This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.
This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas.
Excel is that tool. Every example in this book features step-by-step instructions, a downloadable Excel file containing data and solutions, and plenty of screenshots. To sharpen your marketing analytics, you just need this guide and Excel.
Market Research and Analysis
Houston, B., L. Bruzzese, and S. Weinberg 2002. The Investigative Reporter's Handbook: A Guide to Documents, Databases and Techniques (fourth ed.). Boston: Bedford/St. Martin's. Huber, J. and K. Zwerina 1996. The importance of utility ...
The Handbook of Marketing Analytics showcases the analytical methods used in marketing and their high-impact real-life applications.
This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications.
Marketing Research for Managerial Decision Making
Big data is currently the most powerful resource to the marketing professional, and this book illustrates how to fully harness that power to effectively maximize marketing efforts.
What you will learnAnalyze and visualize data in Python using pandas and MatplotlibStudy clustering techniques, such as hierarchical and k-means clusteringCreate customer segments based on manipulated data Predict customer lifetime value ...
This book can be used as a supplement to a traditional marketing research text or on its own.