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Chapter 3: KPI and Planning
Setting goals and detailed planning are essential to project success. If you do not know what you need to do to achieve you goal you will fail.
If your goal is to sell $100,000 worth of products this year, but you have only sold $80,000 so far, how can you achieve your goal? How many more customers will you need to attract? How much money needs to be budgeted for advertising? Should any promotions or sales be run? Is the website contributing to the goal?
As a Web Analytics Consultant you will need to be able to answer these questions for any number of different businesses.
A KPI gives a business quantifiable measurements of things it has determined important to its efficiency, profitability and long-term success and is needed to measure the information obtained from business, communication and web analytics.
KPI and Planning: Web Analytics Consultants
An Associate Web Analytics Consultant knows how to figure out KPI and planning by calculating a client’s advertising budget, the target value of average spending per customer and the KPI of the company website.
A Senior Web Analytics Consultant knows how to plan for a sales increase and how to calculate the profit from a website. They also know how to present the KPI according to their client’s point of view rather than their own.
Let’s review some basics by imagining that a website is a department store.
- 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 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
- 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
- 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
- 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
- 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