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Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity

Name: Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity
Author: Avinash Kaushik
ISBN: 9780470529393

by Avinash Kaushik

October 2009

Beginner to intermediate

503 pages

13h 16m

English

Sybex

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Copyright
Dear Reader,
Acknowledgments
About the Author
Introduction
The Awesome World of Data-Driven Decision MakingWhat's Inside the Book?Valuable Multimedia Content on the CDRequest for FeedbackThe Beginning
1. The Bold New World of Web Analytics 2.0
1.1. State of the Analytics Union1.2. State of the Industry1.3. Rethinking Web Analytics: Meet Web Analytics 2.01.3.1. The What: Clickstream1.3.2. The How Much: Multiple Outcomes Analysis1.3.3. The Why: Experimentation and Testing1.3.4. The Why: Voice of Customer1.3.5. The What Else: Competitive Intelligence1.4. Change: Yes We Can!1.4.1. The Strategic Imperative1.4.2. The Tactical Shift1.4.3. Bonus Analytics
2. The Optimal Strategy for Choosing Your Web Analytics Soul Mate
2.1. Predetermining Your Future Success2.2. Step 1: Three Critical Questions to Ask Yourself Before You Seek an Analytics Soul Mate!2.2.1. Q1: "Do I want reporting or analysis?"2.2.1.1. The Wrong Affordable Tool2.2.1.2. The Expensive Tool with the Wrong Staff2.2.1.3. The Switch to the Right Tool2.2.2. Q2: "Do I have IT strength, business strength, or both?"2.2.3. Q3: "Am I solving just for Clickstream or for Web Analytics 2.0?"2.3. Step 2: Ten Questions to Ask Vendors Before You Marry Them2.3.1. Q1: "What is the difference between your tool/solution and free tools from Yahoo! and Google?"2.3.2. Q2: "Are you 100 percent ASP, or do you offer a software version? Are you planning a software version?"2.3.3. Q3: "What data capture mechanisms do you use?"2.3.4. Q4: "Can you calculate the total cost of ownership for your tool?"2.3.5. Q5: "What kind of support do you offer? What do you include for free, and what costs more? Is it free 24/7?"2.3.6. Q6: "What features in your tool allow me to segment the data?"2.3.7. Q7: "What options do I have for exporting data from your system into our company's system?"2.3.8. Q8: "What features do you provide for me to integrate data from other sources into your tool?"2.3.9. Q9: "Can you name two new features/tools/acquisitions your company is cooking up to stay ahead of your competition for the next three years?"2.3.10. Q10: "Why did the last two clients you lost cancel their contracts? Who are they using now? May we call one of these former clients?"2.4. Comparing Web Analytics Vendors: Diversify and Conquer2.4.1. The Three-Bucket Strategy2.5. Step 3: Identifying Your Web Analytics Soul Mate (How to Run an Effective Tool Pilot)2.6. Step 4: Negotiating the Prenuptials: Check SLAs for Your Web Analytics Vendor Contract
3. The Awesome World of Clickstream Analysis: Metrics
3.1. Standard Metrics Revisited: Eight Critical Web Metrics3.1.1. Visits and Visitors3.1.1.1. Visits3.1.1.2. Unique Visitors3.1.1.2.1. Month 1 and Week 1 for End of Day 13.1.1.2.2. Month 1 and Week 1 for End of Day 23.1.1.2.3. Month 1 for End of Week 13.1.1.2.4. Month 1 for End of Week 23.1.1.2.5. End of Month 1, for the Whole Month3.1.1.2.6. End of Month 2, for the Two Months3.1.2. Time on Page and Time on Site3.1.2.1. Lesson 1: The Single-Page View Session3.1.2.2. Lesson 2: The Case of Tabbed Browsing3.2. Bounce Rate3.3. Exit Rate3.4. Conversion Rate3.5. Engagement3.6. Web Metrics Demystified3.6.1. Four Attributes of Great Metrics3.6.1.1. Uncomplex3.6.1.2. Relevant3.6.1.3. Timely3.6.1.4. Instantly Useful3.6.2. Example of a Great Web Metric3.6.3. Three Avinash Life Lessons for Massive Success3.6.3.1. Perfection Is...the Enemy of Good Enough3.6.3.2. Critical Few, Baby, Critical Few!3.6.3.3. The Web Metrics Lifecycle Process Is Your Friend!3.7. Strategically Aligned Tactics for Impactful Web Metrics3.7.1. Diagnosing the Root Cause of a Metric's Performance—Conversion3.7.2. Leveraging Custom Reporting3.7.2.1. Creating Micro-ecosystem Reports3.7.3. Starting with Macro Insights3.7.3.1. Q1: "How many Visitors are coming to my website?"3.7.3.2. Q2: "Where are Visitors coming from?"3.7.3.3. Q3: "What do I want Visitors to do on the website?"3.7.3.4. Q4: "What are Visitors actually doing?"
