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Marketing Analytics – Data–Driven Techniques with Microsoft Excel

Data–Driven Techniques with Microsoft Excel

Paperback Engels 2014 9781118373439
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

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.

Specificaties

ISBN13:9781118373439
Taal:Engels
Bindwijze:paperback
Aantal pagina's:720

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Inhoudsopgave

Introduction xxiii
<p>I Using Excel to Summarize Marketing Data&nbsp; 1</p>
<p>1 Slicing and Dicing Marketing Data with PivotTables&nbsp; 3</p>
<p>Analyzing Sales at True Colors Hardware&nbsp;&nbsp; 3</p>
<p>Analyzing Sales at La Petit Bakery&nbsp;&nbsp;&nbsp; 14</p>
<p>Analyzing How Demographics Affect Sales 21</p>
<p>Pulling Data from a PivotTable with the GETPIVOTDATA Function 25</p>
<p>Summary&nbsp; 27</p>
<p>Exercises 27</p>
<p>2 Using Excel Charts to Summarize Marketing Data&nbsp; 29</p>
<p>Combination Charts 29</p>
<p>Using a PivotChart to Summarize Market Research Surveys 36</p>
<p>Ensuring Charts Update Automatically When New Data is Added&nbsp;&nbsp; 39</p>
<p>Making Chart Labels Dynamic 40</p>
<p>Summarizing Monthly Sales–Force Rankings&nbsp;&nbsp; 43</p>
<p>Using Check Boxes to Control Data in a Chart 45</p>
<p>Using Sparklines to Summarize Multiple Data Series 48</p>
<p>Using GETPIVOTDATA to Create the End–of–Week Sales Report 52</p>
<p>Summary&nbsp; 55</p>
<p>Exercises 55</p>
<p>3 Using Excel Functions to Summarize Marketing Data&nbsp; 59</p>
<p>Summarizing Data with a Histogram&nbsp;&nbsp; 59</p>
<p>Using Statistical Functions to Summarize Marketing Data 64</p>
<p>Summary&nbsp; 79</p>
<p>Exercises 80</p>
<p>II Pricing&nbsp; 83</p>
<p>4 Estimating Demand Curves and Using Solver to Optimize Price&nbsp;&nbsp;&nbsp; 85</p>
<p>Estimating Linear and Power Demand Curves 85</p>
<p>Using the Excel Solver to Optimize Price&nbsp;&nbsp; 90</p>
<p>Pricing Using Subjectively Estimated Demand Curves 96</p>
<p>Using SolverTable to Price Multiple Products 99</p>
<p>Summary 103</p>
<p>Exercises&nbsp; 104</p>
<p>5 Price Bundling 107</p>
<p>Why Bundle? 107</p>
<p>Using Evolutionary Solver to Find Optimal Bundle Prices&nbsp; 111</p>
<p>Summary 119</p>
<p>Exercises&nbsp; 119</p>
<p>6 Nonlinear Pricing&nbsp; 123</p>
<p>Demand Curves and Willingness to Pay 124</p>
<p>Profit Maximizing with Nonlinear Pricing Strategies 125</p>
<p>Summary 131</p>
<p>Exercises&nbsp; 132</p>
<p>7 Price Skimming and Sales 135</p>
<p>Dropping Prices Over Time&nbsp;&nbsp;&nbsp; 135</p>
<p>Why Have Sales? 138</p>
<p>Summary 142</p>
<p>Exercises&nbsp; 142</p>
<p>8 Revenue Management&nbsp; 143</p>
<p>Estimating Demand for the Bates Motel and Segmenting Customers 144</p>
<p>Handling Uncertainty&nbsp;&nbsp;&nbsp; 150</p>
<p>Markdown Pricing 153</p>
<p>Summary 156</p>
<p>Exercises&nbsp; 156</p>
<p>III Forecasting&nbsp; 159</p>
