Marketing Research

Häftad, Engelska, 2021

1 784 kr

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Beskrivning

Marketing 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 approach—grounded in the authors' 50 years' combined experience in the marketing research industry—features 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 marketing research is gathered and used in the real world. Detailed yet accessible chapters examine topics including marketing intelligence, problem definition and exploratory research, big data and data analytics, online and social media marketing research, questionnaire design, statistical testing, and managing marketing research studies and teams.

Produktinformation

Utgivningsdatum:2021-03-25
Mått:201 x 252 x 20 mm
Vikt:680 g
Format:Häftad
Språk:Engelska
Antal sidor:432
Upplaga:12
Förlag:John Wiley & Sons Inc
ISBN:9781119716310

Utforska kategorier

Försäljning och marknadsföring inom Ekonomi och Ledarskap

Innehållsförteckning

Preface viiAcknowledgments ix1 Steps in Creating Market Insights and the Growing Role of Marketing Analytics 1Marketing Research and Developing Market Insights 1Marketing Research Defined 2Importance of Marketing Research to Management 2Understanding the Ever-Changing Marketplace 3Social Media and User-Generated Content 3Proactive Role of Marketing Research 4Marketing Analytics Moves to the Forefront 4The Research Process 4Recognize the Problem or Opportunity 5Find Out Why the Information is Being Sought 6Understand the Decision-Making Environment with Exploratory Research 6Use the Symptoms to Clarify the Problem 8Translate the Management Problem into a Marketing Research Problem 9Determine Whether the Information Already Exists 9Determine Whether the Question Can Be Answered 10State the Research Objectives 10Research Objectives As Hypotheses 11Marketing Research Process 11Creating the Research Design 11Choosing a Basic Method of Research 11Selecting the Sampling Procedure 13Collecting the Data 13Analyzing the Data 13Presenting the Report 14Following Up 14Managing the Research Process 14The Research Request 14Request for Proposal 15The Marketing Research Proposal 16What to Look for in a Marketing Research Supplier 17Modifying the Research Process—Marketing Analytics, Big Data, and Unsupervised Learning 17A Shifting Paradigm 18What Motivates Decision Makers to Use Research Information? 18Summary 19Key Terms 19Questions for Review & Critical Thinking 20Working the Net 20Real-Life Research 1.1: Can Anyone Be a Market Researcher? 212 Secondary Data: A Potential Big Data Input 23Nature of Secondary Data 23Advantages of Secondary Data 24Limitations of Secondary Data 25Internal Databases 27Creating an Internal Database 27First, Second, and Third Party Data 27Behavioral Targeting 28Big Data 29The Big Data Breakthrough 29Making Big Data Actionable in Traditional Marketing Research Environments 30Battle over Privacy 31The Federal Trade Commission 32State Data Privacy Laws 32The General Data Protection Regulation 32Summary 33Key Terms 34Questions for Review & Critical Thinking 34Working the Net 34Real-Life Research 2.1: The GDPR and American Small Business 343 Measurement to Build Marketing Insight 36Measurement Process 36Step One: Identify the Concept of Interest 37Step Two: Develop a Construct 38Step Three: Define the Concept Constitutively 38Step Four: Define the Concept Operationally 38Step Five: Develop a Measurement Scale 40Nominal Level of Measurement 41Ordinal Level of Measurement 41Interval Level of Measurement 42Ratio Level of Measurement 42Step Six: Evaluate the Reliability and Validity of the Measurement 43Reliability 45Validity 47Reliability and Validity—A Concluding Comment 51Attitude Measurement Scales 51Graphic Rating Scales 52Itemized Rating Scales 53Traditional One-Stage Format 55Two-Stage Format 55Rank-Order Scales 56Paired Comparisons 56Constant Sum Scales 