{"id":603,"date":"2023-03-29T17:11:44","date_gmt":"2023-03-29T14:11:44","guid":{"rendered":"https:\/\/acua.qcri.org\/blog\/?p=603"},"modified":"2023-03-29T21:01:39","modified_gmt":"2023-03-29T18:01:39","slug":"understanding-audiences-customers-and-users-via-analytics-table-of-contents","status":"publish","type":"post","link":"https:\/\/acua.qcri.org\/blog\/understanding-audiences-customers-and-users-via-analytics-table-of-contents\/","title":{"rendered":"Understanding Audiences, Customers, and Users via Analytics – Table of Contents"},"content":{"rendered":"
\"Understanding
Understanding Audiences, Customers, and Users via Analytics \u2013 An Introduction to the Employment of Web, Social, and Other Types of Digital People Data<\/figcaption><\/figure>\n

Here is the Table of Content for our book on Audiences, Customers, and Users Analytics! As you can see, the book addresses the major areas of analytics for audiences, customers, and users!<\/p>\n

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Part I Introduction to Analytics<\/span>
1<\/span> The Foundations of Web Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 3<\/span>
\u00a0 \u00a0 1.1<\/span> Defining Digital Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 4<\/span>
\u00a0 \u00a0 1.2<\/span> Realizing the Role of Web analytics . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 5<\/span>
\u00a0 \u00a0 1.3<\/span> Key Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 8<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 9<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 9<\/span>
2<\/span> The Foundations of Social Media Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 11<\/span>
\u00a0 \u00a0 2.1<\/span> Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 11<\/span>
\u00a0 \u00a0 2.2<\/span> The Role of Social media Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 12<\/span>
\u00a0 \u00a0 2.3<\/span> Social Media Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 13<\/span>
\u00a0 \u00a0 2.4<\/span> Key Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 14<\/span>
\u00a0 \u00a0 2.5<\/span> Social Media User Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 14<\/span>
\u00a0 \u00a0 2.6<\/span> Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 15<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 16<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 17<\/span>
3<\/span> Web and Social Media Analytics Strategy<\/span> . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 19<\/span>
\u00a0 \u00a0 3.1<\/span> Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 19<\/span>
\u00a0 \u00a0 3.2<\/span> The Benefits of a Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 20<\/span>
\u00a0 \u00a0 3.3<\/span> Web Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 21<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 3.3.1<\/span> What is a Web Strategy? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 21<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 3.3.2<\/span> Key Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 21<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 3.3.3<\/span> Steps in Building a Successful Web Analytics Strategy . . . . .<\/span> 22<\/span>
\u00a0 \u00a0 3.4<\/span> Social Media Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 24<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 3.4.1<\/span> What is a Social Media Strategy? . . . . . . . . . . . . . . . . . . . . . . .<\/span> 24<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 3.4.2<\/span> Key Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 25<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 3.4.3<\/span> Steps in Building a Successful Social Media Strategy . . . . . .<\/span> 25<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 28<\/span><\/p>\n