<p>9 Simple Linear Regression and Correlation 161</p>
<p>Simple Linear Regression&nbsp;&nbsp; 161</p>
<p>Using Correlations to Summarize Linear Relationships 170</p>
<p>Summary 174</p>
<p>Exercises&nbsp; 175</p>
<p>10 Using Multiple Regression to Forecast Sales 177</p>
<p>Introducing Multiple Linear Regression&nbsp;&nbsp; 178</p>
<p>Running a Regression with the Data Analysis Add–In&nbsp;&nbsp; 179</p>
<p>Interpreting the Regression Output&nbsp;&nbsp; 182</p>
<p>Using Qualitative Independent Variables in Regression 186</p>
<p>Modeling Interactions and Nonlinearities 192</p>
<p>Testing Validity of Regression Assumptions&nbsp;&nbsp; 195</p>
<p>Multicollinearity 204</p>
<p>Validation of a Regression&nbsp;&nbsp; 207</p>
<p>Summary 209</p>
<p>Exercises&nbsp; 210</p>
<p>11 Forecasting in the Presence of Special Events&nbsp;&nbsp; 213</p>
<p>Building the Basic Model&nbsp;&nbsp; 213</p>
<p>Summary 222</p>
<p>Exercises&nbsp; 222</p>
<p>12 Modeling Trend and Seasonality 225</p>
<p>Using Moving Averages to Smooth Data and Eliminate Seasonality&nbsp;&nbsp;&nbsp; 225</p>
<p>An Additive Model with Trends and Seasonality 228</p>
<p>A Multiplicative Model with Trend and Seasonality 231</p>
<p>Summary 234</p>
<p>Exercises&nbsp; 234</p>
<p>13 Ratio to Moving Average Forecasting Method 235</p>
<p>Using the Ratio to Moving Average Method 235</p>
<p>Applying the Ratio to Moving Average Method to Monthly Data 238</p>
<p>Summary 238</p>
<p>Exercises&nbsp; 239</p>
<p>14 Winter s Method&nbsp;&nbsp; 241</p>
<p>Parameter Definitions for Winter s Method&nbsp;&nbsp; 241</p>
<p>Initializing Winter s Method&nbsp;&nbsp;&nbsp; 243</p>
<p>Estimating the Smoothing Constants 244</p>
<p>Forecasting Future Months&nbsp; 246</p>
<p>Mean Absolute Percentage Error (MAPE) 247</p>
<p>Summary 248</p>
<p>Exercises&nbsp; 248</p>
<p>15 Using Neural Networks to Forecast Sales&nbsp;&nbsp; 249</p>
<p>Regression and Neural Nets&nbsp;&nbsp;&nbsp; 249</p>
<p>Using Neural Networks 250</p>
<p>Using NeuralTools to Predict Sales 253</p>
<p>Using NeuralTools to Forecast Airline Miles&nbsp; 258</p>
<p>Summary 259</p>
<p>Exercises&nbsp; 259</p>
<p>IV What do Customers Want?&nbsp;&nbsp; 261</p>
<p>16 Conjoint Analysis&nbsp;&nbsp; 263</p>
<p>Products, Attributes, and Levels&nbsp;&nbsp;&nbsp; 263</p>
<p>Full Profile Conjoint Analysis&nbsp;&nbsp;&nbsp; 265</p>
<p>Using Evolutionary Solver to Generate Product Profiles 272</p>
<p>Developing a Conjoint Simulator 277</p>
<p>Examining Other Forms of Conjoint Analysis 279</p>
<p>Summary 281</p>
<p>Exercises&nbsp; 281</p>
<p>17 Logistic Regression&nbsp;&nbsp;&nbsp; 285</p>
<p>Why Logistic Regression Is Necessary 286</p>
<p>Logistic Regression Model&nbsp; 289</p>
<p>Maximum Likelihood Estimate of Logistic Regression Model 290</p>
<p>Using StatTools to Estimate and Test Logistic Regression Hypotheses 293</p>
<p>Performing a Logistic Regression with Count Data 298</p>
<p>Summary 300</p>
<p>Exercises&nbsp; 300</p>
