Chapter 2

Secondary data: A potential big data input 25

Nature of secondary data 26

Advantages of secondary data 26

Limitations of secondary data 27

Internal databases 28

Creating an internal database 28

First-, second- and third-party data 29

Behavioural targeting 29

Big data 30

The big data breakthrough 30

Making big data actionable in traditional marketing research environments 31

Battle over privacy 32

The Office of the Australian Information Commissioner 32

The General Data Protection Regulation 33

Summary 34

Key terms 34

Questions for review and critical thinking 35

Working the net 35

Real-life research 35

Endnotes 36

Chapter 3

Measurement to build marketing insight 37

Measurement process 38

Step one: Identify the concept of interest 38

Step two: Develop a construct 38

Step three: Define the concept constitutively 39

Step four: Define the concept operationally 39

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 44

Validity 46

Reliability and validity — a concluding comment 49

Attitude measurement scales 50

Graphic rating scales 50

Itemised rating scales 51

Traditional one-stage format 54

Two-stage format 54

Rank-order scales 54

Paired comparisons 55

Constant sum scales 56

Semantic differential scales 56

Stapel scales 57

Likert scales 57

Purchase-intent scales 60

Scale conversions 62

Net promoter score 63

Considerations in selecting a scale 64

The nature of the construct being measured 64

Type of scale 64

Balanced versus nonbalanced scale 65

Number of scale categories 65

Forced versus nonforced choice 65

Summary 66

Key terms 67

Questions for review and critical thinking 68

Working the net 69

Real-life research 69

Endnotes 70

Acknowledgements 71

Chapter 4

Acquiring data via a questionnaire 73

Role of a questionnaire 74

Criteria for a good questionnaire 75

Does it provide the necessary decision-making information? 75

Does it consider the respondent? 75

Does it meet editing requirements? 76

Does it solicit information in an unbiased manner: Questionnaire design process 77

