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Über dieses Buch

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.



Basics of R


1. Welcome to R

As a marketing analyst, you have no doubt heard of R. You may have tried R and become frustrated and confused, after which you returned to other tools that are “good enough.” You may know that R uses a command line and dislike that. Or you may be convinced of R’s advantages for experts but worry that you don’t have time to learn or use it.
Chris Chapman, Elea McDonnell Feit

2. An Overview of the R Language

In this chapter, we cover just enough of the R language to get you going. If you’re new to programming, this chapter will get you started well enough to be productive and we’ll call out ways to learn more at the end. R is a great place to learn to program because its environment is clean and much simpler than traditional programming languages such as Java or C++. If you’re an experienced programmer in another language, you should skim this chapter to learn the essentials.
Chris Chapman, Elea McDonnell Feit

Fundamentals of Data Analysis


3. Describing Data

In this chapter, we tackle our first marketing analytics problem: summarizing and exploring a data set with descriptive statistics (mean, standard deviation, and so forth) and visualization methods. Such investigation is the simplest analysis one can do yet also the most crucial. It is important to describe and explore any data set before moving on to more complex analysis. This chapter will build your R skills and provide a set of tools for exploring your own data.
Chris Chapman, Elea McDonnell Feit

4. Relationships Between Continuous Variables

Experienced analysts understand that the most important insights in marketing analysis often come from understanding relationships between variables. While it is helpful to understand single variables, such as how many products are sold at a store, more valuable insight emerges when we understand relationships such as “Customers who live closer to our store visit more often than those who live farther away,” or “Customers of our online shop buy as much in person at the retail shop as do customers who do not purchase online.”
Chris Chapman, Elea McDonnell Feit

5. Comparing Groups: Tables and Visualizations

Marketing analysts often investigate differences between groups of people. Do men or women subscribe to our service at a higher rate? Which demographic segment can best afford our product? Does the product appeal more to homeowners or renters? The answers help us to understand the market, to target customers effectively, and to evaluate the outcome of marketing activities such as promotions.
Chris Chapman, Elea McDonnell Feit

6. Comparing Groups: Statistical Tests

In Chap. 5 we saw how to break out data by groups and inspect it with tables and charts. In this chapter we continue our discussion and address the question, “It looks different, but is it really different?” This involves our first inferential statistical procedures: chi-square, t-tests, and analysis of variance (ANOVA). In the final section, we introduce a Bayesian approach to compare groups.
Chris Chapman, Elea McDonnell Feit

7. Identifying Drivers of Outcomes: Linear Models

In this chapter we investigate linear models, which are often used in marketing to explore the relationship between an outcome of interest and other variables. A common application in survey analysis is to model satisfaction with a product in relation to specific elements of the product and its delivery; this is called “satisfaction drivers analysis.” Linear models are also used to understand how price and advertising are related to sales, and this is called “marketing mix modeling.” There are many other situations in which it is helpful to model an outcome, known formally as a response or dependent variable, as a function of predictor variables (also known as explanatory or independent variables). Once a relationship is estimated, one can use the model to make predictions or forecasts of the likely outcome for other values of the predictors.
Chris Chapman, Elea McDonnell Feit

Advanced Marketing Applications


8. Reducing Data Complexity

Marketing data sets often have many variables—many dimensions—and it is advantageous to reduce these to smaller sets of variables to consider. For instance, we might have many items on a consumer survey that reflect a smaller number of underlying concepts such as customer satisfaction with a service, category leadership for a brand, or luxury for a product. If we can reduce the data to its underlying dimensions, we can more clearly identify the relationships among concepts.
Chris Chapman, Elea McDonnell Feit

9. Additional Linear Modeling Topics

As we noted in Chap. 7, the range of applications and methods in linear modeling and regression is vast.
Chris Chapman, Elea McDonnell Feit

10. Confirmatory Factor Analysis and Structural Equation Modeling

In this chapter, we discuss structural equation models in R. We show how R can be used for both covariance-based and partial least squares modeling, and present basic guidelines for model assessment. We also demonstrate the power of R to simulate data and to use simulation to inform our expectations.
Chris Chapman, Elea McDonnell Feit

11. Segmentation: Clustering and Classification

In this chapter, we tackle a canonical marketing research problem: finding, assessing, and predicting customer segments. In previous chapters we’ve seen how to assess relationships in the data (Chap. 4), compare groups (Chap. 5), and assess complex multivariate models (Chap. 10). In a real segmentation project, one would use those methods to ensure that data has appropriate multivariate structure, and then begin segmentation analysis.
Chris Chapman, Elea McDonnell Feit

12. Association Rules for Market Basket Analysis

Many firms compile records of customer transactions. These data sets take diverse forms including products that are purchased together, services that are tracked over time in a customer relationship management (CRM) system, sequences of visits and actions on a Web site, and records of customer support calls. These records are very valuable to marketers and inform us about customers’ purchasing patterns, ways in which we might optimize pricing or inventory given the purchase patterns, and relationships between the purchases and other customer information.
Chris Chapman, Elea McDonnell Feit

13. Choice Modeling

Much of the data we observe in marketing describes customers purchasing products. For example, as we discussed in Chap. 12, retailers now regularly record the transactions of their customers. In that chapter, we discussed analyzing retail transaction records to determine which products tend to occur together in the same shopping basket.
Chris Chapman, Elea McDonnell Feit


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