Marketing Analytics, 2ed

About the Authors v

Preface vii

Datasets xv

Chapter 1 Introduction 1

1.1 Marketing Analytics 1

1.2 Data for Marketing Analytics 2

1.3 What Are Business Intelligence, Analytics,

and Data Science? 5

1.4 Analysis 6

1.5 Exploratory Data Analysis 6

1.6 Descriptive Analysis 11

1.7 Predictive Analytics 12

1.8 Prescriptive Analytics 12

1.9 Bringing It All Together—Visualization Dashboards 13

1.10 Why Marketing Analytics—Career Prospects

and Nature of Jobs 16

1.11 Organization of the Book 20

Summary 23

Key Terms 24

Discussion Questions 24

Chapter 2 Segmentation 25

2.1 Introduction 27

2.2 Benefits of Customer Analytics 27

2.3 Factors Essential for Obtaining Benefits from

Customer Analytics 28

2.4 Segmentation Analytics 29

2.4.1 Data Collection in Segmentation 29

2.4.2 Customer Segmentation Process 29

2.4.3 Data Analysis for Customer

Segmentation 30

2.5 Cluster Analysis 32

2.5.1 Applications of Cluster Analysis

2.5.2 Examples of Cluster Analysis 34

2.5.3 Data Used for Clustering 34

2.5.4 Clustering Algorithms 36

Summary 48

Key Terms 49

Discussion Questions 49

Project 49

Chapter 3 Positioning 51

3.1 Introduction 52

3.2 Perceptual Mapping 53

3.2.1 Using Two Determinant Attributes 53

3.2.2 Using Multiple Product Attributes 54

3.2.3 Joint Perceptual Maps 54

3.2.4 Constructing a Perceptual Map 55

3.3 White Spaces 57

3.4 Umbrella Brands 57

3.5 Multidimensional Scaling 58

Summary 65

Key Terms 65

Discussion Questions 66

Project 66

Chapter 4 Product Analytics 67

4.1 Introduction 68

4.2 Analyzing Digital Products 70

4.3 Analyzing Non-Digital Products 71

4.3.1 Utility and Choice 72

4.3.2 Application of Choice Models 76

4.3.3 Conjoint Analysis—Survey Design

and Estimation 81

4.3.4 Product Attributes and Attribute Levels 82

4.3.5 Product Levels 82

4.3.6 Steps in Performing Conjoint Analysis 83

4.3.7 Value-Based Pricing 91

4.3.8 Market Forecasting 92

4.3.9 Factors That Impact New Product

Adoption 94

4.3.10 Applying the Diffusion Model in

Marketing 95

Summary 98

Key Terms 98

Discussion Questions 98

Project 99

Chapter 5 Pricing 101

5.1 Introduction 102

5.2 Goals of Pricing 102

5.3 Bundling 103

5.3.1 Illustration of Bundling 103

5.3.2 Types of Bundling 104

5.3.3 Analyzing Bundles as Promotions 105

5.4 Skimming 106

5.4.1 Illustration of Price Skimming 106

5.4.2 Appropriate Usage of Skimming 107

5.4.3 Analytics with Price Skimming 108

5.5 Revenue Management 108

5.6 Promotions 110

5.6.1 Measuring Promotional Lifts 110

5.6.2 Types of Promotions 111

5.7 Discounting 112

5.7.1 Types of Discounting 112

5.8 Price Elasticity of a Beverage Brand 114

5.8.1 Transformations on Metric in Price

Elasticity Model 116

5.8.2 Price Adjustment with Inflation

Measure 116

5.9 Candidate Models/Methodology for

Price Elasticity Measurements 120

Summary 124

Key Terms 124

Discussion Questions 124

Project 125

Annexure 5 125

Chapter 6 Marketing Mix 135

6.1 Introduction 136

6.2 Market Mix Modeling 137

6.3 Variables in Market Mix Modeling 138

6.3.1 Base Variables 138

6.3.2 Incremental Variables 138

6.3.3 Other Variables 139

6.4 Techniques of Market Mix Modeling 140

6.4.1 Regression Analysis 140

6.4.2 Non-Linear Optimization 153

6.5 Using Power BI to Visualize Marketing Mix Data 162

