TABLE OF CONTENTS
List of Figures
vii
List of Tables
xi
Preface
xiii
Tour of the Book
xxiii
Acknowledgments
xxvii
Author Biographies
xxix
1 Introduction to Marketing Analytics Based on First Principles
1
PART 1: ALL CUSTOMERS DIFFER 33 2 Understanding Marketing Principle #1: All Customers Differ 35 3 Cluster Analysis for Segmentation 53 4 Discriminant Analysis for Targeting and Classification 79 5 Perceptual and Preference Mapping for Competitive Positioning 100 PART 2: ALL CUSTOMERS CHANGE 6 Understanding Marketing Principle #2: All Customers Change 7 Using Recency, Frequency and Monetary (RFM) Analysis for Customer Selection 8 Using Logistic Regression for Customer Selection 9 Using Customer Lifetime Value for Customer Selection
131
PART 3: ALL COMPETITORS REACT 10 Understanding Marketing Principle #3: All Competitors React 11 Survey Design and Testing to Derive Customer Insights
211
133 145 168 190
213 225
vi
Table of Content
12 Conjoint Analysis for Product and Pricing Decisions 13 Forecasting Sales for New Products
252
PART 4: ALL RESOURCES ARE LIMITED 14 Understanding Marketing Principle #4: All Resources Are Limited 15 Using Marketing Mix Models to Optimize the Marketing Mix 16 Using Marketing Experiments to Optimize the Marketing Mix 17 Using Topic Models to Glean Customer Insights
311
Index
288
313 324 354 376
391
TOUR OF THE BOOK
Marketing Analytics Techniques Easy step-by-step R code and Tableau instructions on using state-of-the-art marketing analytics techniques for effective marketing decision making. Chapter
Marketing Analytics Technique
3
Cluster Analysis
4
Discriminant Analysis and Classification
5
Perceptual and Preference Mapping
7
RFM Analysis
8
Logistic Regression
9
Customer Lifetime Value
11
Factor Analysis
12
Conjoint Analysis
13
Bass Diffusion Model
15
Marketing Mix Models
16
Experiments and Propensity Score Matching
17
Structural Topic Models
xxiv Tour of the Book
Tables and Figures Summarizing important information, illustrating key concepts visually, and offering checklists.
Key Terms Key technical terms are marked in bold text throughout.
Tour of the Book
Learning Objectives What you will learn in each chapter. Helps organize your study and track your progress.
Highlighted throughout the text are key sections, crucial points for learning.
References Full details are provided at the end of each chapter of important articles, books, and research that are cited within the chapters. These references help identify key publications for further understanding.
xxv
xxvi Tour of the Book
Takeaways Key points summarized in easily digestible bullets at the end of each chapter.
Companion Website Visit the companion website, www.bloomsbury.pub/marketing-analytics or https://samsinstitute.com/resources/marketing-analytics-based-on-firstprinciples, for additional learning and teaching tools, professional video features outlining each of the Four Principles from the authors, datasets, Microsoft PowerPoint slides for lecturers and students, and instructor-only access to sample syllabi for marketing analytics courses, and test bank.
PREFACE
Aim of the Book The primary goal of this book is to create a comprehensive, research-based, actionoriented guide for an international audience of practicing managers and managersin-training to understand, implement, and evaluate real-world marketing analytics. Many marketing analytics texts take a statistic- or model-centric perspective and are organized around different modeling techniques, which can leave the student with little guidance on when to use each approach or how the different techniques fit together. This lack of a “big-picture” structure can leave the student marketers without a working framework or understanding on how to integrate marketing analytics into their day-to-day work. This book addresses this concern by adopting a different approach by: ●●
●●
●●
Organizing the analysis tools, marketing models, and chapters around the First Principles of Marketing to give readers a structured framework for understanding and applying marketing analytics for solving a diverse range of marketing problems. Integrating state-of-the-art marketing analytics techniques into all aspects of marketing to allow managers to make more effective data-based decisions. Providing the underpinning of each analysis technique, with open-source R code for conducting the analyses, as well as how to use Tableau to better visualize and generate evidence-based insights.
