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Advanced techniques in web intelligence [[electronic resource] ] . 2 : web user browsing behaviour and preference analysis / / Juan D. Velasquez, Vasile Palade, Lakhmi C. Jain (eds.)
| Advanced techniques in web intelligence [[electronic resource] ] . 2 : web user browsing behaviour and preference analysis / / Juan D. Velasquez, Vasile Palade, Lakhmi C. Jain (eds.) |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, c2013 |
| Descrizione fisica | 1 online resource (XVI, 184 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
VelasquezJuan D
PaladeVasile <1964-> JainL. C |
| Collana | Studies in computational intelligence |
| Soggetto topico |
Web usage mining
Internet World Wide Web Web browsing |
| ISBN |
9783642333262
3642333265 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | New Trends in Web User Behaviour Analysis -- Web Usage Data Pre-processing -- Cognitive Science forWeb Usage Analysis -- Web Usage Mining: Discovering Usage Patterns for Web Applications -- Web Opinion Mining and Sentimental Analysis -- Web Usage Based Adaptive Systems -- Recommender Systems: Sources of Knowledge and Evaluation Metrics. |
| Record Nr. | UNINA-9910437765203321 |
| Berlin ; ; New York, : Springer, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced Web metrics with Google Analytics [[electronic resource] /] / Brian Clifton
| Advanced Web metrics with Google Analytics [[electronic resource] /] / Brian Clifton |
| Autore | Clifton Brian <1969-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Indianapolis, IN, : Wiley, c2010 |
| Descrizione fisica | 1 online resource (531 p.) |
| Disciplina | 006.3 |
| Collana | Serious skills. |
| Soggetto topico |
Web usage mining
Internet users - Statistics - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-54762-3
9786612547621 0-470-63492-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Web Metrics with Google Analytics, 2nd Edition; Acknowledgments; About the Author; Contents; Foreword; Introduction; Who Should Read This Book; What Is Covered in This Book; GA IQ Coupon; How to Contact the Author; Part I: Measuring Success; Chapter 1: Why Understanding Your Web Traffic Is Important to Your Business; Website Measurement-Why Do This?; Information Web Analytics Can Provide; Where to Start; Decisions Web Analytics Can Help You Make; The ROI of Web Analytics; How Web Analytics Helps You Understand Your Web Traffic; Where Web Analytics Fits In; Where to Get Help; Summary
Chapter 2: Available Methodologies and Their Accuracy Page Tags and Logfiles; Cookies in Web Analytics; Understanding Web Analytics Data Accuracy; Improving the Accuracy of Web Analytics Data; Privacy Considerations for the Web Analytics Industry; Summary; Chapter 3: Google Analytics Features, Benefits, and Limitations; Key Features and Capabilities of Google Analytics; How Google Analytics Works; What Google Analytics Cannot Do; Google Analytics and Privacy; How Is Google Analytics Different?; What Is Urchin?; Summary; Part II: Using Google Analytics Reports Chapter 4: Using the Google Analytics Interface Discoverability and Initial Report Access; Navigating Your Way Around: Report Layout; Summary; Chapter 5: Reports Explained; The Dashboard Overview; The Top Reports; Understanding Page Value; Understanding Data Sampling; Summary; Part III: Implementing Google Analytics; Chapter 6: Getting Up and Running with Google Analytics; Creating Your Google Analytics Account; Tagging Your Pages; Back Up: Keeping a Local Copy of Your Data; Using Accounts and Profiles; Agencies and Hosting Providers: Setting Up Client Accounts Getting Ad Words Data: Linking to Your Ad Words Account Getting Ad Sense Data: Linking to Your Ad Sense Account; Common Pre-implementation Questions; Summary; Chapter 7: Advanced Implementation; _trackPageview(): the Google Analytics Workhorse; Tracking E-commerce Transactions; Campaign Tracking; Event Tracking; Customizing the GATC; Summary; Chapter 8: Best-Practices Configuration Guide; Initial Configuration; Goal Conversions and Funnels; Why Segmentation Is Important; Choosing Advanced Segments versus Profile Filters; Profile Segments: Segmenting Visitors Using Filters Report Segments: Segmenting Visitors Using Advanced Segments Summary; Chapter 9: Google Analytics Hacks; Why Hack an Existing Product?; Customizing the List of Recognized Search Engines; Labeling Visitors, Sessions, and Pages; Tracking Error Pages and Broken Links; Tracking Referral URLs from Pay-Per-Click Networks; Site Overlay: Differentiating Links to the Same Page; Matching Specific Transactions to Specific Referral Data; Tracking Links to Direct Downloads; Changing the Referrer Credited for a Goal Conversion; Roll-up Reporting; Summary Part IV: Using Visitor Data to Drive Website Improvement |