56Semantic Differential Scales 58Stapel Scales 59Likert Scales 60Purchase-Intent Scales 62Scale Conversions 64Net Promoter Score (NPS) 65Considerations in Selecting a Scale 66The Nature of the Construct Being Measured 66Type of Scale 67Balanced versus Nonbalanced Scale 67Number of Scale Categories 67Forced versus Nonforced Choice 68Summary 68Key Terms 69Questions for Review & Critical Thinking 70Working the Net 70Real-Life Research 3.1: PNC Bank Considers Changing Its Customer Satisfaction Measurement Scale 714 Acquiring Data Via a Questionnaire 73Role of a Questionnaire 73Criteria for a Good Questionnaire 74Does It Provide the Necessary Decision-Making Information? 74Does It Consider the Respondent? 75Does It Meet Editing Requirements? 75Does It Solicit Information in an Unbiased Manner: Questionnaire Design Process 76Step One: Determine Survey Objectives, Resources, and Constraints 77Step Two: Determine the Data-Collection Method 78Step Three: Determine the Question Response Format 78Step Four: Decide on the Question Wording 81Step Five: Establish Questionnaire Flow and Layout 84Step Six: Evaluate the Questionnaire 87Step Seven: Obtain Approval of All Relevant Parties 88Step Eight: Pretest and Revise 88Step Nine: Prepare Final Questionnaire Copy 88Step Ten: Implement the Survey 88Field Management Companies 89Avoiding Respondent Fatigue 89Intelligence Moves Into Questionnaire Coding 90Conducting Surveys on Smartphones and Tablets 91The Rapid Growth of Do-It-Yourself (DIY) Surveys 92Summary 93Key Terms 94Questions for Review & Critical Thinking 94Working the Net 95Real-Life Research 4.1: Arrow Cleaners 955 Sample Design 99Concept of Sampling 100Population 100Sample versus Census 101Developing a Sampling Plan 101Step One: Define the Population of Interest 101Step Two: Choose a Data-Collection Method 104Step Three: Identify a Sampling Frame 104Step Four: Select a Sampling Method 104Step Five: Determine Sample Size 106Step Six: Develop Operational Procedures for Selecting Sample Elements 106Step Seven: Execute the Operational Sampling Plan 106Sampling and Nonsampling Errors 106Probability Sampling Methods 107Simple Random Sampling 107Systematic Sampling 108Stratified Sampling 109Cluster Sampling 110Nonprobability Sampling Methods 111Convenience Samples 111Judgment Samples 111Quota Samples 112Snowball Samples 112Internet Sampling 112Determining Sample Size 113Determining Sample Size for Probability Samples 113Budget Available 113Rule of Thumb 114Number of Subgroups Analyzed 114Traditional Statistical Methods 115Normal Distribution 115General Properties 115Basic Concepts 116Making Inferences on the Basis of a Single Sample 118Point and Interval Estimates 118Sampling Distribution of the Proportion 119Determining Sample Size 120Problems Involving Means 120Problems Involving Proportions 122Determining Sample Size for Stratified and Cluster Samples 123Sample Size for Qualitative Research 123Population Size and Sample Size 124Summary 125Key Terms 126Questions for Review & Critical Thinking 126Working the Net 127Real-Life Research 5.1: Insights Research Group (IRG) 1276 Traditional Survey Research 129Why Decision Makers Like Survey Research 129Types of Errors in Survey Research 130Sampling Error 130Systematic Error 131Types of Surveys 135Door-to-Door Interviews 135Executive Interviews 136Mall-Intercept Interviews 136Telephone Interviews 137Self-Administered Questionnaires 138Mail Surveys 139Determination of the Survey Method 141Sampling Precision 141Budget 141Requirements for Respondent Reactions 142Quality of Data 142Length of the Questionnaire 142Incidence Rate 143Structure of the Questionnaire 143Time Available to Complete the Survey 143Summary 144Key Terms 144Questions for Review & Critical Thinking 145Real-Life Research 6.1: Do Consumers Like Chatbots? 