Chapter 1: What is Web Analytics?

• 1.1 Does the web have a bright future?
• 1.1.1 Business Management and the Internet
• 1.1.2 Web Specialists
• 1.1.3 The Web Industry
• 1.2 Online marketing and web analytics
• 1.2.1 The Professional Image Required for the Future of the Web Industry
• 1.2.2 The Significance of Web Marketing
• 1.2.3 The Significance of Web Analytics
• 1.3 Web Analytics
• 1.3.1 Bringing Business Results
• 1.3.2 Listen to Your Users’ Voices
• 1.3.3 The PDCA Cycle
• 1.4 The Future of Web Analytics
• 1.4.1 Data to Understand
• 1.4.2 Future Concerns
• 1.5 Web Analytics Consultants
• 1.5.1 The Philosophy of Web Analytics Consultants
• 1.5.2 Facts Regarding Web Analytics
• 1.5.3 Web Analyst’s Issues in Ethics: Confidentiality
• 1.5.4 Web Analytics Consultant’s Issues in Ethics: Code of Conduct
• 1.5.5 Certification Program for Web Analytics Consultants
• 1.5.6 The Web Analytics Consultants Association
• Case Study: Web analytics is an essential element of B2B marketing
• Case Study: Become a web analysis consultant who can commit 100% to the results

Chapter 2: Business Analysis

• 2.1 Web Analytics Starts with Business Analysis
• 2.1.1 Business Models
• 2.1.2 The 3C Business Model
• 2.1.3 The First C – Customer Analysis
• 2.1.4 The Second C – Competitor Analysis
• 2.1.5 The Third C – Corporation Analysis
• 2.2 Business Models can be used to Understand Customers
• 2.2.1 Developing Communication Design Schematics
• 2.2.2 Using Communication Design to Bring about Positive Results
• 2.3 Using Web Analytics Tools in Industry Analyses
• 2.3.1 Macro & Micro Analysis Tools
• 2.3.2 How to Introduce Micro Analysis
• 2.3.3 Internet Audience Measurement Methodologies
• 2.3.4 Competitor Analysis
• 2.3.5 How to take advantage of web marketing analysis tools
• Case Study: Optimizing Your Website
• Column: Certifying as a Web Analytics Consultant

Chapter 3: KPI and Planning

• 3.1 Understanding Web Analytics Terminology and KPIs
• 3.1.1 Basic Metrics in Web Analytics (e.g. Pageviews)
• 3.1.2 What are KPIs?
• 3.1.3 The definitions of KGI, KSF and KPI
• 3.1.4 KPIs that measure the effectiveness of a website
• 3.2 Develop a plan
• 3.2.1 Planning based on the purpose of the project
• 3.3 E-Commerce Marketing Strategies
• 3.3.1 Planning an E-Commerce Campaign That Will Help You Reach Your KPI
• 3.4 Lead Generation
• 3.4.1 Lead Generation KPIs: What to Measure
• 3.4.2 Divide lead generation into phases
• 3.4.3 Advertising vs Improving Your Webpage
• 3.4.4 How Should We Prioritize Ad Investments?
• Column: Growth Hacking

Chapter 4: Designing a Blueprint for Web Analytics

• 4.1 Selecting Your Web Log Analysis Method
• 4.1.1 The Log file Analysis Method
• 4.1.2 The Packet Sniffing Method
• 4.1.3 The Page Tagging Method
• 4.1.4 How to Choose the Correct Web Log Analysis Method Based on Available Settings and Systems
• 4.1.5 How to Choose Access Analysis Tools: Company Policies
• 4.2 Setup
• 4.2.1 Setting Exclusions
• 4.2.2 Setting Conversion Pages
• 4.2.3 Other Pre-Settings
• 4.3 Solutions for Difficult Access Analysis
• 4.3.1 RIA/AJAX
• 4.3.2 URL Redirection Pages
• 4.3.3 Setting Page Links
• 4.3.4 Download Count of a PDF File (Handling of Data Other than Web Page)
• 4.3.5 Analysis of a SSL Page
• 4.4 Combining Data in Web Analytics
• 4.4.1 Basic Concept of Combining Data in Web Analytics
• 4.4.2 Analyzing Combined Data From Multiple Devices
• 4.4.3 Smartphone Applications and Analytics
• 4.4.4 Data Integration
• 4.4.5 Analysis by Combining Equipment Data
• 4.5 Web Analytics Software
• Case Study: Re-designing the Staff Blog to Successfully Attract New Employees

