Marketing Analytics Data Driven Techniques with Microsoft Excel 1st editon by Wayne Winston 111837343X 9781118373439

Marketing Analytics Data Driven Techniques with Microsoft Excel 1st editon by Wayne Winston – Ebook PDF Instant Download/Delivery: 111837343X, 9781118373439

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ISBN 10: 111837343X
ISBN 13: 9781118373439
Author: Wayne L. Winston

Marketing Analytics:  Data-Driven Techniques with Microsoft Excel shows business managers and data analysts how to use a relatively simple tool–Excel—to analyze useful business information using powerful analytic techniques.    This comprehensive book shows how to use each technique to solve a practical businenss problem and achieve optimum marketing results.   Topics include: How PivotTables, charts, Excel statistical functions and array formulas can be used to describe and summarize marketing data. How to quantify customer value. Allocate the marketing budget between acquiring and retaining high-value customers Analyzing market segments to identify high-value customers Forecasting sales of existing and of new products Estimating trends and seasonality Market basket analysis for optimizing retail sales Optimizing direct mail and online campaigns Selecting media targets for advertising Optimizing product price points Price bundling and discounting Determining which new products to recommend to existing customers Viral marketing models for social media And more The author will demonstrate how to implement more than 85% of these techniques using Excel.  The other techniques require a more powerful BI tool;  for those examples, the author will use Palisades Software—readers will be able download a trial version of that software to recreate the examples for those chapters.  The book will include exercises (for each chapter), plus instructor materials.

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 Curves 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 III: 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 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 SCANPRO 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

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