1457 Qualitative Research 146Nature of Qualitative Research 146Qualitative Research versus Quantitative Research 147The Use of Qualitative Research 147Limitations of Qualitative Research 148Focus Groups 149Popularity of Focus Groups 149Conducting Focus Groups 150Focus Group Trends 157Benefits and Drawbacks of Focus Groups 158Other Qualitative Methodologies 159Individual Depth Interviews 159Projective Tests 163Summary 167Key Terms 167Questions for Review & Critical Thinking 167Working the Net 168Real-Life Research 7.1: A Sound Approach for the Sound 1688 Online Marketing Research: The Growth of Mobile and Social Media Research 171Using the Internet for Secondary Data 172Online Qualitative Research 172Online Bulletin Boards 172Webcam and Streaming Technology Focus Groups 173Using the Internet to Find Online Participants 174Online Individual Depth Interviews (IDIs) 175Online Survey Research 175Advantages of Online Surveys 175Disadvantages of Online Surveys 176Tools for Conducting Online Surveys 177Commercial Online Panels 178Panel Recruitment 178Open Recruitment 178Closed Recruitment 179Respondent Participation 179Panel Management 180Mobile Internet Research—The Future is Now 180Advantages of Mobile 181Designing a Mobile Survey 181Social Media Marketing Research 182Summary 182Key Terms 183Questions for Review & Critical Thinking 183Working the Net 183Real-Life Research 8.1: Shoppers Spending More In-Store Than Online 1839 Primary Data Collection: Observation 185Nature of Observation Research 185Conditions for Using Observation 186Approaches to Observation Research 186Advantages of Observation Research 188Disadvantages of Observation Research 189Human Observation 189Ethnographic Research 189Mobile Ethnography 192Mystery Shoppers 192One-Way Mirror Observations 194Machine Observation 194Neuromarketing 194Facial Action Coding Services (FACS) 197Gender and Age Recognition Systems 199In-Store Tracking 199Television and Video Audience Measurement and Tracking 200Symphony IRI Consumer Network 200Tracking 201Magazines Track Online Readers and Apply It Also to Print 201Social Media Tracking 202Virtual Reality and Augmented Reality Marketing Research 204Summary 204Key Terms 205Questions for Review & Critical Thinking 205Working the Net 206Real-Life Research 9.1: Bausch & Lomb Fine-Tune the Details 20610 Marketing Analytics 208What is Marketing Analytics? 209The Marketing Analytics Process 210Getting the Data 210Big Data Sources 210Data from Traditional Sources 211Organizing, Merging, and Using Big Data 212Acting on Results of Analysis 212Big Data 212Background on Big Data Issues 212How Does It Work? 213Analyzing Data: Descriptive, Predictive, and Prescriptive Analytics 214Descriptive Analytics 214Predictive Analytics 214Prescriptive Analytics 215Advanced Analytical Methods 216Data Mining 216Machine and Deep Learning 219Artificial Intelligence or AI 220Data Visualization 224Infographics 225Marketing Dashboards 225Privacy Issues 226Privacy versus Customization 226Summary 228Key Terms 229Questions for Review & Critical Thinking 229Working the Net 230Real-Life Research 10.1: Affiliated Parking Systems Looks to New Pricing Approach 23011 Primary Data: Experimentation and Test Markets 231What is an Experiment? 232Demonstrating Causation 232Concomitant Variation 233Appropriate Time Order of Occurrence 233Elimination of Other Possible Causal Factors 233Experimental Setting 234Laboratory Experiments 234Field Experiments 234Experimental Validity 234Experimental Notation 235Extraneous Variables 235Examples of Extraneous Variables 236Controlling Extraneous Variables 237Experimental Design, Treatment, and Effects 238Limitations of Experimental Research 239High Cost 239Security Issues 239Implementation Problems 239Selected Experimental Designs 240Preexperimental Designs 240True Experimental Designs 241Quasi-Experiments 242Test Markets 244Types of Test Markets 245Decision to Conduct Test Marketing 248Steps in a Test Market Study 249Summary 252Key Terms 