Marketing Analytics : Data-Driven Techniques with Microsoft Excel.
Wayne L. Winston
Bok Engelsk 2014 · Electronic books.
| Omfang | 1 online resource (722 pages)
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|---|---|
| Utgave | 1st ed.
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| Opplysninger | Cover -- Title Page -- Copyright -- Contents -- Introduction -- Part I Using Excel to Summarize Marketing Data -- Chapter 1 Slicing and Dicing Marketing Data with PivotTables -- Analyzing Sales at True Colors Hardware -- Analyzing Sales at La Petit Bakery -- Analyzing How Demographics Affect Sales -- Pulling Data from a PivotTable with the GETPIVOTDATA Function -- Summary -- Exercises -- Chapter 2 Using Excel Charts to Summarize Marketing Data -- Combination Charts -- Using a PivotChart to Summarize Market Research Surveys -- Ensuring Charts Update Automatically When New Data is Added -- Making Chart Labels Dynamic -- Summarizing Monthly Sales-Force Rankings -- Using Check Boxes to Control Data in a Chart -- Using Sparklines to Summarize Multiple Data Series -- Using GETPIVOTDATA to Create the End-of-Week Sales Report -- Summary -- Exercises -- Chapter 3 Using Excel Functions to Summarize Marketing Data -- Summarizing Data with a Histogram -- Using Statistical Functions to Summarize Marketing Data -- Summary -- Exercises -- Part II Pricing -- Chapter 4 Estimating Demand Curves and Using Solver to Optimize Price -- Estimating Linear and Power Demand Curves -- Using the Excel Solver to Optimize Price -- Pricing Using Subjectively Estimated Demand Curves -- Using SolverTable to Price Multiple Products -- Summary -- Exercises -- Chapter 5 Price Bundling -- Why Bundle? -- Using Evolutionary Solver to Find Optimal Bundle Prices -- Summary -- Exercises -- Chapter 6 Nonlinear Pricing -- Demand Curves and Willingness to Pay -- Profit Maximizing with Nonlinear Pricing Strategies -- Summary -- Exercises -- Chapter 7 Price Skimming and Sales -- Dropping Prices Over Time -- Why Have Sales? -- Summary -- Exercises -- Chapter 8 Revenue Management -- Estimating Demand for the Bates Motel and Segmenting Customers -- Handling Uncertainty -- Markdown Pricing.. - An Improvement in the Basic Model -- Summary -- Exercises -- Part VI Market Segmentation -- Chapter 23 Cluster Analysis -- Clustering U.S. Cities -- Using Conjoint Analysis to Segment a Market -- Summary -- Exercises -- Chapter 24 Collaborative Filtering -- User-Based Collaborative Filtering -- Item-Based Filtering -- Comparing Item- and User-Based Collaborative Filtering -- The Netflix Competition -- Summary -- Exercises -- Chapter 25 Using Classification Trees for Segmentation -- Introducing Decision Trees -- Constructing a Decision Tree -- Pruning Trees and CART -- Summary -- Exercises -- Part VII Forecasting New Product Sales -- Chapter 26 Using S Curves to Forecast Sales of a New Product -- Examining S Curves -- Fitting the Pearl or Logistic Curve -- Fitting an S Curve with Seasonality -- Fitting the Gompertz Curve -- Pearl Curve versus Gompertz Curve -- Summary -- Exercises -- Chapter 27 The Bass Diffusion Model -- Introducing the Bass Model -- Estimating the Bass Model -- Using the Bass Model to Forecast New Product Sales -- Deflating Intentions Data -- Using the Bass Model to Simulate Sales of a New Product -- Modifications of the Bass Model -- Summary -- Exercises -- Chapter 28 Using the Copernican Principle to Predict Duration of Future Sales -- Using the Copernican Principle -- Simulating Remaining Life of Product -- Summary -- Exercises -- Part VIII Retailing -- Chapter 29 Market Basket Analysis and Lift -- Computing Lift for Two Products -- Computing Three-Way Lifts -- A Data Mining Legend Debunked! -- Using Lift to Optimize Store Layout -- Summary -- Exercises -- Chapter 30 RFM Analysis and Optimizing Direct Mail Campaigns -- RFM Analysis -- An RFM Success Story -- Using the Evolutionary Solver to Optimize a Direct Mail Campaign -- Summary -- Exercises -- Chapter 31 Using the SCAN*PRO Model and Its Variants.. - Examining Other Forms of Conjoint Analysis -- Summary -- Exercises -- Chapter 17 Logistic Regression -- Why Logistic Regression Is Necessary -- Logistic Regression Model -- Maximum Likelihood Estimate of Logistic Regression Model -- Using StatTools to Estimate and Test Logistic Regression Hypotheses -- Performing a Logistic Regression with Count Data -- Summary -- Exercises -- Chapter 18 Discrete Choice Analysis -- Random Utility Theory -- Discrete Choice Analysis of Chocolate Preferences -- Incorporating Price and Brand Equity into Discrete Choice Analysis -- Dynamic Discrete Choice -- Independence of Irrelevant Alternatives (IIA) Assumption -- Discrete Choice and Price Elasticity -- Summary -- Exercises -- Part V Customer Value -- Chapter 19 Calculating Lifetime Customer Value -- Basic Customer Value Template -- Measuring Sensitivity Analysis with Two-way Tables -- An