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 80

Step five: Establish questionnaire flow and layout 83

Step six: Evaluate the questionnaire 85

Step seven: Obtain approval of all relevant parties 86

Step eight: Pretest and revise 86

Step nine: Prepare final questionnaire copy 86

Step ten: Implement the survey 86

Field management companies 86

Avoiding respondent fatigue 87

Intelligence moves into questionnaire coding 87

Conducting surveys on smartphones and tablets 89

The rapid growth of DIY surveys 89

Summary 91

Key terms 91

Questions for review and critical thinking 92

Working the net 93

Real-life research 93

Endnotes 93

Acknowledgements 94

Chapter 5

Sample design 95

Concept of sampling 96

Key sampling method issues and errors in 2019

polling 96

Population 96

Sample versus census 97

Developing a sampling plan 98

Step one: Define the population of interest 98

Step two: Choose a data-collection method 100

Step three: Identify a sampling frame 100

Step four: Select a sampling method 100

Step five: Determine sample size 102

Step six: Develop operational procedures for selecting sample elements 102

Step seven: Execute the operational sampling plan 102

Sampling and nonsampling errors 102

Probability sampling methods 103

Simple random sampling 103

Systematic sampling 103

Stratified sampling 104

Cluster sampling 105

Nonprobability sampling methods 105

Convenience samples 106

Judgement samples 106

Quota samples 106

Snowball samples 106

Internet sampling 107

Determining sample size 107

Determining sample size for probability samples 107

Budget available 108

Rule of thumb 108

Number of subgroups analysed 108

Traditional statistical methods 108

Normal distribution 109

General properties 109

Basic concepts 110

Making inferences on the basis of a single sample 111

Point and interval estimates 111

Sampling distribution of the proportion 112

Determining sample size 113

Problems involving means 113

Problems involving proportions 115

Determining sample size for stratified and cluster samples 115

Sample size for qualitative research 115

Population size and sample size 116

Summary 117

Key terms 118

Questions for review and critical thinking 119

Working the net 120

Real-life research 120

Endnotes 121

Acknowledgements 122

Chapter 6

Traditional survey research 123

Why decision makers like survey research 124

Types of errors in survey research 124

Sampling error 125

Systematic error 125

Types of surveys 129

Door-to-door interviews 129

Executive interviews 130

Mall-intercept interviews 130

Telephone interviews 131

Self-administered questionnaires 132

Mail surveys 132

Determination of the survey method 134

Sampling precision 134

Budget 134

Requirements for respondent reactions 134

Quality of data 135

Length of the questionnaire 135

Incidence rate 135

Structure of the questionnaire 135

Time available to complete the survey 136

Summary 137

Key terms 137

Questions for review and critical thinking 138

Real-life research 139

Endnotes 139

Acknowledgements 140

Chapter 7

Qualitative research 141

Nature of qualitative research 142

Qualitative research versus quantitative research 142

The use of qualitative research 142

Limitations of qualitative research 143

Focus groups 143

Popularity of focus groups 144

Conducting focus groups 144

Focus group trends 150

Benefits and drawbacks of focus groups 151

Other qualitative methodologies 152

Individual depth interviews 152

Projective tests 155

Summary 159

Key terms 159

Questions for review and critical thinking 160

Working the net 160

Real-life research 160

Endnotes 161

Acknowledgements 162

Chapter 8

Online marketing research: The growth of mobile and social media research 163

Using the internet for secondary data 164

Online qualitative research 164

Online bulletin boards 164

Webcam and streaming technology focus groups 165

Using the internet to find online participants 165

Online individual depth interviews (IDIs) 166

Online survey research 166

Advantages of online surveys 166

Disadvantages of online surveys 167

Tools for conducting online surveys 168

Commercial online panels 168

Panel recruitment 169

Open recruitment 169

Closed recruitment 169

Respondent participation 170

Panel management 171

Mobile internet research — the future is now 171

Advantages of mobile 171

Designing a mobile survey 172

Social media marketing research 172

Summary 173

Key terms 173

Questions for review and critical thinking 174

Working the net 174

Real-life research 174

Endnotes 175

Acknowledgements 175

Chapter 9

Primary data collection: Observation 177

Nature of observation research 178

Conditions for using observation 178

Approaches to observation research 178

Advantages of observation research 180

Disadvantages of observation research 180

Human observation 181

Ethnographic research 181

Mobile ethnography 183

Mystery shoppers 183

One-way mirror observations 184

Machine observation 185

Neuromarketing 185

Facial action coding services (FACS) 187

Gender and age recognition systems 189

In-store tracking 189

Television and video audience measurement and tracking 190

Tracking 191

Magazines track online readers and apply it also to print 191

Social media tracking 192

Virtual reality and augmented reality marketing research 194

Summary 195

Key terms 195

Questions for review and critical thinking 196

Working the net 197

Real-life research 197

Endnotes 198

Acknowledgements 199

Chapter 10

Marketing analytics 201

What is marketing analytics? 202

The marketing analytics process 203

Getting the data 203

Big data sources 203

Data from traditional sources 204