252Questions for Review & Critical Thinking 253Working the Net 254Real-Life Research 11.1: Los Lobos Beer 25412 Data Processing and Basic Data Analysis 255Overview of Data Analysis Procedure for Survey Research 256Step One: Validation and Editing of Paper Surveys 256Validation 256Quality Assurance for Internet Panels 257Quality Assurance—Respondent Cooperation and Attention Issues 258Special Issues with Big Data 260Editing 260Step Two: Coding 264Coding Process 265Automated Coding Systems and Text Processing 266Intelligent Capture Systems 267The Data Capture Process 268Scanning 268Step Four: Logical Cleaning of Data 269Step Five: Tabulation and Statistical Analysis 269One-Way Frequency Tables 269Cross Tabulations 272Death of Crosstabs? 274Graphic Representations of Data 274Line Charts 275Pie Charts 275Bar Charts 275Descriptive Statistics 278Measures of Central Tendency 278Measures of Dispersion 279Percentages and Statistical Tests 280Summary 281Key Terms 281Questions for Review & Critical Thinking 282Working the Net 284Real-Life Research 12.1: Buzzy’s Tacos 28413 Statistical Testing of Differences and Relationships 285Evaluating Differences and Changes 286Statistical Significance 286Hypothesis Testing 287Steps in Hypothesis Testing 288Types of Errors in Hypothesis Testing 290Accepting H0 versus Failing to Reject (FTR) H0 292One-Tailed versus Two-Tailed Test 292Example of Performing a Statistical Test 292Commonly Used Statistical Hypothesis Tests 295Independent versus Related Samples 295Degrees of Freedom 295Goodness of Fit 296Chi-Square Test 296Hypotheses about One Mean 299t Test 299Hypotheses about Two Means 300Hypotheses about Proportions 302Proportion in One Sample 302Two Proportions in Independent Samples 303Analysis of Variance (ANOVA) 305p Values and Significance Testing 308Summary 309Key Terms 309Questions for Review & Critical Thinking 310Working the Net 311Real-Life Research 13.1: Analyzing William D. Scott (WDS) Segmentation Results 31214 More Powerful Statistical Methods 313Data Scientist—Hot New Career 313Bivariate Statistical Analysis 314Bivariate Analysis of Relationships 314Bivariate Regression 314Nature of the Relationship 315Example of Bivariate Regression 316Correlation for Metric Data: Pearson’s Product–Moment Correlation 322Multivariate Analysis Procedures 323Multivariate Software 324Multiple Regression Analysis 324Applications of Multiple Regression Analysis 325Multiple Regression Analysis Measures 326Dummy Variables 327Potential Use and Interpretation Problems 327Multiple Discriminant Analysis 328Applications of Multiple Discriminant Analysis 329Cluster Analysis 330Procedures for Clustering 330Applications of Cluster Analysis 331Factor Analysis 332Factor Scores 332Factor Loadings 334Naming Factors 334Number of Factors to Retain 335Conjoint Analysis 335Simulating Buyer Choice 335Limitations of Conjoint Analysis 336Neural Networks 337Description of a Neural Network 337How Neural Networks “Learn” 338When Neural Networks Are Appropriate 338Limitations of Neural Networks 338Predictive Analytics 339Using Predictive Analytics 339Privacy Concerns and Ethics 341Commercial Predictive Modeling Software and Applications 341Summary 341Key Terms 342Questions for Review & Critical Thinking 343Working the Net 345Real-Life Research 14.1: Satisfaction Research for Pizza Pronto 34515 Communicating Analytics and Research Insights 347The Research Report 347Organizing the Report 348Format of the Report 349Formulating Recommendations 349Presenting the Results 355Making a Presentation 356Infographics 356Presentations by Internet 358Summary 358Key Terms 359Questions for Review & Critical Thinking 359Working the Net 359Real-Life Research 15.1: TouchWell Storefront Concept and Naming Research 359Appendix A A-1[Appendix B and C are available online at www.wiley.com/go/mcdaniel/marketingresearch12e]Endnotes N-1Glossary G-1QSR Survey QS-1Index I-1