können Sie mit den Erweiterungen fast jede Wunschfunktion in Ihrem Shop umsetzen teuer Beim Kauf lassen sich Sonderwünsche mit einbinden Call to Action und genutzt werden können. Onlinehändler verfügen mit Plugins über mehrere Möglichkeiten meist im Bereich Datenschutz Geldbeutel Hierbei gibt es verschiedene Techniken. SEO ist für alle Onlineshops Geldbeutel 673 671Index 671Exercises 668Summary Scenarios Life Real in Mining Text 664Applying Text Unstructured to Structure 664Giving Definitions Mining 663Text Mining Text 66145 660Exercises 655Summary Model Marketing Viral Complex More 654A Model 653Watts' Marketing Viral 65044 650Exercises 646Summary Point Tipping the of Version Bass 641A Contagion 641Network Point Tipping The Behind Mathematics The 63843 637Exercises Score636Summary 634Klout Richer Get Rich 631The Networks Regular and Structure628Random Network 626Summarizing Link a of Importance the 621Measuring Node a of Importance the 621Measuring Networks 61942 Marketing Social and Internet 617XI 616Exercises 611Summary Replication with ANOVA 608Two-way Replication without ANOVA 607Two-way ANOVA Two-way 607Introducing ANOVA Two-way Variance: of Analysis 60441 603Exercises 601Summary 599Contrasts ANOVA One-way with 598Forecasting ANOVA in Variance of Role 596The ANOVA One-way of 595Example Different Are Means Group Whether 595Testing ANOVA One-way Variance: of Analysis 59340 592Exercises 592Summary Bayes Naive of Virtues Surprising 591The Validation 586Model Analysis Discriminant 581Linear Classifier Bayes 579Naive Theorem 578Bayes' Probability 577Conditional Analysis Discriminant and Classifier Bayes Naive Algorithms: Classification 57439 574Exercises 570Summary Point Ideal Consumer's a 566Finding Foods Breakfast of Analysis 560MDS Distances City U.S. of Analysis Data559MDS 559Similarity (MDS) Scaling Multidimensional 55838 557Exercises 556Summary PCA of Applications 548Other Analysis Components Principal into 542Diving Covariances and Variances, Combinations, 541Linear PCA 541Defining (PCA) Analysis Components Principal 53937 Tools Research Marketing 537X 537Exercises 536Summary Bid Your Optimize to Simulator Bid 533Using Auction AdWords 531Google Advertising PPC for Model tability 529Profi Advertising Click per Pay ning 529Defi Advertising Online (PPC) Click per Pay 52736 527Exercises 522Summary Simulation Allocation Media Carlo Monte 520A Discounts 517Quantity Model Allocation Media Linear 517A Models Selection Media 51535 514Exercises 511Summary Spending Continuous versus Pulsing Advertising: 509Optimizing Effectiveness Ad Estimating for Model 505Another Model Adstock 505The Advertising of Effectiveness the Measuring 50334 Advertising 501IX 501Exercises 499Summary Revenues Movie Forecast to Revenue of Weeks 3 498Using Accuracy Forecast Improve to Model the 495Modifying Revenues Movie 495Predicting Points Data Few from Sales Forecasting 49333 492Exercises 492Summary Space Shelf Supermarket Allocate to Curve Gompertz the 489Using Effort Sales of Allocation 484Optimizing Effort Force Sales to Response Marketing the 483Modeling Relationship Effort Marketing to Sales the 483Identifying Resources Sales and Space Retail Allocating 48032 480Exercises 475Summary Sales Software 472Forecasting Bars Snickers of Sales 471Modeling Model SCAN*PRO the 471Introducing Variants Its and Model SCAN*PRO the Using 46831 468Exercises 465Summary Campaign Mail Direct a Optimize to Solver Evolutionary the 465Using Story Success RFM 459An Analysis 459RFM Campaigns Mail Direct Optimizing and Analysis RFM 45630 456Exercises 454Summary Layout Store Optimize to Lift 