Cover image for Actionable web analytics : using data to make smart business decisions
Title:
Actionable web analytics : using data to make smart business decisions
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Publication Information:
Indianapolis, IN : Wiley Publishing, 2007
Physical Description:
xv, 256 p. : ill. ; 24 cm.
ISBN:
9780470124741
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30000003485194 TK5105.875.I57 B86 2007 Open Access Book Book
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Summary

Summary

Knowing everything you can about each click to your Web site can help you make strategic decisions regarding your business. This book is about the why, not just the how, of web analytics and the rules for developing a "culture of analysis" inside your organization. Why you should collect various types of data. Why you need a strategy. Why it must remain flexible. Why your data must generate meaningful action. The authors answer these critical questions--and many more--using their decade of experience in Web analytics.


Author Notes

Jason Burby is Chief Analytics and Optimization Officer for ZAAZ, Inc., a web design and analytics consulting firm. His clients have included eTrade, Ford, Sony, PayPal/eBay, Washington Mutual, Reuters, T-Mobile, Levi Strauss, and Microsoft.

Shane Atchison , co-founder and CEO of ZAAZ, Inc., leads its long-term strategic vision of helping companies realize the potential of the Internet and its impact on their business. Among his client list have been Converse, Sony, Ford, Microsoft, and National Geographic .


Table of Contents

Foreword
Introduction
Part I The Changing Landscape of Marketing Online
Chapter 1 The Big Picture
New Marketing Trends
The Consumer Revolution
The Shift from Offline to Online Marketing
Instant Brand Building (and Destruction)
Rich Media and Infinite Variety
The Analysis Mandate
ROI Marketing
Innovation
Some Final Thoughts
Chapter 2 Performance Marketing
Data vs. Design
Web Design Today
The Web Award Fallacy
When Visual Design Goes Wrong
Where Data Goes Wrong
Performance-Driven Design: Balancing Logic and Creativity
Case Study: Dealing with Star Power
Case Study: Forget Marketing at All
Recap
Part II Shifting to a Culture of Analysis
Chapter 3 What "Culture of Analysis" Means
What Is a Data-Driven Organization?
Data-Driven Decision Making
Dynamic Prioritization
Perking Up Interest in Web Analytics
Establishing a Web Analytics Steering Committee
Starting Out Small with a Win
Empowering Your Employees
Managing Up
Impact on Roles beyond the Analytics Team
Cross-Channel Implications
Questionnaire: Rating Your Level of Data Drive
Recap
Chapter 4 Avoiding Stumbling Points
Do You Need an Analytics Intervention?
Analytics Intervention Step 1: Admitting the Problem
Analytics Intervention Step 2: Admit That You Are the Problem
Analytics Intervention Step 3: Agree That This Is a Corporate Problem
The Road to Recovery: Overcoming Real Gaps
Issue #1 Lack of Established Processes and Methodology
Issue #2 Failure to Establish Proper KPIs and Metrics
Issue #3 Data Inaccuracy
Issue #4 Data Overload
Issue #5 Inability to Monetize the Impact of Changes
Issue #6 Inability to Prioritize Opportunities
Issue #7 Limited Access to Data
Issue #8 Inadequate Data Integration
Issue #9 Starting Too Big
Issue #10 Failure to Tie Goals to KPIs
Issue #11 No Plan for Acting on Insight
Issue #12 Lack of Committed Individual and Executive Support
Recap
Part III Proven Formula for Success
Chapter 5 Preparing to Be Data-Driven
Web Analytics Methodology
The Four Steps of Web Analytics
Defining Business Metrics (KPIs)
Reports
Analysis
Optimization and Action
Results and Starting Again
Recap
Chapter 6 Defining Site Goals, KPIs, and Key Metrics
Defining Overall Business Goals
Defining Site Goals: The Conversion Funnel
Awareness
Interest
Consideration
Purchase
Website Goals and the Marketing Funnel
Understanding Key Performance Indicators (KPIs)
Constructing KPIs
Creating Targets for KPIs
Common KPIs for Different Site Types
E-Commerce
Lead Generation
Customer Service
Content Sites
Branding Sites
Recap
Chapter 7 Monetizing Site Behaviors
The Monetization Challenge
Case Study: Monetization and Motivation
Web-Monetization Models
Top 10 Ways Monetization Models Can Help Your Company
How to Create Monetization Models
Assembling a Monetization Model
Monetization Models for Different Site Types and Behaviors
E-Commerce Opportunity
Lead Generation
Customer Service
Ad-Supported Content Sites
Recap
Chapter 8 Getting the Right Data
Primary Data Types
Warning: Avoid Data Smog
Behavioral Data
Attitudinal Data
Balancing Behavioral and Attitudinal Data
Competitive Data
Secondary Data Types
Customer Interaction and Data
Third-Party Research
Usability Benchmarking