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Marketing Analytics: Data-Driven Techniques with Microsoft Excel

Wayne L. Winston

ISBN: 978-1-118-37343-9 January 2014 720 Pages


Helping tech-savvy marketers and data analysts solve real-world business problems with Excel

Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results.

Practical exercises in each chapter help you apply and reinforce techniques as you learn.

  • Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools
  • Reveals how to target and retain profitable customers and avoid high-risk customers
  • Helps you forecast sales and improve response rates for marketing campaigns
  • Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising
  • Covers social media, viral marketing, and how to exploit both effectively

Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel.

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Introduction xxiii

I Using Excel to Summarize Marketing Data  1

1 Slicing and Dicing Marketing Data with PivotTables  3

Analyzing Sales at True Colors Hardware   3

Analyzing Sales at La Petit Bakery    14

Analyzing How Demographics Affect Sales 21

Pulling Data from a PivotTable with the GETPIVOTDATA Function 25

Summary  27

Exercises 27

2 Using Excel Charts to Summarize Marketing Data  29

Combination Charts 29

Using a PivotChart to Summarize Market Research Surveys 36

Ensuring Charts Update Automatically When New Data is Added   39

Making Chart Labels Dynamic 40

Summarizing Monthly Sales-Force Rankings   43

Using Check Boxes to Control Data in a Chart 45

Using Sparklines to Summarize Multiple Data Series 48

Using GETPIVOTDATA to Create the End-of-Week Sales Report 52

Summary  55

Exercises 55

3 Using Excel Functions to Summarize Marketing Data  59

Summarizing Data with a Histogram   59

Using Statistical Functions to Summarize Marketing Data 64

Summary  79

Exercises 80

II Pricing  83

4 Estimating Demand Curves and Using Solver to Optimize Price    85

Estimating Linear and Power Demand Curves 85

Using the Excel Solver to Optimize Price   90

Pricing Using Subjectively Estimated Demand Curves 96

Using SolverTable to Price Multiple Products 99

Summary 103

Exercises  104

5 Price Bundling 107

Why Bundle? 107

Using Evolutionary Solver to Find Optimal Bundle Prices  111

Summary 119

Exercises  119

6 Nonlinear Pricing  123

Demand Curves and Willingness to Pay 124

Profit Maximizing with Nonlinear Pricing Strategies 125

Summary 131

Exercises  132

7 Price Skimming and Sales 135

Dropping Prices Over Time    135

Why Have Sales? 138

Summary 142

Exercises  142

8 Revenue Management  143

Estimating Demand for the Bates Motel and Segmenting Customers 144

Handling Uncertainty    150

Markdown Pricing 153

Summary 156

Exercises  156

III Forecasting  159

9 Simple Linear Regression and Correlation 161

Simple Linear Regression   161

Using Correlations to Summarize Linear Relationships 170

Summary 174

Exercises  175

10 Using Multiple Regression to Forecast Sales 177

Introducing Multiple Linear Regression   178

Running a Regression with the Data Analysis Add-In   179

Interpreting the Regression Output   182

Using Qualitative Independent Variables in Regression 186

Modeling Interactions and Nonlinearities 192