Explicit Formula for the Multiplier -- Varying Margins -- DIRECTV, Customer Value, and Friday Night Lights (FNL) -- Estimating the Chance a Customer Is Still Active -- Going Beyond the Basic Customer Lifetime Value Model -- Summary -- Exercises -- Chapter 20 Using Customer Value to Value a Business -- A Primer on Valuation -- Using Customer Value to Value a Business -- Measuring Sensitivity Analysis with a One-way Table -- Using Customer Value to Estimate a Firm's Market Value -- Summary -- Exercises -- Chapter 21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making -- A Markov Chain Model of Customer Value -- Using Monte Carlo Simulation to Predict Success of a Marketing Initiative -- Summary -- Exercises -- Chapter 22 Allocating Marketing Resources between Customer Acquisition and Retention -- Modeling the Relationship between Spending and Customer Acquisition and Retention -- Basic Model for Optimizing Retention and Acquisition Spending.. - Introducing the SCAN*PRO Model -- Modeling Sales of Snickers Bars -- Forecasting Software Sales -- Summary -- Exercises -- Chapter 32 Allocating Retail Space and Sales Resources -- Identifying the Sales to Marketing Effort Relationship -- Modeling the Marketing Response to Sales Force Effort -- Optimizing Allocation of Sales Effort -- Using the Gompertz Curve to Allocate Supermarket Shelf Space -- Summary -- Exercises -- Chapter 33 Forecasting Sales from Few Data Points -- Predicting Movie Revenues -- Modifying the Model to Improve Forecast Accuracy -- Using 3 Weeks of Revenue to Forecast Movie Revenues -- Summary -- Exercises -- Part IX Advertising -- Chapter 34 Measuring the Effectiveness of Advertising -- The Adstock Model -- Another Model for Estimating Ad Effectiveness -- Optimizing Advertising: Pulsing versus Continuous Spending -- Summary -- Exercises -- Chapter 35 Media Selection Models -- A Linear Media Allocation Model -- Quantity Discounts -- A Monte Carlo Media Allocation Simulation -- Summary -- Exercises -- Chapter 36 Pay per Click (PPC) Online Advertising -- Defining Pay per Click Advertising -- Profitability Model for PPC Advertising -- Google AdWords Auction -- Using Bid Simulator to Optimize Your Bid -- Summary -- Exercises -- Part X Marketing Research Tools -- Chapter 37 Principal Components Analysis (PCA) -- Defining PCA -- Linear Combinations, Variances, and Covariances -- Diving into Principal Components Analysis -- Other Applications of PCA -- Summary -- Exercises -- Chapter 38 Multidimensional Scaling (MDS) -- Similarity Data -- MDS Analysis of U.S. City Distances -- MDS Analysis of Breakfast Foods -- Finding a Consumer's Ideal Point -- Summary -- Exercises -- Chapter 39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis -- Conditional Probability -- Bayes' Theorem -- Naive Bayes Classifier.. - Linear Discriminant Analysis.. - Summary -- Exercises -- Part III Forecasting -- Chapter 9 Simple Linear Regression and Correlation -- Simple Linear Regression -- Using Correlations to Summarize Linear Relationships -- Summary -- Exercises -- Chapter 10 Using Multiple Regression to Forecast Sales -- Introducing Multiple Linear Regression -- Running a Regression with the Data Analysis Add-In -- Interpreting the Regression Output -- Using Qualitative Independent Variables in Regression -- Modeling Interactions and Nonlinearities -- Testing Validity of Regression Assumptions -- Multicollinearity -- Validation of a Regression -- Summary -- Exercises -- Chapter 11 Forecasting in the Presence of Special Events -- Building the Basic Model -- Summary -- Exercises -- Chapter 12 Modeling Trend and Seasonality -- Using Moving Averages to Smooth Data and Eliminate Seasonality -- An Additive Model with Trends and Seasonality -- A Multiplicative Model with Trend and Seasonality -- Summary -- Exercises -- Chapter 13 Ratio to Moving Average Forecasting Method -- Using the Ratio to Moving Average Method -- Applying the Ratio to Moving Average Method to Monthly Data -- Summary -- Exercises -- Chapter 14 Winter's Method -- Parameter Definitions for Winter's Method -- Initializing Winter's Method -- Estimating the Smoothing Constants -- Forecasting Future Months -- Mean Absolute Percentage Error (MAPE) -- Summary -- Exercises -- Chapter 15 Using Neural Networks to Forecast Sales -- Regression and Neural Nets -- Using Neural Networks -- Using NeuralTools to Predict Sales -- Using NeuralTools to Forecast Airline Miles -- Summary -- Exercises -- Part IV What do Customers Want? -- Chapter 16 Conjoint Analysis -- Products, Attributes, and Levels -- Full Profile Conjoint Analysis -- Using Evolutionary Solver to Generate Product Profiles -- Developing a Conjoint Simulator.. - 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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| Emner | |
| Sjanger | |
| Dewey | |
| ISBN | 9781118439357
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| ISBN(galt) |
