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Actionable Web Analytics: Using Data to Make Smart Business Decisions

Overview

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 ...

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Overview

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.

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Editorial Reviews

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The Barnes & Noble Review
As every web marketer knows, it's now possible to capture a truly breathtaking amount of data about your site and your online customers. But transforming that data into knowledge is still brutally tough. Using it to make smarter decisions? That's even tougher. That's the challenge this book takes on, and if you need to maximize the ROI of your online presence, it's simply indispensable.

There's still an immense amount of FUD in the web analytics space. Leading consultants Jason Burby and Shane Atchison sweep all that away, drawing on the deep knowledge they've gained working with clients like WaMu, Wachovia, T-Mobile, Converse, Microsoft, and A&E. They offer both a high-level framework for linking web analytics to business decision-making, as well as enormously useful detail -- on issues ranging from multivariate testing to tracking brick-and-mortar referrals.

Possibly your favorite part will be their extensive discussion of defining Key Performance Indicators (KPIs) and embedding them into your web analytics. Perhaps you'll especially appreciate their guidance on recognizing what questions your data needs to answer, and avoiding "data overload." Maybe you're wondering why your already huge investments in web analytics aren't paying off. If so, the authors' list of 12 reasons may intrigue you: Their solutions certainly will.

Could be you'll turn first to their guidance on testing price, promotions, message, page layouts, new site features, navigation, and taxonomy. Or their ideas on shifting your organization's culture from intuition to analysis. Or their chapters on choosing agencies and web analytics vendors. Or something we haven't mentioned. The more you read, the more value you'll find here. Bill Camarda, from the July 2007 Read Only

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Product Details

  • ISBN-13: 9780470124741
  • Publisher: Wiley
  • Publication date: 5/29/2007
  • Edition number: 1
  • Pages: 288
  • Sales rank: 841,634
  • Product dimensions: 9.25 (w) x 7.50 (h) x 0.61 (d)

Meet the Author

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.

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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.

Heuristic Evaluation and Expert Reviews.

Community Sourced Data.

Leveraging These Data Types.

Comparing Performance with Others.

What Is a Relative Index?

Examples of Relative Indices.

Customer Engagement.

Methodology: Leveraging Indices across Your Organization.

Case Study: Leveraging Different Data Types to Improve Site Performance.

Recap.

Chapter 9 Analyzing Site Performance.

Analysis vs. Reporting.

Don’t Blame Your Tools.

Examples of Analysis.

Analyzing Purchasing Processes to Find Opportunities.

Analyzing Lead Processes to Find Opportunities.

Understanding What Onsite Search Is Telling You.6

Evaluating the Effectiveness of Your Home Page.

Evaluating the Effectiveness of Branding Content: Branding Metrics.

Evaluating the Effectiveness of Campaign Landing Pages.

Segmenting Traffic to Identify Behavioral Differences.

Segmenting Your Audience.

Case Study: Segmenting for a Financial Services Provider.

Analyzing Drivers to Offline Conversion.

Tracking Online Partner Handoffs and Brick-And-Mortar Referrals.

Tracking Offline Handoffs to Sales Reps.

Tracking Visitors to a Call Center.

Delayed Conversion.

Tracking Delayed Conversion.

Reporting in a Timely Manner.

Recap.

Chapter 10 Prioritizing.

How We Prioritize.

The Principles of Dynamic Prioritization.

Traditional Resource Prioritization.

Dynamic Prioritization.

Dynamic Prioritization Scorecard.

Dynamic Prioritization in Action.

Forecasting Potential Impact.

Comparing Opportunities.

Moving Your Company Toward Dynamic Prioritization.

Overcoming Common Excuses.

Conclusion.

Recap.

Chapter 11 Moving from Analysis to Site Optimization.

Testing Methodologies and Tools.

A/B Testing.

A/B/n Testing.

Multivariate Tests.

How to Choose a Test Type.

Testing Tools.

What to Test.

Prioritizing Tests.

Creating a Successful Test.

Understanding Post-Test Analysis.

Optimizing Segment Performance.

Example One: Behavior-Based Testing.

Example Two: Day-of-the-Week Testing.

Planning for Optimization.

Budgeting for Optimization.

Skills Needed for a Successful Optimization Team.

Overcoming IT Doubts.

IT Doesn’t Understand the Process.

Testing Prioritization.

Lack of Executive Support.

Learning from Your Successes and Mistakes.

Learning from the Good and the Bad.

A Quick Way Up the Learning Curve.

Spreading the Word.

Test Examples.

Price.

Promotional.

Message.

Page Layout.

New Site Launches or New Functionality.

Site Navigation and Taxonomy.

Recap.

Chapter 12 Agencies.

Why Use an Agency at All?

Finding an Agency.

Creating an RFP.

Introduction and Company Background.

Scope of Work and Business Goals.

Timelines.

Financials.

The Rest of the RFP: Asking the Right Questions.

Mutual Objective: Success.

Doing the Work.

The Secret Agency Sauce.

Recap.

Chapter 13 The Creative Brief.

What Is a Creative Brief?

The Brief.

Components of a Data-Driven Brief.

Creative Brief Metrics.

Analytics and Creativity.

The Iterative Design Cycle.

A Sample Creative Brief.

Creative Brief: Robotwear.Com.

Recap.

Chapter 14 Staffing and Tuning Your Web Team.

Skills That Make a Great Web Analyst.

Technical vs. Interpretive Expertise.

Key Web Analyst Skills.

The Roles of the Web Analyst.

Building Your Web-Analytics Team: Internal and External Teams.

Estimating Your Cost.

Key Analytics Positions.

Expanding the Circle of Influence.

Internal vs. External Teams.

Education and Training for Web Analysts.

Web Analytics Association.

Conferences.

University of British Columbia Courses.

Message Boards.

ClickZ and Other Online Media.

Blogs.

Web Analytics Wednesdays.

Vendor Training.

Agency Partners.

Hands-on Experience.

Recap.

Chapter 15 Partners.

When to Choose an Analytics Tool Vendor.

Methodology for Selecting a Tool.

Selecting a Review Committee.

Establishing a Timeline.

Criteria to Review and Select Vendors.

10 Questions to Ask Web Analytics Vendors.

Comparing to Free Tools.

ASP or Software Version.

Data Capture.

Total Cost of Ownership.

Support.

Data Segmentation.

Data Export and Options.

Data Integration.

The Future.

References.

Recap.

Conclusion.

Appendix:Web Analytics "Big Three" Definitions.

How We Define Terms.

Definition Framework Overview.

Term: Unique Visitors.

Term: Visits/Sessions.

Term: Page Views.

Index.

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