| Record Nr. | UNINA-9910458827603321 |
Clifton Brian <1969->
|
||
| Indianapolis, IN, : Wiley, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced Web metrics with Google Analytics [[electronic resource] /] / Brian Clifton
| Advanced Web metrics with Google Analytics [[electronic resource] /] / Brian Clifton |
| Autore | Clifton Brian <1969-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Indianapolis, IN, : Wiley, c2010 |
| Descrizione fisica | 1 online resource (531 p.) |
| Disciplina | 006.3 |
| Collana | Serious skills. |
| Soggetto topico |
Web usage mining
Internet users - Statistics - Data processing |
| ISBN |
0-470-63494-4
1-282-54762-3 9786612547621 0-470-63492-8 |
| Classificazione | ST 515 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Web Metrics with Google Analytics, 2nd Edition; Acknowledgments; About the Author; Contents; Foreword; Introduction; Who Should Read This Book; What Is Covered in This Book; GA IQ Coupon; How to Contact the Author; Part I: Measuring Success; Chapter 1: Why Understanding Your Web Traffic Is Important to Your Business; Website Measurement-Why Do This?; Information Web Analytics Can Provide; Where to Start; Decisions Web Analytics Can Help You Make; The ROI of Web Analytics; How Web Analytics Helps You Understand Your Web Traffic; Where Web Analytics Fits In; Where to Get Help; Summary
Chapter 2: Available Methodologies and Their Accuracy Page Tags and Logfiles; Cookies in Web Analytics; Understanding Web Analytics Data Accuracy; Improving the Accuracy of Web Analytics Data; Privacy Considerations for the Web Analytics Industry; Summary; Chapter 3: Google Analytics Features, Benefits, and Limitations; Key Features and Capabilities of Google Analytics; How Google Analytics Works; What Google Analytics Cannot Do; Google Analytics and Privacy; How Is Google Analytics Different?; What Is Urchin?; Summary; Part II: Using Google Analytics Reports Chapter 4: Using the Google Analytics Interface Discoverability and Initial Report Access; Navigating Your Way Around: Report Layout; Summary; Chapter 5: Reports Explained; The Dashboard Overview; The Top Reports; Understanding Page Value; Understanding Data Sampling; Summary; Part III: Implementing Google Analytics; Chapter 6: Getting Up and Running with Google Analytics; Creating Your Google Analytics Account; Tagging Your Pages; Back Up: Keeping a Local Copy of Your Data; Using Accounts and Profiles; Agencies and Hosting Providers: Setting Up Client Accounts Getting Ad Words Data: Linking to Your Ad Words Account Getting Ad Sense Data: Linking to Your Ad Sense Account; Common Pre-implementation Questions; Summary; Chapter 7: Advanced Implementation; _trackPageview(): the Google Analytics Workhorse; Tracking E-commerce Transactions; Campaign Tracking; Event Tracking; Customizing the GATC; Summary; Chapter 8: Best-Practices Configuration Guide; Initial Configuration; Goal Conversions and Funnels; Why Segmentation Is Important; Choosing Advanced Segments versus Profile Filters; Profile Segments: Segmenting Visitors Using Filters Report Segments: Segmenting Visitors Using Advanced Segments Summary; Chapter 9: Google Analytics Hacks; Why Hack an Existing Product?; Customizing the List of Recognized Search Engines; Labeling Visitors, Sessions, and Pages; Tracking Error Pages and Broken Links; Tracking Referral URLs from Pay-Per-Click Networks; Site Overlay: Differentiating Links to the Same Page; Matching Specific Transactions to Specific Referral Data; Tracking Links to Direct Downloads; Changing the Referrer Credited for a Goal Conversion; Roll-up Reporting; Summary Part IV: Using Visitor Data to Drive Website Improvement |
| Record Nr. | UNINA-9910792450503321 |
|
Clifton Brian <1969->
|
||
| Indianapolis, IN, : Wiley, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in web mining and web usage analysis : 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Philadelphia, PA, USA, August 20, 2006 : revised papers / / Olfa Nasraoui (ed.)