Testing Validity of Regression Assumptions   195

Multicollinearity 204

Validation of a Regression   207

Summary 209

Exercises  210

11 Forecasting in the Presence of Special Events   213

Building the Basic Model   213

Summary 222

Exercises  222

12 Modeling Trend and Seasonality 225

Using Moving Averages to Smooth Data and Eliminate Seasonality    225

An Additive Model with Trends and Seasonality 228

A Multiplicative Model with Trend and Seasonality 231

Summary 234

Exercises  234

13 Ratio to Moving Average Forecasting Method 235

Using the Ratio to Moving Average Method 235

Applying the Ratio to Moving Average Method to Monthly Data 238

Summary 238

Exercises  239

14 Winter’s Method   241

Parameter Definitions for Winter’s Method   241

Initializing Winter’s Method    243

Estimating the Smoothing Constants 244

Forecasting Future Months  246

Mean Absolute Percentage Error (MAPE) 247

Summary 248

Exercises  248

15 Using Neural Networks to Forecast Sales   249

Regression and Neural Nets    249

Using Neural Networks 250

Using NeuralTools to Predict Sales 253

Using NeuralTools to Forecast Airline Miles  258

Summary 259

Exercises  259

IV What do Customers Want?   261

16 Conjoint Analysis   263

Products, Attributes, and Levels    263

Full Profile Conjoint Analysis    265

Using Evolutionary Solver to Generate Product Profiles 272

Developing a Conjoint Simulator 277

Examining Other Forms of Conjoint Analysis 279

Summary 281

Exercises  281

17 Logistic Regression    285

Why Logistic Regression Is Necessary 286

Logistic Regression Model  289

Maximum Likelihood Estimate of Logistic Regression Model 290

Using StatTools to Estimate and Test Logistic Regression Hypotheses 293

Performing a Logistic Regression with Count Data 298

Summary 300

Exercises  300

18 Discrete Choice Analysis 303

Random Utility Theory 303

Discrete Choice Analysis of Chocolate Preferences 305

Incorporating Price and Brand Equity into Discrete Choice Analysis 309

Dynamic Discrete Choice   315

Independence of Irrelevant Alternatives (IIA) Assumption  316

Discrete Choice and Price Elasticity 317

Summary 318

Exercises  319

V Customer Value 325

19 Calculating Lifetime Customer Value 327

Basic Customer Value Template    328

Measuring Sensitivity Analysis with Two-way Tables   330

An Explicit Formula for the Multiplier   r 331

Varying Margins 331

DIRECTV, Customer Value, and Friday Night Lights (FNL)333

Estimating the Chance a Customer Is Still Active   334

Going Beyond the Basic Customer Lifetime Value Model  335

Summary 336

Exercises  336

20 Using Customer Value to Value a Business 339

A Primer on Valuation    339

Using Customer Value to Value a Business   340

Measuring Sensitivity Analysis with a One-way Table   343

Using Customer Value to Estimate a Firm’s Market Value  344

Summary 344

Exercises  345

21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making   347

A Markov Chain Model of Customer Value   347

Using Monte Carlo Simulation to Predict Success of a Marketing Initiative    353

Summary 359

Exercises  360

22 Allocating Marketing Resources between Customer Acquisition and Retention 347

Modeling the Relationship between Spending and Customer Acquisition and Retention 365