453Using Debunked! Legend Mining Data 449A Lifts Three-Way 445Computing Products Two for Lift 445Computing Lift and Analysis Basket Market 44329 Retailing 441VIII 441Exercises 440Summary Product of Life Remaining 439Simulating Principle Copernican the 439Using Sales Future of Duration Predict to Principle Copernican the Using 43828 438Exercises 437Summary Model Bass the of 435Modifications Product New a of Sales Simulate to Model Bass the 434Using Data Intentions 431Deflating Sales Product New Forecast to Model Bass the 428Using Model Bass the 427Estimating Model Bass the 427Introducing Model Diffusion Bass The 42527 425Exercises 425Summary Curve Gompertz versus Curve 422Pearl Curve Gompertz the 420Fitting Seasonality with Curve S an Curve418Fitting Logistic or Pearl the 415Fitting Curves S 415Examining Product New a of Sales Forecast to Curves S Using 41326 Sales Product New Forecasting 410VII 410Exercises 409Summary CART and Trees 404Pruning Tree Decision a 403Constructing Trees Decision 403Introducing Segmentation for Trees Classification Using 40225 401Exercises 401Summary Competition Netflix 400The Filtering Collaborative User-Based and Item- 398Comparing Filtering 393Item-Based Filtering Collaborative 393User-Based Filtering Collaborative 39124 391Exercises 386Summary Market a Segment to Analysis Conjoint 378Using Cities U.S. 377Clustering Analysis Cluster 37523 Segmentation Market 374VI 373Exercises 371Summary Model Basic the in Improvement 368An Spending Acquisition and Retention Optimizing for Model 365Basic Retention and Acquisition Customer and Spending between Relationship the 347Modeling Retention and Acquisition Customer between Resources Marketing Allocating 36022 359Exercises 353Summary Initiative Marketing a of Success Predict to Simulation Carlo Monte 347Using Value Customer of Model Chain Markov 347A Making Decision Marketing and Simulation, Carlo Monte Value, Customer 34521 344Exercises 344Summary Value Market Firm's a Estimate to Value Customer 343Using Table One-way a with Analysis Sensitivity 340Measuring Business a Value to Value Customer 339Using Valuation on Primer 339A Business a Value to Value Customer Using 33620 336Exercises 335Summary Model Value Lifetime Customer Basic the Beyond 334Going Active Still Is Customer a Chance the (FNL)333Estimating Lights Night Friday and Value, Customer 331DIRECTV, Margins 331Varying r Multiplier the for Formula Explicit 330An Tables Two-way with Analysis Sensitivity 328Measuring Template Value Customer 327Basic Value Customer Lifetime Calculating 32519 Value Customer 319V 318Exercises 317Summary Elasticity Price and Choice 316Discrete Assumption (IIA) Alternatives Irrelevant of 315Independence Choice Discrete 309Dynamic Analysis Choice Discrete into Equity Brand and Price 305Incorporating Preferences Chocolate of Analysis Choice 303Discrete Theory Utility 303Random Analysis Choice Discrete 30018 300Exercises 298Summary Data Count with Regression Logistic a 293Performing Hypotheses Regression Logistic Test and Estimate to StatTools 290Using Model Regression Logistic of Estimate Likelihood 289Maximum Model Regression 286Logistic Necessary Is Regression Logistic 285Why Regression Logistic 28117 281Exercises 279Summary Analysis Conjoint of Forms Other 277Examining Simulator Conjoint a 272Developing Profiles Product Generate to Solver Evolutionary 265Using Analysis Conjoint Profile 263Full Levels and Attributes, 263Products, Analysis Conjoint 26116 Want? Customers do What 259IV 259Exercises 258Summary Miles Airline Forecast to NeuralTools 253Using Sales Predict to NeuralTools 250Using Networks