<p>(*Update: Issues re layout have been addressed, and syntax is included for SAS and SPSS.) Increasingly, marketers have to analyze massive amounts of customer data. Marketing models translate that data into information to use for fact-based strategic and tactical decisions. This book covers a breadth of marketing questions and their optimal marketing model for solutions.Chapters include: 1) Introduction to Marketing Models. 2) Marketing Segmentation and Cluster Analysis. 3) Brand Choice and Logit Models. 4) Measuring Customer Attitudes and Factor Analysis. 5) Drivers of Customer Satisfaction and Path Models. 6) Perceptual Maps and Multidimensional Scaling. 7) New Products and Conjoint Analysis. 8) ROI, Experiments, and ANOVA. 9) Diffusion Models and Forecasting. 10) Word-of-Mouth and Social Networks. 11) Marketing Models: Classic Models, Big Data, to infinity and beyond. 12) Introduction to SAS, 13) Introduction to SPSS.About the AuthorDawn Iacobucci is Professor of Marketing at Vanderbilt University (previously Sr. Associate Dean at Vanderbilt, Kellogg (1987-2004), Arizona (2001-2002), and Wharton (2004-2007)). Her research has been published in the Journal of Marketing, the Journal of Marketing Research, Harvard Business Review, the Journal of Consumer Psychology, International Journal of Research in Marketing, Marketing Science, the Journal of Service Research, Psychometrika, Psychological Bulletin, and Social Networks. She edited Networks in Marketing, Handbook of Services Marketing and Management, Kellogg on Marketing, and Kellogg on Integrated Marketing. She is author of Marketing Management, MM, Mediation Analysis, Analysis of Variance (ANOVA), and coauthor with Gilbert Churchill on the lead Marketing Research text.</p>

Marketing Models: Multivariate Statistics and Marketing Analytics, 4e

Business & Money

Iacobucci, Dr. Dawn

Marketing & Sales

CreateSpace Independent Publishing Platform

Analysis of Variance, Experimental Design, and Multivariate ANOVA, 3e

Science & Math

Research

Independently published

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