This approach – as captured in the title, Marketing Analytics: Based on First Principles – has been applied and refined at multiple universities by the authors for undergraduate, MBA, and EMBA students. The companion book, Marketing Strategy: Based on First Principles and Data Analytics, has received resoundingly positive feedback from more than 200 instructors around the world as well as CEOs of major Fortune 500 companies, and was featured in the 2018 Forbes Summer Reading List for Marketers. This new book uses the same First Principles approach, but rather than focusing on marketing strategy with some supporting data analytics, the focus is reversed, where the new book focuses primarily on marketing analytics and offers just enough marketing strategy and decision making to provide a meaningful context for understanding the analysis tools. The book can support classes focused solely on marketing analytics at both the undergrad
xiv
Prefac
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
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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: ●●
●● ●● ●●
n accessible description of the marketing analytics technique that avoids A 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
xvi Prefac 8
Logistic Regression
Model-based Customer Selection
9
Customer Lifetime Value
Calculating Customer Lifetime Value
11
Factor Analysis
Implementing a Brand Audit Survey
12
Conjoint Analysis
Product Design and Pricing Decisions
13
Bass Diffusion Model
Forecasting Sales of New Products
15
Marketing Mix Models
Optimizing the Marketing Mix
16
Experiments and Propensity Score Matching
Designing and Implementing A/B Tests
17
Structural Topic Models
Gleaning Insights from Online Reviews
Unique Features for Instructors Rich and Detailed Instructor Materials To support in-class delivery of content, supporting materials are available to instructors through the book’s online web portal, www.bloomsbury.pub/ marketing-analytics, or from the authors directly at www.SAMSinstitute.com. These supporting materials include example syllabi, hundreds of PowerPoint slides (for classroom instruction), video supplements to many chapters that focus on implementing the analytics example, as well as a test bank and solution guide (restricted to instructors). The goal is to reduce the time and effort it takes for an instructor to adopt the book for classroom instruction.
Additional Marketing Analytics Cases Besides the case study examples (that include the data and step-by-step R and Tableau code) that come with each technical chapter, we also provide recommendations for additional data-driven empirical cases that instructors can use as individual or teambased assignments. These cases have been tailored for use with the material in this book and are published by Darden Business Publishing. This includes assignment questions, sample R code and Tableau packaged workbooks, and datasets that have been formatted for use with the material in this textbook. As more data-driven cases become available, we will continue to provide resources for instructors to link real-world business cases with the tools that students learn from this textbook. The topics currently covered by these cases include: Logistic regression, segmentation, targeting, and positioning, conjoint analysis, and marketing mix models with text analysis. The cases are available through Darden Business Publishing and the supplemental material is available on the book’s online web portal. The book can also be used along with analytics cases developed by C-CUBESTM (www.ccubes.net). Several of these cases provide real-world data that allow the students to practice the marketing analytics techniques used in the book using R. The cases complement the chapters in the book, as indicated in Table 2 below.
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Please visit https://ccubes.us/teaching-resources/ for more information on the cases as well as instructions on how to order the cases and teaching resources. Table 2 Compatibility with the C-CUBES Platform Title
Abstract
Courses
Suitable For
Listening to the Voice of the Customer
This note describes how customer surveys can be used to gather customer input for a wide range of strategic decisions.
Marketing, Strategy, Business, Consumer Behavior, Marketing Research, Customer Analytics
Chapters 3, 4, 11
Pursuing the Right Prospects Fixing Sales and Bidding at GQS Through Data Analytics
This case shows how an engineering company can improve sales and bidding by pursuing prospects that they have a greater chance of converting, rather than just prospects that are the largest.
Marketing, Strategy, Business, Buying Behavior, Marketing Research, Customer Analytics
Chapters 4, 5, 8
Hollywood Regressed
This case shows how movie studios can use scientific and analytic approaches to predict box office success.
Marketing, Strategy, Marketing Research, Customer Analytics
Chapters 3, 4, 5, 13, 15
Logistic Regression for Customer Insights
This reading shows how firms can use Logistic Regression for customer insights.
Marketing, Strategy, Marketing Research, Customer Analytics
Chapter 8
Furniture Retail in Greater Houston A and B
This reading shows how firms can use customer satisfaction surveys to inform a customerbased strategy.
Marketing, Strategy, Marketing Research, Customer Analytics
Chapters 4, 5, 8, 11, 15
Randomized Experiments for Customer Insights
This reading shows how firms can use randomized experiments for customer insights.
Marketing, Strategy, Marketing Research, Customer Analytics
Chapter 16
HealFirst Clinics A
This reading shows how a chain of clinics can use an experimental approach to improve strategy implementation.