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 29<\/span><\/div>\n
<\/div>\n
Part II Data Sources<\/span>
4<\/span> Data Collection Methods<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 33<\/span>
\u00a0 \u00a0 4.1<\/span> Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 33<\/span>
\u00a0 \u00a0 4.2<\/span> Online Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 34<\/span>
\u00a0 \u00a0 4.3<\/span> Crowdsourcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 36<\/span>
\u00a0 \u00a0 4.4<\/span> Eye Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 37<\/span>
\u00a0 \u00a0 4.5<\/span> Mouse Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 41<\/span>
\u00a0 \u00a0 4.6<\/span> Search Logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 43<\/span>
\u00a0 \u00a0 4.7<\/span> Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 45<\/span>
\u00a0 \u00a0 4.8<\/span> Social Media APIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 46<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 49<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 49<\/span><\/div>\n
<\/div>\n
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Part III Data Quality and Preparation<\/span>
5<\/span> Data Quality Assessment<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 55<\/span>
\u00a0 \u00a0 5.1<\/span> Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 55<\/span>
\u00a0 \u00a0 5.2<\/span> Data Quality Assessment Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 56<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 5.2.1<\/span> Completeness and Consistency . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 57<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 5.2.2<\/span> Validity, Accuracy, and Timeliness . . . . . . . . . . . . . . . . . . . . . .<\/span> 57<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 5.2.3<\/span> Examples of Data Quality Assessment Aspects . . . . . . . . . . .<\/span> 57<\/span>
\u00a0 \u00a0 5.3<\/span> Data Quality Assessment Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 58<\/span>
\u00a0 \u00a0 5.4<\/span> Data Quality Assessment Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 59<\/span>
\u00a0 \u00a0 5.5<\/span> Data Quality Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 60<\/span>
\u00a0 \u00a0 5.6<\/span> The Challenges of Data Quality Assessment . . . . . . . . . . . . . . . . . . . .<\/span> 60<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 5.6.1<\/span> Examples of Challenges in Data Quality Assessment . . . . . .<\/span> 62<\/span>
\u00a0 \u00a0 5.7<\/span> Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 62<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 63<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 63<\/span>
6<\/span> Data Preprocessing<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 65<\/span>
\u00a0 \u00a0 6.1<\/span> Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 65<\/span>
\u00a0 \u00a0 6.2<\/span> Data Preprocessing in Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 67<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 6.2.1<\/span> Characterizing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 67<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 6.2.2<\/span> Dimensionality Reduction and Data Transformation . . . . . . .<\/span> 67<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 6.2.3<\/span> Data Enrichment and Validation . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 68<\/span>
\u00a0 \u00a0 6.3<\/span> Data Preprocessing Tools and Usage . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 68<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 6.3.1<\/span> Web Usage Mining (WUM) . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 70<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 6.3.2<\/span> Data Stream Mining (Stream Learning) . . . . . . . . . . . . . . . . . .<\/span> 70<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 6.3.3<\/span> Knowledge Discovery from Social Media . . . . . . . . . . . . . . . .<\/span> 71<\/span>
\u00a0 \u00a0 6.4<\/span> Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 72<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 73<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 74<\/span><\/p>\n

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<\/div>\n
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Part IV Analytical Methods<\/span>
7<\/span> Social Media Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 77<\/span>
\u00a0 \u00a0 7.1<\/span> Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 77<\/span>
\u00a0 \u00a0 7.2<\/span> Content Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 78<\/span>
\u00a0 \u00a0 7.3<\/span> Sentiment and Emotion analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 79<\/span>
\u00a0 \u00a0 7.4<\/span> Social Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 82<\/span>
\u00a0 \u00a0 7.5<\/span> Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 83<\/span>
\u00a0 \u00a0 7.6<\/span> Multimedia Content Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 83<\/span>
\u00a0 \u00a0 7.7<\/span> The Benefits of Social Media Analytics Tools . . . . . . . . . . . . . . . . . . .<\/span> 84<\/span>
\u00a0 \u00a0 7.8<\/span> Recommendations for selecting Social Media Analytics Tool . . . . . .<\/span> 85<\/span>
\u00a0 \u00a0 7.9<\/span> Tracking Social Media Performance Using Social Media Analytics<\/span>
Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 86<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 89<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 89<\/span>
8<\/span> Search Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 93<\/span>
\u00a0 \u00a0 8.1<\/span> Review of Search Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 93<\/span>
\u00a0 \u00a0 8.1.1<\/span> What Is a Search Log? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 94<\/span>
\u00a0 \u00a0 8.2<\/span> Process of Search Log Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 95<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 8.2.1<\/span> Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 95<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 8.2.2<\/span> Fields in a Standard Search Log . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 95<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 8.2.3<\/span> Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 96<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 8.2.4<\/span> Cleaning the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 96<\/span>
\u00a0 \u00a0 8.2.5<\/span> Normalizing Searching Episodes . . . . . . . . . . . . . . . . . . . . . . .<\/span> 96<\/span>
\u00a0 \u00a0 8.3<\/span> Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 97<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 8.3.1<\/span> Analysis Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 97<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 8.3.2<\/span> Conducting the Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 99<\/span>
\u00a0 \u00a0 8.4<\/span> Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<\/span> 99<\/span>
\u00a0 \u00a0 8.5<\/span> Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101<\/span>
9<\/span> Persona Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103<\/span>
\u00a0 \u00a0 9.1<\/span> What Is Persona Analytics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103<\/span>
\u00a0 \u00a0 9.2<\/span> Why Is Persona Analytics Needed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105<\/span>
\u00a0 \u00a0 9.3<\/span> Company Use Cases for Persona Analytics . . . . . . . . . . . . . . . . . . . . . 106<\/span>
\u00a0 \u00a0 9.4<\/span> Future Directions for Persona Analytics . . . . . . . . . . . . . . . . . . . . . . . . 108<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110<\/span>
10<\/span> User Study Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113<\/span>
\u00a0 \u00a0 10.1 Defining User Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113<\/span>
\u00a0 \u00a0 10.2 How to Create a User Study? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 10.2.1 Acquiring Resources for a User Study . . . . . . . . . . . . . . . . . . . 114<\/span>
\u00a0 \u00a0 \u00a0 \u00a0 10.2.2 Designing User Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116<\/span><\/p>\n

