Marketing Analytics
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.
I Using Excel to Summarize Marketing Data 1
1 Slicing and Dicing Marketing Data with PivotTables 3
2 Using Excel Charts to Summarize Marketing Data 29
3 Using Excel Functions to Summarize Marketing Data 59
4 Estimating Demand Curves and Using Solver to Optimize Price 85
5 Price Bundling 107
6 Nonlinear Pricing 123
7 Price Skimming and Sales 135
8 Revenue Management 143
9 Simple Linear Regression and Correlation 161
10 Using Multiple Regression to Forecast Sales 177
11 Forecasting in the Presence of Special Events 213
12 Modeling Trend and Seasonality 225
13 Ratio to Moving Average Forecasting Method 235
14 Winter’s Method 241
15 Using Neural Networks to Forecast Sales 249
16 Conjoint Analysis 263
17 Logistic Regression 285
18 Discrete Choice Analysis 303
19 Calculating Lifetime Customer Value 327
20 Using Customer Value to Value a Business 339
21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making 347
22 Allocating Marketing Resources between Customer Acquisition and Retention
23 Cluster Analysis 377
24 Collaborative Filtering 393
25 Using Classification Trees for Segmentation 403
26 Using S Curves to Forecast Sales of a New Product 415
27 The Bass Diffusion Model 427
28 Using the Copernican Principle to Predict Duration of Future Sales 439
29 Market Basket Analysis and Lift 445
30 RFM Analysis and Optimizing Direct Mail Campaigns 459
31 Using the SCAN*PRO Model and Its Variants 471
32 Allocating Retail Space and Sales Resources 483
33 Forecasting Sales from Few Data Points 495
34 Measuring the Effectiveness of Advertising 505
35 Media Selection Models 517
36 Pay per Click (PPC) Online Advertising 529
37 Principal Components Analysis (PCA) 541
38 Multidimensional Scaling (MDS) 559
39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis 577
40 Analysis of Variance: One-way ANOVA 595
41 Analysis of Variance: Two-way ANOVA 607
42 Networks 621
43 The Mathematics Behind The Tipping Point 641
44 Viral Marketing 653
45 Text Mining 663
Index 673