Marketing, Strategy, Business, Buying Behavior, Marketing Research, Customer Analytics
Chapters 4, 5, 16
HealFirst Clinics B
This reading shows how a chain of clinics can use an experimental approach to improve strategy implementation.
Marketing, Strategy, Business, Buying Behavior, Marketing Research, Customer Analytics
Chapters 4, 5, 16
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Green Cover Planning to Go Big A
This reading shows how firms can use Conjoint Analysis for customer insights.
Marketing, Strategy, Business, Buying Behavior, Marketing Research, Customer Analytics
Chapters 4, 5, 12
Green Cover Planning to Go Big B
This reading shows how firms can use Conjoint Analysis for customer insights.
Marketing, Strategy, Business, Buying Behavior, Marketing Research, Customer Analytics
Chapters 4, 5, 12
MODCO Using Customer Complaints and Complements for Strategy Implementation
This reading shows how firms can use voice of the customer data to obtain customer, strategy, and financial insights.
Marketing, Strategy, Business, Buying Behavior, Marketing Research, Customer Analytics
Chapters 4, 5, 8, 11, 15
DISTRICO Executing Strategy
This reading shows how firms can use customer satisfaction surveys to inform a customerbased strategy.
Marketing, Strategy, Marketing Research, Customer Analytics
Chapters 4, 5, 8, 11, 15
Besides the Darden Business Publishing and C-CUBES cases, we also envision that instructors will be able to easily adjust the case study examples included in each technical chapter to create additional meaningful assignments for their students. For example, instructors could change some of the data or bring in their own datasets that have a similar format to the case study examples in the chapter. The R code and Tableau packaged workbooks could be used by the students to solve the newly developed assignments. Instructors who adopt this approach could use the cases and code included in the book for teaching purposes (i.e., teaching the respective marketing analytics technique), and then use their adjusted cases as additional (e.g., homework) assignments.
Diverse Examples Examples are critical to making complex marketing concepts and arguments comprehensible and compelling. This book includes many diverse marketing examples, reflecting different companies and industry segments. The examples reveal how the focal marketing analytics techniques apply to various situations. In addition, the international flavor of the book is consistent with globalization trends in most industries and markets.
Putting It Together: Sample Syllabus for Marketing Analytics Course The objective of the marketing analytics course is to show students the benefits of using a systematic and analytical approach to marketing decision making. An analytical approach will enable students to:
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1. Understand how the “First Principles” of marketing strategy help firms organize the analytics opportunities and challenges in today’s data era in an overarching fashion; 2. Use and execute marketing analytics techniques to understand how to solve marketing analytics problems in a scientific and process-driven manner. We argue that most analytic challenges facing marketing researchers, consultants, and managers could be integrated under one umbrella that comprises four fundamental marketing problems. We then emphasize how the “First Principles” of marketing strategy help solve the four fundamental marketing problems, and help students develop marketing analytics competencies pertaining to each of the four First Principles. Overall, by completing this course, students will be on their way to being able to make the return on investment case for marketing expenditures that companies are increasingly asking of their executives. Table 3 shows how a fifteen-week, semester-long marketing analytics course based on this book could be structured (not including quizzes and exams). More class syllabi examples are available on the book’s website. Table 3 Example Outline for Marketing Analytics Course Week
Topic
Book Readings/Description
1
The Nature and Scope of Marketing Analytics and Management
Chapter 1
2
Data Collection and Measurement Scales & Software Installation
Chapters 2, 6, 10, & 14 R: https://cloud.r-project.org/ Tableau: https://www.tableau.com/ academic/students
3
Customer Segmentation and Targeting
Chapter 3 Tool: Cluster Analysis
4
Customer Targeting
Chapter 4 Tool: Discriminant Analysis
5
Competitive Positioning and Consumer Preferences
Chapter 5 Tool: Perceptual and Preference Maps
6
Constructs, Questionnaire Design, and Data Dimension Reduction
Chapter 11 Tool: Factor Analysis
7
Heuristic Models for Customer Selection
Chapter 7 Tool: RFM Analysis
8
Choice Models
Chapter 8 Tool: Logistic Regression
9
Customer Lifetime Value for Customer Selection and Optimal Resource Allocation
Chapter 9 Tool: Customer Lifetime Value
10
Understanding Consumer Preferences
Chapter 12 Tool: Conjoint Analysis
xx Prefac 11
Market Size Estimation and Forecasting Diffusion of New Products
Chapter 13 Tool: Bass Model
12
Marketing Mix Models
Chapter 15 Tool: Log/Log Regression
13
Experiments
Chapter 16 Tool: Propensity Score Matching
14
New Methods in Marketing Analytics
Chapter 17 Tool: Topic Modeling
15
Wrap-up and Final Exam Preparation
Takeaways for Students and Instructors We are excited that you are considering using our book to teach marketing analytics to your students. We have done our absolute best to ensure you will have a fulfilling experience should you adopt our book. We summarize the key benefits of using our book below. Key Benefits for Instructors: ●●
●●
●●
●●
●●
We have organized marketing analytics techniques and concepts around the First Principles of Marketing Strategy to give you a structured framework for organizing your marketing analytics course. Each of the 12 technical chapters includes a running case study featuring the fictitious retailer Chestnut Ridge, including data and step-by-step R code and Tableau instructions. We have developed hundreds of slides, test banks, and other teaching materials to make adoption of this book as easy as possible. We have provided many diverse marketing examples across different companies and countries and industry segments, showing how various processes, tools, and frameworks apply to many different firms, countries, and situations. We have provided in-depth videos about key topics from the book, including the First Principles, marketing concepts, real business examples, and data analytical methods.
