Customer Lifetime Value

and graduate levels or as a supporting book for other more topical classes (e.g., marketing strategy, sales management, digital marketing) that need to utilize some marketing analytical techniques relevant to a specific problem.

First Principles Approach to Marketing Analytics

To better link marketing analytics to real-world practice, this book shows that marketing analytics and decisions can be organized to solve four underlying “problems” or complexities that all firms face when implementing marketing strategies that require making marketing decisions. These four problems represent critical hurdles to marketing success; they also define the organization for this book. We refer to them as the First Principles of Marketing because they reflect the foundational assumptions on which marketing strategy is based. In short, marketing strategists’ most critical decisions must address these First Principles.

Each First Principle or underlying assumption, when matched with its associated managerial decisions, is a Marketing Principle (MP). For example, all customers differ, so firms must conduct analyses and make decisions to manage customer heterogeneity, and together these insights constitute MP#1. This First Principle approach to marketing is unique. Its goal is to align the analysis tools, processes, and research techniques offered in many consulting books, together with existing frameworks and insights on the marketing mix (4Ps), competitors, and marketing tasks from traditional textbooks. Their alignment suggests tactics for “solving,” or at least addressing, the underlying First Principles. Organizing the varied discussions around four fundamental principles means that every analytic tool, model, and decision appears within its meaningful context.

Integrated Marketing Analytics

Firms increasingly rely on marketing analytics to improve their marketing decision making. To enable a manager to develop and implement a marketing strategy successfully, strong marketing analytics capabilities often are a prerequisite. In response to these trends, and to increase the linkages between marketing analytics and data-based decision making, this book integrates relevant marketing analytics techniques with the First Principles of Marketing. The marketing analytics techniques and exercises offered throughout the book provide details and examples of the analytical methods used most frequently by marketers. All technical chapters include an accessible description of state-of-the-art marketing analytics techniques and how and when managers may use them, followed by a case study that includes the step-by-step R code, Tableau packaged workbooks, and datasets needed to solve the case. Thus, students have access to hands-on examples they can analyze using the tools outlined in the book, in a relevant, real-world context. A major advantage of R is that it is free, making it extremely attractive for both instructors

and students to use the cases and software code. Also, Tableau currently offers a free academic license for instructors and full-time students, making it an attractive visualization tool for both instructors and students.

Structure of the Book

The 17 chapters in this book are organized to match the natural temporal ordering of the First Principles. Chapter 1 serves as an introduction to marketing analytics, including its history and definitions, why it is important to learn marketing analytics, as well as why using a First Principles approach for marketing analytics makes sense. Chapter 1 also provides a short summary of each of the First Principles, and discusses the software tools (R and Tableau) used in this book.

Chapters 2, 6, 10, and 14 parallel one another. Each of these chapters discusses one of the First Principles, and introduces the marketing analytics models and analyses managers can use to solve the respective underlying problems of each First Principle.

Chapters 3–5, 7–9, 11–13, and 15–17 are the technical chapters of the book. There are 12 technical chapters in total. Each of the technical chapters covers a different marketing analytics technique and includes: ● An accessible description of the marketing analytics technique that avoids technical jargon and “Greek” letters as much as possible; ● A running case study featuring the fictitious retailer Chestnut Ridge; ● Data for the case study, which is available on the book’s online web portal; ● Step-by-step R code and Tableau instructions to analyze the marketing analytics technique discussed and solve the case study in each chapter. Thus, each of the technical chapters uses a “tell–show–do” approach that should facilitate learning and retention.

Marketing Analytics Techniques Covered

The marketing analytics techniques covered in this book are the most popular techniques currently employed by marketers. The techniques covered in the book are shown in Table 1:

Table 1 Marketing Analytics Techniques

Chapter Marketing Analytics Technique Use Case Example

3 Cluster Analysis Identifying Customer Segments 4 Discriminant Analysis and Classification Deciding Which Segments to Target 5 Perceptual and Preference Mapping Positioning the Brand for Success 7 RFM Analysis Heuristic-based Customer Selection