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.

Table of Contents

Part I Using Excel to Summarize Marketing Data

Chapter 1 Slicing and Dicing Marketing Data with PivotTables

Chapter 2 Using Excel Charts to Summarize Marketing Data

Chapter 3 Using Excel Functions to Summarize Marketing Data

Part II Pricing

Chapter 4 Estimating Demand CuNes and Using Solver to Optimize Price

Chapter 5 Price Bundling

Chapter 6 Nonlinear Pricing

Chapter 7 Price Skimming and Sales

Chapter 8 Revenue Management

Part Ill Forecasting

Chapter 9 Simple Linear Regression and Correlation

Chapter 10 Using Multiple Regression to Forecast Sales

Chapter 11 Forecasting in the Presence of Special Events

Chapter 12 Modeling Trend and Seasonality

Chapter 13 Ratio to Moving Average Forecasting Method

Chapter 14 Winter’s Method

Chapter 15 Using Neural Networks to Forecast Sales

Part IV What do Customers Want?

Chapter 16 Conjoint Analysis

Chapter 17 Logistic Regression

Chapter 18 Discrete Choice Analysis

Part V Customer Value

Chapter 19 Calculating Lifetime Customer Value

Chapter 20 Using Customer Value to Value a Business

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

Chapter 22 Allocating Marketing Resources between Customer Acquisition and Retention

Part VI Market Segmentation

Chapter 23 Cluster Analysis

Chapter 24 Collaborative Filtering

Chapter 25 Using Classification Trees for Segmentation

Part VI Market Segmentation

Chapter 23 Cluster Analysis

Chapter 24 Collaborative Filtering

Chapter 25 Using Classification Trees for Segmentation

Part VII Forecasting New Product Sales

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

Chapter 27 The Bass Diffusion Model

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

Part VIII Retailing

Chapter 29 Market Basket Analysis and Lift

Chapter 30 RFM Analysis and Optimizing Direct Mail Campaigns

Chapter 31 Using the SCAN*PRO Model and Its Variants

Chapter 32 Allocating Retail Space and Sales Resources

Chapter 33 Forecasting Sales from Few Data Points

Part IX Advertising

Chapter 34 Measuring the Effectiveness of Advertising

Chapter 35 Media Selection Models

Chapter 36 Pay per Click (PPC) Online Advertising

Part X Marketing Research Tools

Chapter 37 Principal Components Analysis (PCA)

Chapter 38 Multidimensional Scaling (MDS)

Chapter 39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis

Chapter 40 Analysis of Variance: One-way ANOVA

Chapter 41 Analysis of Variance: Two-way ANOVA

Part XI Internet and Social Marketing

Chapter 42 Networks

Chapter 43 The Mathematics Behind The Tipping Point

Chapter 44 Viral Marketing

Chapter 45 Text Mining

About the Author

Wayne L. Winston is John and Esther Reese chaired Professor of Decision Sciences at the Indiana University Kelley School of Business and will be a Visiting Professor at the Bauer College of Business at the University of Houston. He has won more than 45 teaching awards at Indiana University. He has also written numerous journal articles and a dozen books, and has developed two online courses for Harvard Business School.