4. The Awesome World of Clickstream Analysis: Practical Solutions
4.1. A Web Analytics Primer4.1.1. Getting Primitive Indicators Out of the Way4.1.2. Understanding Visitor Acquisition Strengths4.1.3. Fixing Stuff and Saving Money4.1.4. Click Density Analysis4.1.5. Measuring Visits to Purchase4.2. The Best Web Analytics Report4.2.1. Sources of Traffic4.2.2. Outcomes4.3. Foundational Analytical Strategies4.3.1. Segment or Go Home4.3.1.1. Benefits of Segmenting4.3.1.2. Creating and Applying Segments4.3.2. Focus on Customer Behavior, Not Aggregates4.4. Everyday Clickstream Analyses Made Actionable4.4.1. Internal Site Search Analysis4.4.1.1. Site Search Usage4.4.1.2. Measuring Site Search Quality4.4.1.3. Segmenting and Measuring Impact4.4.2. Search Engine Optimization (SEO) Analysis4.4.2.1. Performance: Traffic from Organic Search4.4.2.2. Content Coverage: Indexing by Search Engines4.4.2.3. Keyword Performance: Search Engine Results4.4.2.4. Outcomes: Goals, Revenue, and ROI4.4.3. Pay Per Click/Paid Search Analysis4.4.3.1. Performance: Traffic from Paid Search4.4.3.2. Measuring the End-to-End View4.4.3.3. Analyzing PPC Ad Position4.4.3.4. Measuring PPC Customer Behavior4.4.4. Direct Traffic Analysis4.4.4.1. Reviewing Current Performance4.4.4.2. Understanding the Opportunity and Educating Management4.4.4.3. Segmenting to Understand Uniqueness4.4.5. Email Campaign Analysis4.4.5.1. Campaign Response4.4.5.2. Website Behavior4.4.5.3. Business Outcomes4.4.6. Rich Experience Analysis: Flash, Video, and Widgets4.4.6.1. Event Tracking4.5. Reality Check: Perspectives on Key Web Analytics Challenges4.5.1. Visitor Tracking Cookies4.5.1.1. Transient vs. Persistent4.5.1.2. First Party vs. Third Party4.5.1.3. Cookie Choice and Data Storage4.5.1.4. Cookie Deletion Rates4.5.1.5. Without Cookies, Life Continues4.5.2. Data Sampling 4114.5.2.1. Code Red: Sampling Web Pages on Your Site4.5.2.2. Code Orange: Sampling Data Collected from Each Page4.5.2.3. Code Green: Sampling Data Processed When You Run the Query/Report4.5.3. The Value of Historical Data4.5.3.1. Data Decay4.5.3.2. The Goodness of Not Worrying About History4.5.3.3. Saving What Matters4.5.4. The Usefulness of Video Playback of Customer Experience4.5.4.1. Scale4.5.4.2. Interpretation4.5.4.3. "You Can Track Sara, Akio, and Roxana"4.5.4.4. Cost vs. Benefit4.5.4.5. Hope for the Future4.5.5. The Ultimate Data Reconciliation Checklist4.5.5.1. #1: Don't Compare Data Collected from Web Logs vs. JavaScript Tags4.5.5.2. #2: The Gift That Keeps Giving: First- and Third-Party Cookies4.5.5.3. #3: Imprecise Website Tagging4.5.5.4. #4: Torture Your Vendor: Check Definitions of Key Metrics4.5.5.5. #5: A Tough Nut: Sessionization4.5.5.6. #6: The Permanent Tripwire: URL Parameter Configuration4.5.5.7. #7: The Problem of the Big: Campaign Parameter Configuration4.5.5.8. #8: The Hidden Angel: Data Sampling4.5.5.9. #9: Order of Tags
5. The Key to Glory: Measuring Success