| Advances in web mining and web usage analysis : 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Philadelphia, PA, USA, August 20, 2006 : revised papers / / Olfa Nasraoui (ed.) |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (XII, 252 p.) |
| Disciplina | 006.3 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Internet users
Web usage mining |
| ISBN | 3-540-77485-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Adaptive Website Design Using Caching Algorithms -- Incorporating Usage Information into Average-Clicks Algorithm -- Nearest-Biclusters Collaborative Filtering with Constant Values -- Fast Categorization of Web Documents Represented by Graphs -- Leveraging Structural Knowledge for Hierarchically-Informed Keyword Weight Propagation in the Web -- How to Define Searching Sessions on Web Search Engines -- Incorporating Concept Hierarchies into Usage Mining Based Recommendations -- A Random-Walk Based Scoring Algorithm Applied to Recommender Engines -- Towards a Scalable kNN CF Algorithm: Exploring Effective Applications of Clustering -- Detecting Profile Injection Attacks in Collaborative Filtering: A Classification-Based Approach -- Predicting the Political Sentiment of Web Log Posts Using Supervised Machine Learning Techniques Coupled with Feature Selection -- Analysis of Web Search Engine Query Session and Clicked Documents -- Understanding Content Reuse on the Web: Static and Dynamic Analyses. |
| Altri titoli varianti |
Eighth International Workshop on Knowledge Discovery on the Web
8th International Workshop on Knowledge Discovery on the Web International Workshop on Knowledge Discovery on the Web WebKDD 2006 |
| Record Nr. | UNISA-996465381703316 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Data smart : using data science to transform information into insight / / John W. Foreman
| Data smart : using data science to transform information into insight / / John W. Foreman |
| Autore | Foreman John W |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Indianapolis : , : Wiley, , [2014] |
| Descrizione fisica | 1 online resource (434 p.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Web sites - Design Web usage mining |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-83986-2
1-118-66148-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask; Some Sample Data; Moving Quickly with the Control Button; Copying Formulas and Data Quickly; Formatting Cells; Paste Special Values; Inserting Charts; Locating the Find and Replace Menus; Formulas for Locating and Pulling Values; Using VLOOKUP to Merge Data; Filtering and Sorting; Using PivotTables; Using Array Formulas; Solving Stuff with Solver; OpenSolver: I Wish We Didn't Need This, but We Do; Wrapping Up
Chapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base Girls Dance with Girls, Boys Scratch Their Elbows; Getting Real: K-Means Clustering Subscribers in E-mail Marketing; Joey Bag O' Donuts Wholesale Wine Emporium; The Initial Dataset; Determining What to Measure; Start with Four Clusters; Euclidean Distance: Measuring Distances as the Crow Flies; Distances and Cluster Assignments for Everybody!; Solving for the Cluster Centers; Making Sense of the Results; Getting the Top Deals by Cluster; The Silhouette: A Good Way to Let Different K Values Duke It Out How about Five Clusters? Solving for Five Clusters; Getting the Top Deals for All Five Clusters; Computing the Silhouette for 5-Means Clustering; K-Medians Clustering and Asymmetric Distance Measurements; Using K-Medians Clustering; Getting a More Appropriate Distance Metric; Putting It All in Excel; The Top Deals for the 5-Medians Clusters; Wrapping Up; Chapter 3 Naive Bayes and the Incredible Lightness of Being an Idiot; When You Name a Product Mandrill, You're Going to Get Some Signal and Some Noise; The World's Fastest Intro to Probability Theory; Totaling Conditional Probabilities Joint Probability, the Chain Rule, and Independence What Happens in a Dependent Situation?; Bayes Rule; Using Bayes Rule to Create an AI Model; High-Level Class Probabilities Are Often Assumed to Be Equal; A Couple More Odds and Ends; Let's Get This Excel Party Started; Removing Extraneous Punctuation; Splitting on