Marketing Models are neither just Statistics nor just Marketing, but a synthesis of the information sources creating a cohesive predictive system. If you're looking for a book that talks about the "logic of marketing" and the "design of statistical models" in an integrated way to increase model accuracy and improve business profits, then this book was written for you. Nevertheless, anyone who's worked around Marketing Models at all will have heard people talk about modifying models for "statistical reasons" or modifying them for "business reasons" as though the two sets of criteria are from Mars and Venus, respectively. In this book, I try to help readers develop a deeper understanding of the reasoning behind both sets of rules to put themselves in a better position to weigh the value of all evidence and define the most applicable business goals for their models to address. And after defining those goals, design the best models for achieving them. If you'd like to better understand: how to define dependent variables to maximize business goals; how business logic should influence your model design; when lower R-Squared statistics can represent better models; how much information you can reasonably expect from your data; how to safely work with imperfect data that may offer partial information but that shouldn't be naively relied upon, and; ultimately how to create models offering superior business value then read on.