<p>18 Discrete Choice Analysis 303</p>
<p>Random Utility Theory 303</p>
<p>Discrete Choice Analysis of Chocolate Preferences 305</p>
<p>Incorporating Price and Brand Equity into Discrete Choice Analysis 309</p>
<p>Dynamic Discrete Choice&nbsp;&nbsp; 315</p>
<p>Independence of Irrelevant Alternatives (IIA) Assumption&nbsp; 316</p>
<p>Discrete Choice and Price Elasticity 317</p>
<p>Summary 318</p>
<p>Exercises&nbsp; 319</p>
<p>V Customer Value 325</p>
<p>19 Calculating Lifetime Customer Value 327</p>
<p>Basic Customer Value Template&nbsp;&nbsp;&nbsp; 328</p>
<p>Measuring Sensitivity Analysis with Two–way Tables&nbsp;&nbsp; 330</p>
<p>An Explicit Formula for the Multiplier&nbsp;&nbsp; r 331</p>
<p>Varying Margins 331</p>
<p>DIRECTV, Customer Value, and Friday Night Lights (FNL)333</p>
<p>Estimating the Chance a Customer Is Still Active&nbsp;&nbsp; 334</p>
<p>Going Beyond the Basic Customer Lifetime Value Model&nbsp; 335</p>
<p>Summary 336</p>
<p>Exercises&nbsp; 336</p>
<p>20 Using Customer Value to Value a Business 339</p>
<p>A Primer on Valuation&nbsp;&nbsp;&nbsp; 339</p>
<p>Using Customer Value to Value a Business&nbsp;&nbsp; 340</p>
<p>Measuring Sensitivity Analysis with a One–way Table&nbsp;&nbsp; 343</p>
<p>Using Customer Value to Estimate a Firm s Market Value&nbsp; 344</p>
<p>Summary 344</p>
<p>Exercises&nbsp; 345</p>
<p>21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making&nbsp;&nbsp; 347</p>
<p>A Markov Chain Model of Customer Value&nbsp;&nbsp; 347</p>
<p>Using Monte Carlo Simulation to Predict Success of a Marketing Initiative&nbsp;&nbsp;&nbsp; 353</p>
<p>Summary 359</p>
<p>Exercises&nbsp; 360</p>
<p>22 Allocating Marketing Resources between Customer Acquisition and Retention 347</p>
<p>Modeling the Relationship between Spending and Customer Acquisition and Retention 365</p>
<p>Basic Model for Optimizing Retention and Acquisition Spending 368</p>
<p>An Improvement in the Basic Model&nbsp;&nbsp; 371</p>
<p>Summary 373</p>
<p>Exercises&nbsp; 374</p>
<p>VI Market Segmentation 375</p>
<p>23 Cluster Analysis&nbsp;&nbsp; 377</p>
<p>Clustering U.S. Cities&nbsp;&nbsp;&nbsp; 378</p>
<p>Using Conjoint Analysis to Segment a Market&nbsp; 386</p>
<p>Summary 391</p>
<p>Exercises&nbsp; 391</p>
<p>24 Collaborative Filtering&nbsp; 393</p>
<p>User–Based Collaborative Filtering 393</p>
<p>Item–Based Filtering&nbsp; 398</p>
<p>Comparing Item– and User–Based Collaborative Filtering&nbsp; 400</p>
<p>The Netflix Competition 401</p>
<p>Summary 401</p>
<p>Exercises&nbsp; 402</p>
<p>25 Using Classification Trees for Segmentation 403</p>
<p>Introducing Decision Trees&nbsp; 403</p>
<p>Constructing a Decision Tree 404</p>
<p>Pruning Trees and CART 409</p>
<p>Summary 410</p>
<p>Exercises&nbsp; 410</p>
<p>VII Forecasting New Product Sales&nbsp; 413</p>
<p>26 Using S Curves to Forecast Sales of a New Product&nbsp; 415</p>
<p>Examining S Curves&nbsp; 415</p>
