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Über dieses Buch

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. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research.

This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.

Inhaltsverzeichnis

Frontmatter

Basics of Python

Frontmatter

Chapter 1. Welcome to Python

Abstract
Python is a general-purpose programming language. It has increasingly become the language of choice not only for teaching programming, given its simple syntax and great readability, but for programming applications of all kinds, ranging from data analysis and data science to full stack web development.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 2. An Overview of Python

Abstract
In this chapter, we cover just enough of Python 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. Python is a great place to learn to program because its syntax is simpler and it has less overhead (e.g. memory management) 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.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Fundamentals of Data Analysis

Frontmatter

Chapter 3. Describing Data

Abstract
In this chapter, we tackle our first marketing analytics problem: exploring a new dataset. The goals for this chapter are to learn how to:
  • Simulate a dataset
  • Summarize and explore a dataset with descriptive statistics (mean, standard deviation, and so forth)
  • Explore simple visualization methods
Such investigation is the simplest analysis one can do yet also the most crucial. It is important to describe and explore any dataset before moving on to more complex analysis. This chapter will build your Python skills and provide a set of tools for exploring your own data.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 4. Relationships Between Continuous Variables

Abstract
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.”
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 5. Comparing Groups: Tables and Visualizations

Abstract
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.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 6. Comparing Groups: Statistical Tests

Abstract
In Chap. 5 we saw how to break out data by groups and inspect them 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).
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 7. Identifying Drivers of Outcomes: Linear Models

Abstract
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, known as explanatory or independent variables. Once a relationship is estimated, one can use the model to make predictions of the outcome for other values of the predictors. For example, in a course, we might find that final exam scores can be predicted based on the midterm exam score.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 8. Additional Linear Modeling Topics

Abstract
As we noted in Chap. 7, the range of applications and methods in linear modeling and regression is vast. In this chapter, we discuss three additional topics in linear modeling that often arise in marketing.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Advanced Data Analysis

Frontmatter

Chapter 9. Reducing Data Complexity

Abstract
Marketing datasets 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 questions (e.g. 9) on a consumer survey that reflect a smaller number (such as 3) 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 underlying relationships among concepts.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 10. Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations

Abstract
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 data (Chap. 4), compare groups (Chap. 5), and assess models (Chap. 7). In a real segmentation project, one would use those methods to ensure that data have appropriate multivariate structure, and then begin segmentation analysis.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 11. Classification: Assigning Observations to Known Categories

Abstract
In this chapter, we will explore supervised learning methods. Unlike with clustering, generally, the value of a supervised model output is inherent in the framing of the question. This makes interpretation easier, but it requires an outcome variable to have a strong relationship with its indicator variables, and benefits from data that are well structured and clean. With statistical modeling, people often say “garbage in, garbage out,” meaning that even a very sophisticated model will not be able to produce reliable results if the data are not high quality or there is no actual relationship between input and output variables.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Chapter 12. Conclusion

Abstract
Congratulations! By working through this book, you have established a solid foundation in Python programming, data analytics, and marketing research. We would like to share a few final thoughts that summarize some of the most important points.
Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Backmatter

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