Spaces; Counting Tokens and Calculating Probabilities; And We Have a Model! Let's Use It; Wrapping Up; Chapter 4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself; Why Should Data Scientists Know Optimization? Starting with a Simple Trade-Off Representing the Problem as a Polytope; Solving by Sliding the Level Set; The Simplex Method: Rooting around the Corners; Working in Excel; There's a Monster at the End of This Chapter; Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model; You Use a Blending Model; Let's Start with Some Specs; Coming Back to Consistency; Putting the Data into Excel; Setting Up the Problem in Solver; Lowering Your Standards; Dead Squirrel Removal: The Minimax Formulation; If-Then and the "Big M" Constraint Multiplying Variables: Cranking Up the Volume to 11 |
| Record Nr. | UNINA-9910464267003321 |
|
Foreman John W
|
||
| Indianapolis : , : Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data smart : using data science to transform information into insight / / John W. Foreman
| Data smart : using data science to transform information into insight / / John W. Foreman |
| Autore | Foreman John W. |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Indianapolis : , : Wiley, , [2014] |
| Descrizione fisica | 1 online resource (434 p.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Web sites - Design Web usage mining |
| ISBN |
1-118-83986-2
1-118-66148-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask; Some Sample Data; Moving Quickly with the Control Button; Copying Formulas and Data Quickly; Formatting Cells; Paste Special Values; Inserting Charts; Locating the Find and Replace Menus; Formulas for Locating and Pulling Values; Using VLOOKUP to Merge Data; Filtering and Sorting; Using PivotTables; Using Array Formulas; Solving Stuff with Solver; OpenSolver: I Wish We Didn't Need This, but We Do; Wrapping Up
Chapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base Girls Dance with Girls, Boys Scratch Their Elbows; Getting Real: K-Means Clustering Subscribers in E-mail Marketing; Joey Bag O' Donuts Wholesale Wine Emporium; The Initial Dataset; Determining What to Measure; Start with Four Clusters; Euclidean Distance: Measuring Distances as the Crow Flies; Distances and Cluster Assignments for Everybody!; Solving for the Cluster Centers; Making Sense of the Results; Getting the Top Deals by Cluster; The Silhouette: A Good Way to Let Different K Values Duke It Out How about Five Clusters? Solving for Five Clusters; Getting the Top Deals for All Five Clusters; Computing the Silhouette for 5-Means Clustering; K-Medians Clustering and Asymmetric Distance Measurements; Using K-Medians Clustering; Getting a More Appropriate Distance Metric; Putting It All in Excel; The Top Deals for the 5-Medians Clusters; Wrapping Up; Chapter 3 Naive Bayes and the Incredible Lightness of Being an Idiot; When You Name a Product Mandrill, You're Going to Get Some Signal and Some Noise; The World's Fastest Intro to Probability Theory; Totaling Conditional Probabilities Joint Probability, the Chain Rule, and Independence What Happens in a Dependent Situation?; Bayes Rule; Using Bayes Rule to Create an AI Model; High-Level Class Probabilities Are Often Assumed to Be Equal; A Couple More Odds and Ends; Let's Get This Excel Party Started; Removing Extraneous Punctuation; Splitting on Spaces; Counting Tokens and Calculating Probabilities; And We Have a Model! Let's Use It; Wrapping Up; Chapter 4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself; Why Should Data Scientists Know Optimization? Starting with a Simple Trade-Off Representing the Problem as a Polytope; Solving by Sliding the Level Set; The Simplex Method: Rooting around the Corners; Working in Excel; There's a Monster at the End of This Chapter; Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model; You Use a Blending Model; Let's Start with Some Specs; Coming Back to Consistency; Putting the Data into Excel; Setting Up the Problem in Solver; Lowering Your Standards; Dead Squirrel Removal: The Minimax Formulation; If-Then and the "Big M" Constraint Multiplying Variables: Cranking Up the Volume to 11 |
| Record Nr. | UNINA-9910788939203321 |
|
Foreman John W.