<p>Fitting the Pearl or Logistic Curve418</p>
<p>Fitting an S Curve with Seasonality 420</p>
<p>Fitting the Gompertz Curve&nbsp;&nbsp;&nbsp; 422</p>
<p>Pearl Curve versus Gompertz Curve 425</p>
<p>Summary 425</p>
<p>Exercises&nbsp; 425</p>
<p>27 The Bass Diffusion Model 427</p>
<p>Introducing the Bass Model&nbsp;&nbsp;&nbsp; 427</p>
<p>Estimating the Bass Model&nbsp; 428</p>
<p>Using the Bass Model to Forecast New Product Sales&nbsp;&nbsp; 431</p>
<p>Deflating Intentions Data&nbsp;&nbsp; 434</p>
<p>Using the Bass Model to Simulate Sales of a New Product 435</p>
<p>Modifications of the Bass Model&nbsp;&nbsp;&nbsp; 437</p>
<p>Summary 438</p>
<p>Exercises&nbsp; 438</p>
<p>28 Using the Copernican Principle to Predict Duration of Future Sales&nbsp;&nbsp; 439</p>
<p>Using the Copernican Principle&nbsp; 439</p>
<p>Simulating Remaining Life of Product 440</p>
<p>Summary 441</p>
<p>Exercises&nbsp; 441</p>
<p>VIII Retailing 443</p>
<p>29 Market Basket Analysis and Lift 445</p>
<p>Computing Lift for Two Products 445</p>
<p>Computing Three–Way Lifts&nbsp;&nbsp;&nbsp; 449</p>
<p>A Data Mining Legend Debunked! 453</p>
<p>Using Lift to Optimize Store Layout&nbsp;&nbsp; 454</p>
<p>Summary 456</p>
<p>Exercises&nbsp; 456</p>
<p>30 RFM Analysis and Optimizing Direct Mail Campaigns 459</p>
<p>RFM Analysis 459</p>
<p>An RFM Success Story&nbsp;&nbsp;&nbsp; 465</p>
<p>Using the Evolutionary Solver to Optimize a Direct Mail Campaign 465</p>
<p>Summary 468</p>
<p>Exercises&nbsp; 468</p>
<p>31 Using the SCAN∗PRO Model and Its Variants&nbsp;&nbsp; 471</p>
<p>Introducing the SCAN∗PRO Model 471</p>
<p>Modeling Sales of Snickers Bars&nbsp;&nbsp;&nbsp; 472</p>
<p>Forecasting Software Sales&nbsp; 475</p>
<p>Summary 480</p>
<p>Exercises&nbsp; 480</p>
<p>32 Allocating Retail Space and Sales Resources 483</p>
<p>Identifying the Sales to Marketing Effort Relationship&nbsp;&nbsp; 483</p>
<p>Modeling the Marketing Response to Sales Force Effort 484</p>
<p>Optimizing Allocation of Sales Effort 489</p>
<p>Using the Gompertz Curve to Allocate Supermarket Shelf Space&nbsp;&nbsp; 492</p>
<p>Summary 492</p>
<p>Exercises&nbsp; 493</p>
<p>33 Forecasting Sales from Few Data Points&nbsp;&nbsp; 495</p>
<p>Predicting Movie Revenues&nbsp;&nbsp;&nbsp; 495</p>
<p>Modifying the Model to Improve Forecast Accuracy 498</p>
<p>Using 3 Weeks of Revenue to Forecast Movie Revenues 499</p>
<p>Summary 501</p>
<p>Exercises&nbsp; 501</p>
<p>IX Advertising 503</p>
<p>34 Measuring the Effectiveness of Advertising 505</p>
<p>The Adstock Model&nbsp; 505</p>
<p>Another Model for Estimating Ad Effectiveness 509</p>
<p>Optimizing Advertising: Pulsing versus Continuous Spending 511</p>
<p>Summary 514</p>
<p>Exercises&nbsp; 515</p>
<p>35 Media Selection Models&nbsp;&nbsp; 517</p>
<p>A Linear Media Allocation Model 517</p>
<p>Quantity Discounts 520</p>
<p>A Monte Carlo Media Allocation Simulation 522</p>
<p>Summary 527</p>
<p>Exercises&nbsp; 527</p>