Key Benefits for Students/Working Professionals: ●●
●●
We offer an accessible explanation of each of the marketing analytics techniques covered in the book, avoiding technical jargon and “Greek” letters as much as possible. Many marketing analytics textbooks provide statistics and mathematical modeling, without offering insights into when and why one might want to use a specific marketing analytics technique. This gap can be problematic, because, for businesspeople, questions about when to use a technique and what
Prefac xxi
●●
●●
●●
it does for them are usually more important than details about the underlying mathematical models, for example. Our book seeks to answer these managerially meaningful questions. We use a “tell–show–do” approach to the book, integrating state-of-the-art marketing analytics techniques into all aspects of the strategic planning process to allow you to make more effective data-based decisions. We use the latest marketing research as underpinning for all our guidance, synthesizing more than sixty years of thought in marketing research in one book. We provide solutions to each of our technical chapter case studies, including step-by-step instructions on how to use R and Tableau to estimate the many marketing analytics techniques covered in the book.
Overview of First Principles of Marketing Strategy P#1: All Customers Differ à Managing Customer M Heterogeneity The most basic issue facing managers making marketing mix decisions (pricing, product, promotion, place) is that all customers differ. Customers vary widely in their needs and preferences, whether real or perceived. Their desires vary even for basic commodity products (e.g., bottled water). Thus, effective marketing strategies must manage this customer heterogeneity, often through segmenting, targeting, and positioning efforts. They allow the firm to make sense of the customer landscape by identifying a manageable number of homogeneous customer groups, such that the firm can meaningfully evaluate its relative strengths and make strategically critical decisions about how to win and keep customers.
MP#2: All Customers Change à Managing Customer Dynamics Managers developing their marketing strategies must account for variation, as customers’ needs change over time. Even within a well-defined segment, members’ individual needs often evolve at different rates or directions. At some point in the future, customers who once were part of a relatively homogeneous segment will exhibit widely divergent needs and desires. A firm’s marketing strategy must account for customer dynamics to avoid becoming obsolete by identifying and understanding how a firm’s customers migrate (i.e., change), triggers of these migrations, differing needs across stages, and, ultimately, desirable positions to appeal to these customers over time.
xxii Prefac
MP#3: All Competitors React à Managing Sustainable Competitive Advantage No matter how well a firm addresses customer heterogeneity and customer dynamics, competitors will constantly try to copy its success or innovate business processes and offerings to match customers’ needs and desires better. Since all competitors react, through persistent efforts to copy and innovate, marketing managers must constantly work at building and maintaining barriers to competitive attacks. Managers build sustainable competitive advantages that are relevant for a specific target segment, by building high-quality brands, delivering innovative offerings, and developing strong customer relationships.
MP#4: All Resources Are Limited à Managing Resource Trade-offs Most marketing decisions require trade-offs across multiple objectives, because the resources available to address these needs often are interdependent and limited. When marketing strategies allocate spending to brand advertising, or innovating new products, or expanding the sales organization to build stronger relationships, they often rely on the same fixed resource pool. A firm only has so many resources, so important trade-offs are unavoidable. Managing resources optimally is critical; marketing resources provide the levers to implement what the firm learns from the first three Marketing Principles.