6.5.1 Why Unpivot? (Wide to Long) 164

6.5.2 Create a Bar Chart Showing “Total

Amount by Marketing Channel” 172

6.5.3 Filter or Slice by Marketing Channel 174

6.5.4 Use a Single Measure for All Spend

Types (Calculate Total Spend) 176

6.5.5 Performing More Advanced Analysis

(e.g., Spend per Unit Sold by Channel) 180

Summary 184

Key Terms 186

Discussion Questions 186

Project 186

Annexure 6 186

Chapter 7 Customer Journey 199

7.1 Introduction 200

7.2 Importance of Customer Journey 200

7.3 What Is Customer Journey Mapping? 201

7.4 Customer Journey Mapping and Use of Analytics 203

7.5 How to Map a Customer’s Journey 203

7.5.1 Data Gathering 203

7.5.2 Creating a Buyer Persona 204

7.5.3 Determining the Stages of a

Customer’s Journey 204

7.5.4 Determining the Touchpoints of a

Customer’s Journey 204

7.5.5 Asking Customers 204

7.5.6 Identifying the Pain Points in a

Customer’s Journey 205

7.5.7 Fixing the Problems 205

7.5.8 Updating 205

7.6 What Does Analytics with Customer Journeys

Involve? 206

7.7 Customer Journey Use Case for a Beverage Brand 209

7.8 Journey of a Loyal Customer 209

7.9 Principal Component Analysis 211

7.9.1 Variance by Principal Components 212

7.10 Applying Principal Components to Brand

Evaluations Data 213

Summary 221

Key Terms 221

Discussion Questions 222

Project 222

Annexure 7 222

Chapter 8 Nurturing Customers 233

8.1 Introduction 234

8.2 Metrics for Tracking Customer Experience 234

8.2.1 Customer Feedback Metrics 235

8.2.2 Behavior-Derived Customer Metric 239

8.3 Upgrading Customers: Use Case of Upselling 240

8.4 Logistic Regression Analysis 243

8.4.1 Estimation of Model Parameters β 245

8.4.2 Diagnostic Tests with Logistic

Regression

8.4.3 Interpretation of Effect of a Metric

on Log-Odds 247

8.5 Use of Logistic Regression as a Classification

Technique 248

8.6 Analysis Approach with Services Marketing Metrics 251

8.7 Inferring Customer Satisfaction Through

Sentiment Analysis 252

8.7.1 Challenges and Considerations 253

8.8 Python Code for Deriving Sentiment from

Text Data 254

8.9 Application Exercise 254

Summary 261

Key Terms 262

Discussion Questions 262

Project 262

Annexure 8 263

Chapter 9 Customer Analytics 281

9.1 Introduction 282

9.1.1 Building a Customer Persona 284

9.1.2 Benefits of Creating Customer

Personas 284

9.1.3 Part of Buyer Persona 285

9.2 Customer Lifetime Value 288

9.2.1 Need for Calculating CLV 288

9.2.2 Calculating CLV 288

9.2.3 Boosting CLV 289

9.2.4 Identifying Profitable Customers 289

9.2.5 Computing CLV 290

9.2.6 Example of CLV 291

9.3 Churn Analytics 296

9.3.1 Calculating Churn Rate 297

9.3.2 Identifying Churn 298

9.3.3 Importance of Customer Retention 298

9.4 Ways to Retain Customers for Physical and

E-Commerce Retailers 300

9.5 Visualizing Churn and LTV with Power BI 302

Summary 314

Key Terms 314

Discussion Questions 314

Project 315

Annexure 9 315

Chapter 10 Digital Analytics: Metrics and Measurement 329

10.1 Introduction 330

10.2 Important Web Metrics 331

10.2.1 Overall Website Traffic 331

10.2.2 Conversion Rate 332

10.2.3 Exit Rate 332

10.2.4 Bounce Rate 335

10.2.5 Click-Through Rate 336

10.2.6 Page Views 337

10.2.7 Unique Page Views 338

10.2.8 Sessions 338

10.2.9 Time on Site 340

10.2.10 Unique Visitors 340

10.3 Attribution Challenge and Shapley Regression 341