5.1. Focus on the "Critical Few"5.2. Five Examples of Actionable Outcome KPIs5.2.1. Task Completion Rate5.2.2. Share of Search5.2.3. Visitor Loyalty and Recency5.2.4. RSS/Feed Subscribers5.2.5. % of Valuable Exits5.3. Moving Beyond Conversion Rates5.3.1. Cart and Checkout Abandonment5.3.2. Days and Visits to Purchase5.3.3. Average Order Value5.3.4. Primary Purpose (Identify the Convertible)5.4. Measuring Macro and Micro Conversions5.4.1. Examples of Macro and Micro Conversions5.4.1.1. Photo-Publishing and Sharing Website5.4.1.2. Technical Support Website5.4.1.3. Multipurpose Ecommerce Website5.5. Quantifying Economic Value5.6. Measuring Success for a Non-ecommerce Website5.6.1. Visitor Loyalty5.6.2. Visitor Recency5.6.3. Length of Visit5.6.4. Depth of Visit5.7. Measuring B2B Websites

6. Solving the "Why" Puzzle—Leveraging Qualitative Data
6.1. Lab Usability Studies: What, Why, and How Much?6.1.1. What Is Lab Usability?6.1.2. How to Conduct a Test6.1.2.1. Preparing6.1.2.2. Conducting the Test6.1.2.3. Analyzing the Data6.1.2.4. Following Up, Retesting, and Measuring Success6.1.3. Best Practices for Lab Usability Studies6.1.4. Benefits of Lab Usability Studies6.1.5. Areas of Caution6.2. Usability Alternatives: Remote and Online Outsourced6.2.1. Live Recruiting and Remote User Research6.2.1.1. Live Recruiting and Remote Studies6.2.1.2. Outsourced Online Usability6.3. Surveys: Truly Scalable Listening6.3.1. Types of Surveys6.3.1.1. Page-Level Surveys6.3.1.2. Site-Level Surveys6.3.2. The Single Biggest Surveying Mistake6.3.3. Three Greatest Survey Questions Ever6.3.3.1. Q1: What is the purpose of your visit to our website today?6.3.3.2. Q2: Were you able to complete your task?6.3.3.3. Q3: If you were not able to complete your task today, why not?6.3.3.4. Launching Your Customer-Centric Strategy6.3.4. Eight Tips for Choosing an Online Survey Provider6.3.4.1. Tip 1: Mathematical Rigor6.3.4.2. Tip 2: On-the-Fly Segmentation Capabilities6.3.4.3. Tip 3: Benchmarks and Indices6.3.4.4. Tip 4: Open Text Categorization6.3.4.5. Tip 5: Type of Survey Invitation6.3.4.6. Tip 6: Cookie Sophistication6.3.4.7. Tip 7: Integration with Clickstream Data6.3.4.8. Tip 8: Pilot Friendly6.4. Web-Enabled Emerging User Research Options6.4.1. Competitive Benchmarking Studies6.4.2. Rapid Usability Tests6.4.3. Online Card-Sorting Studies6.4.4. Artificially Intelligent Visual Heat Maps
7. Failing Faster: Unleashing the Power of Testing and Experimentation
7.1. A Primer on Testing Options: A/B and MVT7.1.1. A/B Testing7.1.1.1. Pros of A/B Testing7.1.1.2. Cons of A/B Testing7.1.2. Multivariate Testing7.1.2.1. Common MVT Techniques7.1.2.1.1. Pros of Multivariate Testing7.1.2.1.2. Cons of Multivariate Testing7.2. Actionable Testing Ideas7.2.1. Fix the Big Losers—Landing Pages7.2.2. Focus on Checkout, Registration, and Lead Submission Pages7.2.3. Optimize the Number and Layout of Ads7.2.4. Test Different Prices and Selling Tactics7.2.5. Test Box Layouts, DVD Covers, and Offline Stuff7.2.6. Optimize Your Outbound Marketing Efforts7.3. Controlled Experiments: Step Up Your