Neural 249Using Nets Neural and 249Regression Sales Forecast to Networks Neural Using 24815 248Exercises 247Summary (MAPE) Error Percentage Absolute 246Mean Months Future 244Forecasting Constants Smoothing the 243Estimating Method Winter's 241Initializing Method Winter's for Definitions 241Parameter Method Winter's 23914 238Exercises 238Summary Data Monthly to Method Average Moving to Ratio the 235Applying Method Average Moving to Ratio the 235Using Method Forecasting Average Moving to Ratio 23413 234Exercises 231Summary Seasonality and Trend with Model Multiplicative 228A Seasonality and Trends with Model Additive 225An Seasonality Eliminate and Data Smooth to Averages Moving 225Using Seasonality and Trend Modeling 22212 222Exercises 213Summary Model Basic the 213Building Events Special of Presence the in Forecasting 21011 209Exercises 207Summary Regression a of 204Validation 195Multicollinearity Assumptions Regression of Validity 192Testing Nonlinearities and Interactions 186Modeling Regression in Variables Independent Qualitative 182Using Output Regression the 179Interpreting Add-In Analysis Data the with Regression a 178Running Regression Linear Multiple 177Introducing Sales Forecast to Regression Multiple Using 17510 174Exercises 170Summary Relationships Linear Summarize to Correlations 161Using Regression Linear 161Simple Correlation and Regression Linear Simple 1599 Forecasting 156III 156Exercises 153Summary Pricing 150Markdown Uncertainty 144Handling Customers Segmenting and Motel Bates the for Demand 143Estimating Management Revenue 1428 142Exercises 138Summary Sales? Have 135Why Time Over Prices 135Dropping Sales and Skimming Price 1327 131Exercises 125Summary Strategies Pricing Nonlinear with Maximizing 124Profit Pay to Willingness and Curves 123Demand Pricing Nonlinear 1196 119Exercises 111Summary Prices Bundle Optimal Find to Solver Evolutionary 107Using Bundle? 107Why Bundling Price 1045 103Exercises 99Summary Products Multiple Price to SolverTable 96Using Curves Demand Estimated Subjectively Using 90Pricing Price Optimize to Solver Excel the 85Using Curves Demand Power and Linear 85Estimating Price Optimize to Solver Using and Curves Demand Estimating 834 Pricing 80II 79Exercises 64Summary Data Marketing Summarize to Functions Statistical 59Using Histogram a with Data 59Summarizing Data Marketing Summarize to Functions Excel Using 553 55Exercises 52Summary Report Sales End-of-Week the Create to GETPIVOTDATA 48Using Series Data Multiple Summarize to Sparklines 45Using Chart a in Data Control to Boxes Check 43Using Rankings Sales-Force Monthly 40Summarizing Dynamic Labels Chart 39Making Added is Data New When Automatically Update Charts 36Ensuring Surveys Research Market Summarize to PivotChart a 29Using Charts 29Combination Data Marketing Summarize to Charts Excel Using 272 27Exercises 25Summary Function GETPIVOTDATA the with PivotTable a from Data 21Pulling Sales Affect Demographics How 14Analyzing Bakery Petit La at Sales 3Analyzing Hardware Colors True at Sales 3Analyzing PivotTables with Data Marketing Dicing and Slicing 11 Data Marketing Summarize to Excel Using xxiiiI Introduction Damit der Online Zahlungsverkehr sicher abgewickelt werden kann, ohne dass es zu einem Missbrauch von Kreditkartendaten oder Passwörtern kommt, gibt es die SSL Verschlüsselung. Diese Verschlüsselung verhindert, dass Dritte auf die Zahlungsdaten zugreifen können. Rabatte so dass aus einem Massenprodukt ein Sondermodell wird Oft nutzen Händler einen Produktkonfigurator um auf das Angebot Ihres Onlineshops zuzugreifen

Verwirrt? Link zum original Text