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\u00a0 \u00a0 \u00a0 \u00a0 10.2.3 Selecting Participants for a User Study . . . . . . . . . . . . . . . . . . 117<\/span>
\u00a0 \u00a0 10.2.4 Who Should Run the User Study? (And How?) . . . . . . . . . . . 117<\/span>
\u00a0 \u00a0 10.3 What Are the Risks of User Studies? . . . . . . . . . . . . . . . . . . . . . . . . . . 119<\/span>
\u00a0 \u00a0 10.4 Analyzing User Study Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120<\/span>
\u00a0 \u00a0 10.5 Implementing User Study Results in Practice . . . . . . . . . . . . . . . . . . . 122<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123<\/span>
11<\/span> Website Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125<\/span>
\u00a0 \u00a0 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125<\/span>
\u00a0 \u00a0 11.2 Visitor Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127<\/span>
\u00a0 \u00a0 11.3 Visit Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127<\/span>
\u00a0 \u00a0 11.4 Demographic and System Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128<\/span>
\u00a0 \u00a0 11.5 Internal Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128<\/span>
\u00a0 \u00a0 11.6 Visitor Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129<\/span>
\u00a0 \u00a0 11.7 Top Pages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129<\/span>
\u00a0 \u00a0 11.8 Referrers and Keyword Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130<\/span>
\u00a0 \u00a0 11.9 Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130<\/span>
\u00a0 \u00a0 11.10 Choosing a Web Analytics Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133<\/span>
Part V Validation Methods<\/span>
12<\/span> A Discussion of the Validity of Data Analytics<\/span> . . . . . . . . . . . . . . . . . . . . . 137<\/span>
\u00a0 \u00a0 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137<\/span>
\u00a0 \u00a0 12.2 Why Not Count Instead of Measure? . . . . . . . . . . . . . . . . . . . . . . . . . . 140<\/span>
Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142<\/span>
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142<\/span><\/div>\n
<\/div>\n
Authors\u2019 Biographies<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145<\/span>
Glossary<\/span> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
<\/div>\n<\/div>\n

Jansen, B. J., Aldous, K, Salminen, J., Almerekhi, H. and Jung, S.G. (2023). Understanding Audiences, Customers, and Users via Analytics \u2013 An Introduction to the Employment of Web, Social, and Other Types of Digital People Data<\/span>. Springer Nature.<\/p>\n","protected":false},"excerpt":{"rendered":"

Here is the Table of Content for our book on Audiences, Customers, and Users Analytics! As you can see, the book addresses the major areas of analytics for audiences, customers, and users! Part I Introduction to Analytics1 The Foundations of Web Analytics . . . . . . . . . . . . .… Continue reading Understanding Audiences, Customers, and Users via Analytics – Table of Contents<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[71],"tags":[],"yoast_head":"\nUnderstanding Audiences, Customers, and Users via Analytics - Table of Contents - Team Acua<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/acua.qcri.org\/blog\/understanding-audiences-customers-and-users-via-analytics-table-of-contents\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Understanding Audiences, Customers, and Users via Analytics - Table of Contents - Team Acua\" \/>\n<meta property=\"og:description\" content=\"Here is the Table of Content for our book on Audiences, Customers, and Users Analytics! 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