Analytics Game!7.3.1. Measuring Paid Search Impact on Brand Keywords and Cannibalization7.3.2. Examples of Controlled Experiments7.3.3. Challenges and Benefits7.4. Creating and Nurturing a Testing Culture7.4.1. Tip 1: Your First Test is "Do or Die"7.4.2. Tip 2: Don't Get Caught in the Tool/Consultant Hype7.4.3. Tip 3: "Open the Kimono"—Get Over Yourself7.4.4. Tip 4: Start with a Hypothesis7.4.5. Tip 5: Make Goals Evaluation Criteria and Up-Front Decisions7.4.6. Tip 6: Test For and Measure Multiple Outcomes7.4.7. Tip 7: Source Your Tests in Customer Pain7.4.8. Tip 8: Analyze Data and Communicate Learnings7.4.9. Tip 9: Two Must-Haves: Evangelism and Expertise
8. Competitive Intelligence Analysis
8.1. CI Data Sources, Types, and Secrets8.1.1. Toolbar Data8.1.2. Panel Data8.1.3. ISP (Network) Data8.1.4. Search Engine Data8.1.5. Benchmarks from Web Analytics Vendors8.1.6. Self-reported Data8.1.7. Hybrid Data8.2. Website Traffic Analysis8.2.1. Comparing Long-Term Traffic Trends8.2.2. Analyzing Competitive Sites Overlap and Opportunities8.2.3. Analyzing Referrals and Destinations8.3. Search and Keyword Analysis8.3.1. Top Keywords Performance Trend8.3.2. Geographic Interest and Opportunity Analysis8.3.3. Related and Fast-Rising Searches8.3.4. Share-of-Shelf Analysis8.3.5. Competitive Keyword Advantage Analysis8.3.6. Keyword Expansion Analysis8.4. Audience Identification and Segmentation Analysis8.4.1. Demographic Segmentation Analysis8.4.2. Psychographic Segmentation Analysis8.4.3. Search Behavior and Audience Segmentation Analysis
9. Emerging Analytics: Social, Mobile, and Video
9.1. Measuring the New Social Web: The Data Challenge9.1.1. The Content Democracy Evolution9.1.2. The Twitter Revolution9.2. Analyzing Offline Customer Experiences (Applications)9.3. Analyzing Mobile Customer Experiences9.3.1. Mobile Data Collection: Options9.3.1.1. Logs-Based Solutions9.3.1.2. Packet-Sniffing-Based Solutions9.3.1.3. Tag-Based Solutions—JavaScript or Image9.3.2. Mobile Reporting and Analysis9.4. Measuring the Success of Blogs9.4.1. Raw Author Contribution9.4.2. Holistic Audience Growth9.4.2.1. Conversation Rate9.4.3. Citations and Ripple Index9.4.4. Cost of Blogging9.4.5. Benefit (ROI) from Blogging9.4.5.1. Comparative Value9.4.5.2. Direct Value9.4.5.3. Nontraditional Value9.4.5.4. Unquantifiable Value9.5. Quantifying the Impact of Twitter9.5.1. Growth in Number of Followers9.5.2. Message Amplification9.5.3. Click-Through Rates and Conversions9.5.3.1. Average Shared Links CTR9.5.3.2. Conversion Rate (Outcomes)9.5.4. Conversation Rate9.5.5. Emerging Twitter Metrics9.6. Analyzing Performance of Videos9.6.1. Data Collection for Videos9.6.2. Key Video Metrics and Analysis9.6.2.1. Baseline Performance Metrics9.6.2.1.1. Tracking Attention or Audience Engagement9.6.2.2. Reporting on Social Engagement9.6.2.3. Tracking Viralness9.6.2.4. Segment, Segment, Segment9.6.3. Advanced Video Analysis9.6.3.1. Computing Contextual Influence9.6.3.2. Actively Collecting Voice of Customer (VOC)9.6.3.3. Testing to Measure Actual Customer Behavior
