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Marketing Models: Multivariate Statistics and Marketing Analytics, 4e, by Dr. Dawn Iacobucci
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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 Author Dawn 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.
- Sales Rank: #1663010 in Books
- Published on: 2016-11-05
- Original language: English
- Dimensions: 10.00" h x .64" w x 8.00" l,
- Binding: Paperback
- 280 pages
About the Author
Dawn Iacobucci is Professor of Marketing at Vanderbilt University (previously 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, the 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, and coauthor with Gilbert Churchill on the lead Marketing Research text.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Models mean profit
By John Muller
New flash, all big companies' marketing is science based, using extensive modeling. Because models reduce risk, skewing the odds in your favor. Every business regardless of size can and should use them. The models in this book are powerful and broad-based and good place to start.
I own an analytical marketing company and created most of these models in a spreadsheet (some of which I was already using). Using them helps my clients make significant and correct decisions. They give focus, direction, and most important, knowledge. Further, size is just not an issue; any business can use them. Most of my clients are small to mediums size, but all have enough data to utilize models.
Many are also useful for startups as well. Entrepreneurs are spectacularly bad at marketing (marketing isn't selling), not understanding that marketing starts well before you spend any time or money developing your product/service. It is this analytical, science-based marketing that sets a solid foundation.
Most​ businesses don't use science-based marketing and 95% of them fail. 80% of entrepreneurs fail in the first 18 months, according to a Forbes article. Models and other tools available set you up for success. It is just foolhardy not to use them
0 of 0 people found the following review helpful.
It would've been better if it showed codes on commonly used stats packages
By Edward Lee
It would've been better if it showed codes on commonly used stats packages. Hard to follow without examples to follow computationally.
0 of 0 people found the following review helpful.
Five Stars
By Amazon Customer
content is great and clear, a mktg analytics student.
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