Chapter 5: Analyzing Trends

• 5.1 How Logfiles Are Structured
• 5.1.1 Aggregated Logfile Data
• 5.1.2 Hits and the number of hits to a webpage
• 5.2 Understanding Raw Data
• 5.2.1 Log formats
• 5.2.2 Defining Metrics
• 5.2.3 Status Code
• 5.2.4 User agents
• 5.2.5 Session Duration
• 5.3 Analyzing Metrics
• 5.3.1 Pageviews
• 5.3.2 Sessions
• 5.3.3 Analyzing Unique Users
• 5.3.4 Using Conversions for web analytics
• 5.3.5 Session Duration
• 5.4 Notes on Web Analytics
• 5.4.1 Analyzing Multiple Devices
• 5.4.2 Notes on Deviations
• 5.4.3 Screen resolution, browsers, and operating systems
• 5.5 Characteristics and Deviation in Web Analytics Data
• 5.5.1 Power Distribution and Average
• 5.5.2 Reasons of Deviation
• Case Study: Focusing on the number of pageviews and improving the site’s structure

Chapter 6: Analyzing Referrers

• 6.1 Reference source structures
• 6.1.1 Types of Referrers
• 6.1.2 The User Processes Flow
• 6.1.3 Query strings
• 6.1.4 Improving Traffic for Each Referral Type
• 6.1.5 Referrer sources
• 6.2 Situations when there is no referrer
• 6.2.1 Newsletter
• 6.2.2 Using query strings to identify referrers in no referrer logs
• 6.3 Search Queries and Results
• 6.3.1 What is SEO?
• 6.3.2 SEO and organic searches
• 6.3.3 Google Search Console
• 6.3.4 Keyword tools
• 6.3.5 Take advantage of tools that track trends
• 6.3.6 Off-page and Onpage Search Engine Optimization
• 6.4 Analysis of Keyword and PLA Advertisements
• 6.4.1 Traffic via PPC Ads
• 6.4.2 Pay Per Click Ads
• 6.4.3 Ad Partners
• 6.4.4 The Quality of Pay Per Click Ads The Quality of Product Listing Ads
• 6.4.5 Managing Pay-per-click Ads
• 6.5 Social Media Analytics
• 6.5.1 SNS, Social Media Websites
• 6.5.2 Data from Social Media
• 6.5.3 Using Social Media
• 6.5.4 Social media advertising
• 6.6 Measuring Your Ad’s Effects
• 6.6.1 Terms Used in Ad Effects Measurement
• 6.6.2 Designing Internet Ads
• 6.6.3 Types of Ads
• 6.6.4 Post Click Attribution
• 6.6.5 Ad Technology

Chapter 7: Analyzing Content

• 7.1 Content structures and research methods
• 7.1.1 Managing the quality of a website
• 7.1.2 Self-report studies
• 7.1.3 User experience research
• 7.2 Bounce rate analytics
• 7.2.1 Finding Issues in Bounce Rates
• 7.2.2 LPO (Landing Page Optimization)
• 7.2.3 Improvements with LPO tools
• 7.3 Analyze the exit rate
• 7.3.1 Finding Issues with the Exit Rate
• 7.3.2 Analyzing Internal Search Functions
• 7.3.3 Utilize heatmapping tools
• 7.4 Analyzing Form Abandonment Rates
• 7.4.1 Finding Issues Regarding Form Abandonment Rates
• 7.4.2 Improving Forms
• 7.4.3 Improving with EFO
• Case Study: Don’t Rely Solely on Experience and Intuition.

Chapter 8: Web Analytics, Proposals and Reports

• 8.1 Writing reports
• 8.1.1 The Relationship between PVs, Sessions, and Unique Users
• 8.1.2 Rules you must follow when writing a report
• 8.1.3 Defining Report Requirements
• 8.1.4 Tips for improving the flow of written reports
• 8.1.5 Types of web analytics reports
• 8.1.6 Notes on Writing Reports
• 8.2 How to use tables, charts, and graphs
• 8.2.1 Graphs
• 8.2.2 Types of Charts and Their Usage
• 8.2.3 Notes on creating charts
• 8.3 Logic
• 8.3.1 Deductive and inductive reasoning
• 8.3.2 Cause-Effect Relationships
• 8.3.3 The MECE Principle
• Case Study: How to write a report that encourages companies to take action

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