|
||
| Indianapolis : , : Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data smart : using data science to transform information into insight / / John W. Foreman
| Data smart : using data science to transform information into insight / / John W. Foreman |
| Autore | Foreman John W. |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Indianapolis : , : Wiley, , [2014] |
| Descrizione fisica | 1 online resource (434 p.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Web sites - Design Web usage mining |
| ISBN |
1-118-83986-2
1-118-66148-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask; Some Sample Data; Moving Quickly with the Control Button; Copying Formulas and Data Quickly; Formatting Cells; Paste Special Values; Inserting Charts; Locating the Find and Replace Menus; Formulas for Locating and Pulling Values; Using VLOOKUP to Merge Data; Filtering and Sorting; Using PivotTables; Using Array Formulas; Solving Stuff with Solver; OpenSolver: I Wish We Didn't Need This, but We Do; Wrapping Up
Chapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base Girls Dance with Girls, Boys Scratch Their Elbows; Getting Real: K-Means Clustering Subscribers in E-mail Marketing; Joey Bag O' Donuts Wholesale Wine Emporium; The Initial Dataset; Determining What to Measure; Start with Four Clusters; Euclidean Distance: Measuring Distances as the Crow Flies; Distances and Cluster Assignments for Everybody!; Solving for the Cluster Centers; Making Sense of the Results; Getting the Top Deals by Cluster; The Silhouette: A Good Way to Let Different K Values Duke It Out How about Five Clusters? Solving for Five Clusters; Getting the Top Deals for All Five Clusters; Computing the Silhouette for 5-Means Clustering; K-Medians Clustering and Asymmetric Distance Measurements; Using K-Medians Clustering; Getting a More Appropriate Distance Metric; Putting It All in Excel; The Top Deals for the 5-Medians Clusters; Wrapping Up; Chapter 3 Naive Bayes and the Incredible Lightness of Being an Idiot; When You Name a Product Mandrill, You're Going to Get Some Signal and Some Noise; The World's Fastest Intro to Probability Theory; Totaling Conditional Probabilities Joint Probability, the Chain Rule, and Independence What Happens in a Dependent Situation?; Bayes Rule; Using Bayes Rule to Create an AI Model; High-Level Class Probabilities Are Often Assumed to Be Equal; A Couple More Odds and Ends; Let's Get This Excel Party Started; Removing Extraneous Punctuation; Splitting on Spaces; Counting Tokens and Calculating Probabilities; And We Have a Model! Let's Use It; Wrapping Up; Chapter 4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself; Why Should Data Scientists Know Optimization? Starting with a Simple Trade-Off Representing the Problem as a Polytope; Solving by Sliding the Level Set; The Simplex Method: Rooting around the Corners; Working in Excel; There's a Monster at the End of This Chapter; Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model; You Use a Blending Model; Let's Start with Some Specs; Coming Back to Consistency; Putting the Data into Excel; Setting Up the Problem in Solver; Lowering Your Standards; Dead Squirrel Removal: The Minimax Formulation; If-Then and the "Big M" Constraint Multiplying Variables: Cranking Up the Volume to 11 |
| Altri titoli varianti | Using data science to transform information into insight |
| Record Nr. | UNINA-9910973988803321 |
|
Foreman John W.
|
||
| Indianapolis : , : Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digital und web analytics : Metriken auswerten, besucherverhalten verstehen, website optimieren / / Marco Hassler ; lektorat, Sabine Schulz
| Digital und web analytics : Metriken auswerten, besucherverhalten verstehen, website optimieren / / Marco Hassler ; lektorat, Sabine Schulz |
| Autore | Hassler Marco |
| Edizione | [4., aktualisierte auflage.] |
| Pubbl/distr/stampa | [Frechen, Germany] : , : MITP, , 2017 |
| Descrizione fisica | 1 online resource (487 pages) : illustrations (some color) |