<p>36 Pay per Click (PPC) Online Advertising 529</p>
<p>Defi ning Pay per Click Advertising 529</p>
<p>Profi tability Model for PPC Advertising&nbsp;&nbsp; 531</p>
<p>Google AdWords Auction&nbsp; 533</p>
<p>Using Bid Simulator to Optimize Your Bid 536</p>
<p>Summary 537</p>
<p>Exercises&nbsp; 537</p>
<p>X Marketing Research Tools&nbsp;&nbsp;&nbsp; 539</p>
<p>37 Principal Components Analysis (PCA)&nbsp; 541</p>
<p>Defining PCA 541</p>
<p>Linear Combinations, Variances, and Covariances&nbsp;&nbsp; 542</p>
<p>Diving into Principal Components Analysis&nbsp;&nbsp; 548</p>
<p>Other Applications of PCA&nbsp; 556</p>
<p>Summary 557</p>
<p>Exercises&nbsp; 558</p>
<p>38 Multidimensional Scaling (MDS) 559</p>
<p>Similarity Data559</p>
<p>MDS Analysis of U.S. City Distances&nbsp;&nbsp; 560</p>
<p>MDS Analysis of Breakfast Foods&nbsp;&nbsp;&nbsp; 566</p>
<p>Finding a Consumer s Ideal Point 570</p>
<p>Summary 574</p>
<p>Exercises&nbsp; 574</p>
<p>39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis 577</p>
<p>Conditional Probability 578</p>
<p>Bayes Theorem 579</p>
<p>Naive Bayes Classifier&nbsp;&nbsp;&nbsp; 581</p>
<p>Linear Discriminant Analysis&nbsp;&nbsp;&nbsp; 586</p>
<p>Model Validation&nbsp;&nbsp;&nbsp; 591</p>
<p>The Surprising Virtues of Naive Bayes 592</p>
<p>Summary 592</p>
<p>Exercises&nbsp; 593</p>
<p>40 Analysis of Variance: One–way ANOVA 595</p>
<p>Testing Whether Group Means Are Different 595</p>
<p>Example of One–way ANOVA 596</p>
<p>The Role of Variance in ANOVA&nbsp;&nbsp;&nbsp; 598</p>
<p>Forecasting with One–way ANOVA 599</p>
<p>Contrasts 601</p>
<p>Summary 603</p>
<p>Exercises&nbsp; 604</p>
<p>41 Analysis of Variance: Two–way ANOVA 607</p>
<p>Introducing Two–way ANOVA 607</p>
<p>Two–way ANOVA without Replication 608</p>
<p>Two–way ANOVA with Replication 611</p>
<p>Summary 616</p>
<p>Exercises&nbsp; 617</p>
<p>XI Internet and Social Marketing 619</p>
<p>42 Networks 621</p>
<p>Measuring the Importance of a Node 621</p>
<p>Measuring the Importance of a Link&nbsp;&nbsp; 626</p>
<p>Summarizing Network Structure628</p>
<p>Random and Regular Networks&nbsp;&nbsp;&nbsp; 631</p>
<p>The Rich Get Richer&nbsp; 634</p>
<p>Klout Score636</p>
<p>Summary 637</p>
<p>Exercises&nbsp; 638</p>
<p>43 The Mathematics Behind The Tipping Point 641</p>
<p>Network Contagion&nbsp; 641</p>
<p>A Bass Version of the Tipping Point&nbsp;&nbsp; 646</p>
<p>Summary 650</p>
<p>Exercises&nbsp; 650</p>
<p>44 Viral Marketing 653</p>
<p>Watts Model 654</p>
<p>A More Complex Viral Marketing Model 655</p>
<p>Summary 660</p>
<p>Exercises&nbsp; 661</p>
<p>45 Text Mining 663</p>
<p>Text Mining Definitions 664</p>
<p>Giving Structure to Unstructured Text&nbsp;&nbsp; 664</p>
<p>Applying Text Mining in Real Life Scenarios 668</p>
<p>Summary 671</p>
<p>Exercises&nbsp; 671</p>
<p>Index 673</p>

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        Marketing Analytics – Data–Driven Techniques with Microsoft Excel