10. Optimal Solutions for Hidden Web Analytics Traps
10.1. Accuracy or Precision?10.2. A Six-Step Process for Dealing with Data Quality10.3. Building the Action Dashboard10.3.1. Creating Awesome Dashboards10.3.2. The Consolidated Dashboard10.3.3. Five Rules for High-Impact Dashboards10.3.3.1. Benchmark and Segment10.3.3.2. Isolate Your Critical Few Metrics10.3.3.3. Don't Stop at Metrics—Include Insights10.3.3.4. The Power of a Single Page10.3.3.5. Churn and Stay Relevant10.4. Nonline Marketing Opportunity and Multichannel Measurement10.4.1. Shifting to the Nonline Marketing Model10.4.2. Multichannel Analytics10.5. The Promise and Challenge of Behavior Targeting10.5.1. The Promise of Behavior Targeting10.5.2. Overcoming Fundamental Analytics Challenges10.5.3. Two Prerequisites for Behavior Targeting10.5.3.1. Invest in Solid Customer Listening Posts10.5.3.2. Do A/B or Multivariate Testing First10.6. Online Data Mining and Predictive Analytics: Challenges10.6.1. Type of Data10.6.2. Number of Variables10.6.3. Multiple Primary Purposes10.6.4. Multiple Visit Behaviors10.6.5. Missing Primary Keys and Data Sets10.6.5.1. Pace of Change on the Web10.7. Path to Nirvana: Steps Toward Intelligent Analytics Evolution10.7.1. Step 1: Tag, Baby, Tag!10.7.2. Step 2: Configuring Web Analytics Tool Settings10.7.2.1. Configuring Reporting10.7.2.2. Configuring Goals10.7.3. Step 3: Campaign/Acquisition Tracking10.7.4. Step 4: Revenue and Uber-intelligence10.7.5. Step 5: Rich-Media Tracking (Flash, Widgets, Video)
11. Guiding Principles for Becoming an Analysis Ninja
11.1. Context Is Queen11.1.1. Comparing Key Metrics Performance for Different Time Periods11.1.2. Providing Context Through Segmenting11.1.3. Comparing Key Metrics and Segments Against Site Average11.1.4. Joining PALM (People Against Lonely Metrics)11.1.5. Leveraging Industry Benchmarks and Competitive Data11.1.6. Tapping into Tribal Knowledge11.2. Comparing KPI Trends Over Time11.2.1. Presenting Tribal Knowledge11.2.2. Segmenting to the Rescue!11.3. Beyond the Top 10: What's Changed11.4. True Value: Measuring Latent Conversions and Visitor Behavior11.4.1. Latent Visitor Behavior11.4.2. Latent Conversions11.5. Four Inactionable KPI Measurement Techniques11.5.1. Averages11.5.1.1. Segmenting Average Data11.5.1.2. Using Distributions11.5.2. Percentages11.5.2.1. Show Relevant Raw Numbers11.5.2.2. Segment the Data11.5.2.3. User Statistical Significance11.5.3. Ratios11.5.3.1. Use Custom Reports to Put the Ratio in Context11.5.3.2. Resist Setting "Golden" Rules11.5.4. Compound or Calculated Metrics11.5.4.1. Don't Use Compound/Calculated Metrics11.5.4.2. Degrade to Critical Few Components11.5.4.3. Revisit and Revalidate11.6. Search: Achieving the Optimal Long-Tail Strategy11.6.1. Compute Your Head and Tail11.6.1.1. Insights You Can Find11.6.1.2. Actions to Take11.6.2. Understanding Your Brand and Category Terms11.6.2.1. Why Should You Care?11.6.3. The Optimal Search Marketing