| Disciplina | 006.312 |
| Soggetto topico |
Web usage mining
Internet users - Statistics - Data processing |
| ISBN | 3-95845-361-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ger |
| Record Nr. | UNINA-9910156302403321 |
|
Hassler Marco
|
||
| [Frechen, Germany] : , : MITP, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Einführung in die suchmaschinenoptimierung (SEO) und -marketing (SEM) : mit dem Schwerpunkt Google / / Holger Weber
| Einführung in die suchmaschinenoptimierung (SEO) und -marketing (SEM) : mit dem Schwerpunkt Google / / Holger Weber |
| Autore | Weber Holger |
| Pubbl/distr/stampa | Hamburg, [Germany] : , : Diplomica Verlag, , 2014 |
| Descrizione fisica | 1 online resource (98 p.) |
| Disciplina | 025.04252 |
| Soggetto topico |
Search engines
Web site development Web usage mining |
| Soggetto genere / forma | Electronic books. |
| ISBN | 3-8428-4855-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ger |
| Nota di contenuto |
Inhalt; 1 Einleitung; 1.1 Ziel des Buches; 1.2 Aufbau des Buches; 2 Grundlagen von Suchmaschinen; 2.1 Zeitliche Entwicklung der Suchmaschinen; 2.2 Marktanteile; 2.3 Arten von Suchmaschinen; 2.4 Aufbau von Suchmaschinen; 2.5 Aufbau von Ergebnisseiten; 3 Suchmaschinen-Optimierung; 3.1 Abgrenzung und Erklärung der wichtigsten Begriffe; 3.2 Aufbau des Suchmaschinen-Algorithmus; 3.3 On the Page - Optimization; 3.4 Off the Page - Optimization; 3.5 Negative Einflussfaktoren; 3.6 Grenzen der Optimierung; 3.7 Tools für SEO; 4 Suchmaschinen Marketing; 4.1 Stärken des Online Marketing
4.2 Arten von Online Marketing4.3 Planung einer SEM-Kampagne; 4.4 Überblick über Produkte für SEM; 4.5 Nachteile des SEM; 5 Analyse von Praxisdaten; 5.1 Ergebnisse des BVDW Fragebogens zu SEM und SEO; 5.2 Analysen zweier AdWords Kampagnen; 6 Web-Controlling; 6.1 Definition von Web-Controlling; 6.2 Kennzahlen; 6.3 Methoden; 6.4 Web-Controlling Software; 7 Fazit; Abkürzungsverzeichnis; Abbildungsverzeichnis; Tabellenverzeichnis; Literatur; Buch-Quellen; Online-Quellen |
| Record Nr. | UNINA-9910460650803321 |
|
Weber Holger
|
||
| Hamburg, [Germany] : , : Diplomica Verlag, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Einführung in die suchmaschinenoptimierung (SEO) und -marketing (SEM) : mit dem Schwerpunkt Google / / Holger Weber
| Einführung in die suchmaschinenoptimierung (SEO) und -marketing (SEM) : mit dem Schwerpunkt Google / / Holger Weber |
| Autore | Weber Holger |
| Pubbl/distr/stampa | Hamburg, [Germany] : , : Diplomica Verlag, , 2014 |
| Descrizione fisica | 1 online resource (98 p.) |
| Disciplina | 025.04252 |
| Soggetto topico |
Search engines
Web site development Web usage mining |
| ISBN | 3-8428-4855-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ger |
| Nota di contenuto |
Inhalt; 1 Einleitung; 1.1 Ziel des Buches; 1.2 Aufbau des Buches; 2 Grundlagen von Suchmaschinen; 2.1 Zeitliche Entwicklung der Suchmaschinen; 2.2 Marktanteile; 2.3 Arten von Suchmaschinen; 2.4 Aufbau von Suchmaschinen; 2.5 Aufbau von Ergebnisseiten; 3 Suchmaschinen-Optimierung; 3.1 Abgrenzung und Erklärung der wichtigsten Begriffe; 3.2 Aufbau des Suchmaschinen-Algorithmus; 3.3 On the Page - Optimization; 3.4 Off the Page - Optimization; 3.5 Negative Einflussfaktoren; 3.6 Grenzen der Optimierung; 3.7 Tools für SEO; 4 Suchmaschinen Marketing; 4.1 Stärken des Online Marketing
4.2 Arten von Online Marketing4.3 Planung einer SEM-Kampagne; 4.4 Überblick über Produkte für SEM; 4.5 Nachteile des SEM; 5 Analyse von Praxisdaten; 5.1 Ergebnisse des BVDW Fragebogens zu SEM und SEO; 5.2 Analysen zweier AdWords Kampagnen; 6 Web-Controlling; 6.1 Definition von Web-Controlling; 6.2 Kennzahlen; 6.3 Methoden; 6.4 Web-Controlling Software; 7 Fazit; Abkürzungsverzeichnis; Abbildungsverzeichnis; Tabellenverzeichnis; Literatur; Buch-Quellen; Online-Quellen |
| Record Nr. | UNINA-9910787459103321 |
|
Weber Holger
|
||
| Hamburg, [Germany] : , : Diplomica Verlag, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