10.4 Test and Control or A/B Testing 345

10.4.1 ANOVA and Regression 346

10.4.2 ANOVA: Linear Regression Model 349

10.5 Example Use Case: Webpage Design with A/B

Testing 351

10.6 Search Engine Marketing 353

10.6.1 Why Is SEM Important? 353

10.6.2 SEM Platforms 354

10.6.3 How Does an Ad Win the Ad Auction? 354

10.7 Search Engine Optimization 355

10.7.1 Working of SEO 356

10.7.2 Factors Affecting SEO 356

10.8 SEM or SEO: Which Is the Optimal Choice? 357

10.9 Social Media Analytics 362

10.10 App Marketing Metrics 363

Summary 365

Key Terms 366

Discussion Questions 366

Project 366

Annexure 10 367

Chapter 11 Artificial Intelligence and Machine Learning 373

11.1 Introduction 374

11.2 Importance of AI in Marketing 375

11.3 Key Applications of AI in Marketing 376

11.3.1 Personalization of Online Experience 376

11.3.2 Chatbots 377

11.3.3 AI-Powered Dynamic Emailing 379

11.4 Common Terminologies—AI, ML, and DL 379

11.5 Important Concepts of ML 380

11.5.1 Variance and Bias Trade-Off 380

11.5.2 Training 381

11.5.3 Validation and Test 382

11.5.4 The k-Fold Cross-Validation 382

11.5.5 Bootstrapping/Bagging Validation 384

11.5.6 Regularization Penalty 385

11.5.7 Decision Trees 386

11.6 Random Forests 390

11.6.1 Data Description and Formatting

before Classification Model 391

11.7 Model Evaluation Using ROC, AUC, and

Confusion Matrix 396

11.8 Boosting Trees 402

11.9 Variable Importance 405

11.10 Simple Feed-Forward Network 406

11.11 Deep Neural Network 408

11.12 Image Recognition 412

11.12.1 Convolutional Neural Network 414

11.12.2 Marketing Applications of Image

Recognition 417

11.13 Working with Textual Data 418

11.13.1 Recurrent Neural Network 423

11.14 Recommendation Systems 424

11.14.1 Collaborative Filtering 425

11.14.2 Content-based Filtering 435

11.15 Generative and Traditional AI 442

11.15.1 Generative AI Preliminaries 442

11.15.2 Generative AI Use Cases in Marketing 445

11.15.3 Customer Service Revolutionized by

Generative AI 451

11.15.4 Implementation Best Practices for

Generative AI 453

11.15.5 Various Generative AI Tools Used

by Marketers 454

11.16 Challenges Involved with AI 455

Summary 457

Key Terms 459

Discussion Questions 459

Chapter 12 Data Visualization 461

12.1 Introduction 461

12.2 Necessity of Data Visualization 462

12.2.1 Understand Patterns and Trends 463

12.2.2 Simplified Understanding of

Complex Data 464

12.2.3 Data-Driven Storytelling 464

12.3 Charts 466

12.3.1 Bar Chart 466

12.3.2 Stacked Bar Graph 467

12.3.3 Histogram 468

12.3.4 Box Plot 469

12.3.5 Violin Plot 469

12.3.6 Pie Chart 470

12.3.7 Scatter Plot 471

12.3.8 Bubble Chart 472

12.3.9 Line Chart 473

12.3.10 Heat Map 473

12.4 Visualizations Useful with Common Data

Science Techniques 475

12.4.1 Visualizations for Communicating

Regression Inference 476

12.4.2 Visualization Inference for Predictive

Machine Learning Models 479

12.4.3 Communicating Inference with

Segmentation/Clustering 480

12.5 Storytelling with Power BI Dashboard 482

12.5.1 Brand Health Dashboard 482

12.5.2 Building a Branding Dashboard 483

12.6 Conclusion 496

Summary 496

Key Terms 497

Discussion Questions 497

Project 497

Appendix 1: Installing and Using R 499

Appendix 2: Installing Python 505

Endnotes 511

Index 517