Organising, merging and using big data 204

Acting on results of analysis 204

Big data 205

Background on big data issues 205

How does it work? 205

Analysing data: Descriptive, predictive and prescriptive analytics 206

Descriptive analytics 206

Predictive analytics 206

Prescriptive analytics 207

Advanced analytical methods 208

Data mining 208

Machine and deep learning 211

Artificial intelligence or AI 211

Data visualisation 215

Infographics 215

Marketing dashboards 216

Privacy issues 216

Privacy versus customisation 216

Summary 219

Key terms 219

Questions for review and critical thinking 220

Working the net 220

Real-life research 221

Endnotes 221

Acknowledgement 223

Chapter 11

Primary data: Experimentation and test markets 225

What is an experiment? 226

Demonstrating causation 226

Concomitant variation 227

Appropriate time order of occurrence 227

Elimination of other possible causal factors 227

Experimental setting 227

Laboratory experiments 227

Field experiments 228

Experimental validity 228

Experimental notation 228

Extraneous variables 229

Examples of extraneous variables 229

Controlling extraneous variables 230

Experimental design, treatment and effects 230

Limitations of experimental research 231

High cost 231

Security issues 231

Implementation problems 231

Selected experimental designs 232

Pre-experimental designs 232

True experimental designs 233

Quasi-experiments 233

Test markets 235

Types of test markets 236

Decision to conduct test marketing 238

Steps in a test market study 239

Summary 242

Key terms 242

Questions for review and critical thinking 244

Working the net 244

Real-life research 245

Endnotes 245

Acknowledgements 246

Chapter 12

Data processing and basic data analysis 247

Overview of data analysis procedure for survey research 248

Step one: Validation and editing of paper surveys 248

Validation 248

Quality assurance for online panels 249

Quality assurance — respondent cooperation and attention issues 250

Special issues with big data 251

Editing 251

Step two: Coding 253

Coding process 253

Automated coding systems and text processing 254

Intelligent capture systems 255

The data capture process 256

Scanning 256

Step four: Logical cleaning of data 256

Step five: Tabulation and statistical analysis 257

One-way frequency tables 257

Cross tabulations 259

Death of crosstabs? 261

Graphic representations of data 261

Line charts 261

Pie charts 262

Bar charts 262

Descriptive statistics 264

Measures of central tendency 264

Measures of dispersion 265

Percentages and statistical tests 266

Summary 268

Key terms 269

Questions for review and critical thinking 269

Working the net 271

Real-life research 271

Endnotes 272

Acknowledgements 272

Chapter 13

Statistical testing of differences and relationships 273

Evaluating differences and changes 274

Statistical significance 274

Hypothesis testing 275

Steps in hypothesis testing 275

Types of errors in hypothesis testing 277

Accepting H 0 versus failing to reject H 0 279

One-tailed versus two-tailed test 279

Example of performing a statistical test 279

Commonly used statistical hypothesis tests 281

Independent versus related samples 281

Degrees of freedom 282

Goodness of fit 282

Chi-square test 282

Hypotheses about one mean 284

t Test 284

Hypotheses about two means 286

Hypotheses about proportions 287

Proportion in one sample 287

Two proportions in independent samples 288

Analysis of variance (ANOVA) 290

P values and significance testing 292

Summary 293

Key terms 293

Questions for review and critical thinking 294

Working the net 296

Real-life research 296

Endnotes 297

Acknowledgements 297

Chapter 14

More powerful statistical methods 299

Data scientist — hot new career 300

Bivariate statistical analysis 300

Bivariate analysis of relationships 300

Bivariate regression 300

Nature of the relationship 301

Example of bivariate regression 301

Correlation for metric data: Pearson’s product–moment correlation 307

Multivariate analysis procedures 308

Multivariate software 308

Multiple regression analysis 309

Applications of multiple regression analysis 310

Multiple regression analysis measures 310

Dummy variables 311

Potential use and interpretation problems 311

Multiple discriminant analysis 312

Applications of multiple discriminant analysis 313

Cluster analysis 313

Procedures for clustering 313

Applications of cluster analysis 314

Factor analysis 315

Factor scores 316

Factor loadings 317

Naming factors 317

Number of factors to retain 317

Conjoint analysis 317

Simulating buyer choice 318

Limitations of conjoint analysis 318

Neural networks 319

Description of a neural network 319

How neural networks ‘learn’ 320

When neural networks are appropriate 320

Limitations of neural networks 320

Predictive analytics 320

Using predictive analytics 321

Privacy concerns and ethics 322

Commercial predictive modelling software and applications 322

Summary 323

Key terms 324

Questions for review and critical thinking 325

Working the net 327

Real-life research 327

Endnotes 328

Acknowledgements 329

Chapter 15

Communicating analytics and research insights 331

The research report 332

Organising the report 332

Format of the report 333

Formulating recommendations 333

Presenting the results 339

Making a presentation 340

Infographics 340

Using online tools for effective presentations 341

Summary 343

Key terms 343

Questions for review and critical thinking 343

Working the net 343

Marketing research in the digital era — a comprehensive marketing research project 344

Endnotes 346

Acknowledgements 346

Appendix A: Statistical tables 347

Index 359