Basic Model for Optimizing Retention and Acquisition Spending 368

An Improvement in the Basic Model   371

Summary 373

Exercises  374

VI Market Segmentation 375

23 Cluster Analysis   377

Clustering U.S. Cities    378

Using Conjoint Analysis to Segment a Market  386

Summary 391

Exercises  391

24 Collaborative Filtering  393

User-Based Collaborative Filtering 393

Item-Based Filtering  398

Comparing Item- and User-Based Collaborative Filtering  400

The Netflix Competition 401

Summary 401

Exercises  402

25 Using Classification Trees for Segmentation 403

Introducing Decision Trees  403

Constructing a Decision Tree 404

Pruning Trees and CART 409

Summary 410

Exercises  410

VII Forecasting New Product Sales  413

26 Using S Curves to Forecast Sales of a New Product  415

Examining S Curves  415

Fitting the Pearl or Logistic Curve418

Fitting an S Curve with Seasonality 420

Fitting the Gompertz Curve    422

Pearl Curve versus Gompertz Curve 425

Summary 425

Exercises  425

27 The Bass Diffusion Model 427

Introducing the Bass Model    427

Estimating the Bass Model  428

Using the Bass Model to Forecast New Product Sales   431

Deflating Intentions Data   434

Using the Bass Model to Simulate Sales of a New Product 435

Modifications of the Bass Model    437

Summary 438

Exercises  438

28 Using the Copernican Principle to Predict Duration of Future Sales   439

Using the Copernican Principle  439

Simulating Remaining Life of Product 440

Summary 441

Exercises  441

VIII Retailing 443

29 Market Basket Analysis and Lift 445

Computing Lift for Two Products 445

Computing Three-Way Lifts    449

A Data Mining Legend Debunked! 453

Using Lift to Optimize Store Layout   454

Summary 456

Exercises  456

30 RFM Analysis and Optimizing Direct Mail Campaigns 459

RFM Analysis 459

An RFM Success Story    465

Using the Evolutionary Solver to Optimize a Direct Mail Campaign 465

Summary 468

Exercises  468

31 Using the SCAN*PRO Model and Its Variants   471

Introducing the SCAN*PRO Model 471

Modeling Sales of Snickers Bars    472

Forecasting Software Sales  475

Summary 480

Exercises  480

32 Allocating Retail Space and Sales Resources 483

Identifying the Sales to Marketing Effort Relationship   483

Modeling the Marketing Response to Sales Force Effort 484

Optimizing Allocation of Sales Effort 489

Using the Gompertz Curve to Allocate Supermarket Shelf Space   492

Summary 492

Exercises  493

33 Forecasting Sales from Few Data Points   495

Predicting Movie Revenues    495

Modifying the Model to Improve Forecast Accuracy 498

Using 3 Weeks of Revenue to Forecast Movie Revenues 499

Summary 501

Exercises  501

IX Advertising 503

34 Measuring the Effectiveness of Advertising 505

The Adstock Model  505

Another Model for Estimating Ad Effectiveness 509

Optimizing Advertising: Pulsing versus Continuous Spending 511

Summary 514

Exercises  515

35 Media Selection Models   517

A Linear Media Allocation Model 517

Quantity Discounts 520

A Monte Carlo Media Allocation Simulation 522

Summary 527

Exercises  527

36 Pay per Click (PPC) Online Advertising 529

Defi ning Pay per Click Advertising 529

Profi tability Model for PPC Advertising   531

Google AdWords Auction  533

Using Bid Simulator to Optimize Your Bid 536

Summary 537

Exercises  537

X Marketing Research Tools    539

37 Principal Components Analysis (PCA)  541

Defining PCA 541

Linear Combinations, Variances, and Covariances   542

Diving into Principal Components Analysis   548

Other Applications of PCA  556

Summary 557

Exercises  558

38 Multidimensional Scaling (MDS) 559

Similarity Data559

MDS Analysis of U.S. City Distances   560

MDS Analysis of Breakfast Foods    566

Finding a Consumer’s Ideal Point 570

Summary 574

Exercises  574

39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis 577

Conditional Probability 578

Bayes’ Theorem 579

Naive Bayes Classifier    581

Linear Discriminant Analysis    586

Model Validation    591

The Surprising Virtues of Naive Bayes 592

Summary 592

Exercises  593

40 Analysis of Variance: One-way ANOVA 595

Testing Whether Group Means Are Different 595

Example of One-way ANOVA 596

The Role of Variance in ANOVA    598

Forecasting with One-way ANOVA 599

Contrasts 601

Summary 603

Exercises  604

41 Analysis of Variance: Two-way ANOVA 607

Introducing Two-way ANOVA 607

Two-way ANOVA without Replication 608

Two-way ANOVA with Replication 611

Summary 616

Exercises  617

XI Internet and Social Marketing 619

42 Networks 621

Measuring the Importance of a Node 621

Measuring the Importance of a Link   626

Summarizing Network Structure628

Random and Regular Networks    631

The Rich Get Richer  634

Klout Score636

Summary 637

Exercises  638

43 The Mathematics Behind The Tipping Point 641

Network Contagion  641

A Bass Version of the Tipping Point   646

Summary 650

Exercises  650

44 Viral Marketing 653

Watts’ Model 654

A More Complex Viral Marketing Model 655

Summary 660

Exercises  661

45 Text Mining 663

Text Mining Definitions 664

Giving Structure to Unstructured Text   664

Applying Text Mining in Real Life Scenarios 668

Summary 671

Exercises  671

Index 673

Excel Files for Marketing Analytics
Exercise Solution Files for Marketing Analytics