Marketing Analytics: Data-Driven Techniques with Microsoft Excel

  • 9h 6m
  • Wayne L. Winston
  • John Wiley & Sons (US)
  • 2014

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.

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.

In this Book

  • Slicing and Dicing Marketing Data with PivotTables
  • Using Excel Charts to Summarize Marketing Data
  • Using Excel Functions to Summarize Marketing Data
  • Estimating Demand Curves and Using Solver to Optimize Price
  • Price Bundling
  • Nonlinear Pricing
  • Price Skimming and Sales
  • Revenue Management
  • Simple Linear Regression and Correlation
  • Using Multiple Regression to Forecast Sales
  • Forecasting in the Presence of Special Events
  • Modeling Trend and Seasonality
  • Ratio to Moving Average Forecasting Method
  • Winter's Method
  • Using Neural Networks to Forecast Sales
  • Conjoint Analysis
  • Logistic Regression
  • Discrete Choice Analysis
  • Calculating Lifetime Customer Value
  • Using Customer Value to Value a Business
  • Customer Value, Monte Carlo Simulation, and Marketing Decision Making
  • Allocating Marketing Resources between Customer Acquisition and Retention
  • Cluster Analysis
  • Collaborative Filtering
  • Using Classification Trees for Segmentation
  • Using S Curves to Forecast Sales of a New Product
  • The Bassl Diffusion Mode
  • Using the Copernican Principle to Predict Duration of Future Sales
  • Market Basket Analysis and Lift
  • RFM Analysis and Optimizing Direct Mail Campaigns
  • Using the SCAN*PRO Model and Its Variants
  • Allocating Retail Space and Sales Resources
  • Forecasting Sales from Few Data Points
  • Measuring the Effectiveness of Advertising
  • Media Selection Models
  • Pay per Click (PPC) Online Advertising
  • Principal Components Analysis (PCA)
  • Multidimensional Scaling (MDS)
  • Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis
  • Analysis of Variance: One-way ANOVA
  • Analysis of Variance: Two-way ANOVA
  • Networks
  • The Mathematics Behind The Tipping Point
  • Viral Marketing
  • Text Mining


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