Strategy11.6.3.1. The SEM Fix11.6.3.2. Insights You Can Find11.6.3.3. Actions to Take11.6.4. Executing the Optimal Long-Tail Strategy11.7. Search: Measuring the Value of Upper Funnel Keywords11.8. Search: Advanced Pay-per-Click Analyses11.8.1. Identifying Keyword Arbitrage Opportunities11.8.2. Focusing on "What's Changed"11.8.3. Analyzing Visual Impression Share and Lost Revenue11.8.4. Embracing the ROI Distribution Report11.8.5. Zeroing In on the User Search Query and Match Types
12. Advanced Principles for Becoming an Analysis Ninja
12.1. Multitouch Campaign Attribution Analysis12.1.1. What Is All This Multitouch?12.1.2. Do You Have an Attribution Problem?12.1.3. Attribution Models12.1.3.1. Last-Click Credit12.1.3.2. First-Click Credit12.1.3.3. Even-Click Credit12.1.3.4. Split Credit12.1.3.5. Custom Credit12.1.4. Core Challenge with Attribution Analysis in the Real World12.1.5. Promising Alternatives to Attribution Analysis12.1.5.1. Media Mix Modeling12.1.5.1.1. Challenges of Online Media Mix Modeling12.1.5.2. Marginal Attribution Analysis12.1.5.2.1. Marginal Attribution Analysis Challenges12.1.6. Parting Thoughts About Multitouch12.2. Multichannel Analytics: Measurement Tips for a Nonline World12.2.1. Tracking Online Impact of Offline Campaigns12.2.1.1. Vanity URLs12.2.1.2. Unique Redeemable Coupons and Offer Codes12.2.1.3. Online Surveys and Market Research12.2.1.4. Correlating Traffic Patterns and Offline Ad Schedules12.2.1.5. Leveraging the Power of Controlled Experiments12.2.2. Tracking the Offline Impact of Online Campaigns12.2.2.1. Measuring Offline Calls to Action12.2.2.2. Tracking Phone Calls and Live Chat12.2.2.3. Using Unique Coupon Codes and Offers12.2.2.4. Marrying and Mining Online and Offline Data12.2.2.5. Using Surveys to Predict Offline Impact12.2.2.6. Conducting Controlled Experiments12.2.2.7. Leveraging Primary Research
13. The Web Analytics Career
13.1. Planning a Web Analytics Career: Options, Salary Prospects, and Growth13.1.1. Technical Individual Contributor13.1.1.1. Career Prospects13.1.1.2. Salary Prospects13.1.1.3. Long-Term Job Title Growth (Promotions)13.1.2. Business Individual Contributor13.1.2.1. Career Prospects13.1.2.2. Salary Prospects13.1.2.3. Long-Term Job Title Growth13.1.3. Technical Team Leader13.1.3.1. Career Prospects13.1.3.2. Salary Prospects13.1.3.3. Long-Term Job Title Growth13.1.4. Business Team Leader13.1.4.1. Career Prospects13.1.4.2. Salary Prospects13.1.4.3. Long-Term Job Title Growth13.2. Cultivating Skills for a Successful Career in Web Analysis13.2.1. Do It: Use the Data13.2.2. Get Experience with Multiple Tools13.2.3. Play in the Real World13.2.3.1. Web Analytics Education13.2.3.2. Beyond a Web Analytics Education13.2.3.3. Online Advertising Education13.2.3.4. Social Media Analytics Education13.2.3.5. The Bottom Line on Online Marketing and Analytics Education13.2.4. Become a Data Capture Detective13.2.5. Rock Math: Learn Basic Statistics13.2.6. Ask Good Questions13.2.7. Work Closely with Business Teams13.2.8. Learn Effective Data Visualization and Presentation13.2.9. Stay Current: Attend Free Webinars13.2.10. Stay Current: Read Blogs13.3. An Optimal Day in the Life of an Analysis Ninja13.4. Hiring the Best: Advice for Analytics Managers and Directors13.4.1. Key Attributes of Great Analytics Professionals13.4.2. Experienced or Novice: Making the Right Choice13.4.3. The Single Greatest Test in an Interview: Critical Thinking
14. HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture
14.1. Transforming Company Culture: How to Excite People About Analytics14.1.1. Do Something Surprising: Don't Puke Data14.1.1.1. Start with Outcomes and Impact, Not Visits14.1.1.2. Create Heroes and Role Models14.1.1.3. If You Want Excitement, Make It Fun!14.1.1.4. Hold Contests14.1.1.5. Hold Internal Conferences14.1.1.6. Hold Office Hours14.2. Deliver Reports and Analyses That Drive Action14.2.1.14.2.1.1. The "What's Your Point?" Filter14.2.1.2. The Hard Tie to Business Outcomes14.2.1.3. Present Multiple Perspectives: True Web Analytics 2.014.2.2. The Unböring Filter14.2.3. Connecting Insights with Actual Data14.2.3.1. The Expectations of Scale Filter14.2.3.2. Having Something Unique to Say14.3. Changing Metric Definitions to Change Cultures: Brand Evangelists Index14.3.1. The Case and the Analysis14.3.2. The Problem14.3.3. The Solution14.3.4. The Results14.3.5. The Outcome14.3.6. An Alternative Calculation: Weighted Mean14.3.7. The Punch Line14.4. Slay the Data Quality Dragon: Shift from Questioning to Using Data14.4.1. Pick a Different Boss14.4.1.1. Educate Your Organization About the "Perfect" Source14.4.2. Distract HiPPOs with Actionable Insights14.4.3. Dirty Little Secret 1: Head Data Can Be Actionable in the First Week/Month14.4.4. Dirty Little Secret 2: Data Precision Improves Lower in the Funnel14.4.5. The Solution Is Not to Implement Another Tool!14.4.6. Recognize Diminishing Marginal Returns14.4.7. Small Site, Bigger Problems14.4.7.1. Illogical Customer Behavior and Inaccurate Benchmarks14.4.8. Fail Faster on the Web14.5. Five Rules for Creating a Data-Driven Boss14.5.1. Get Over Yourself14.5.2. Embrace Incompleteness14.5.3. Always Give 10 Percent Extra14.5.4. Become a Marketer14.5.5. Business in the Service of Data. Not!14.5.6. Adopt the Web Analytics 2.0 Mind-Set14.6. Need Budget? Strategies for Embarrassing Your Organization14.6.1.14.6.1.1. Implement an Experimentation and Testing Program14.6.2. Capture Voice of Customer14.6.2.1. Deploy the Benchmarks14.6.2.2. Competitive Intelligence: Your New Best Friend14.6.3. Hijack a Friendly Website14.6.4. If All Else Fails...Call Me!14.7. Strategies to Break Down Barriers to Web Measurement14.7.1. First, a Surprising Insight14.7.2. Lack of Budget/Resources14.7.3. Lack of Strategy14.7.4. Siloed Organization14.7.5. Lack of Understanding14.7.6. Too Much Data14.7.7. Lack of Senior Management Buy-In14.7.7.1. Difficulty Reconciling Data14.7.8. IT Blockages14.7.9. Lack of Trust in Analytics14.7.10. Finding Staff14.7.11. Poor Technology14.8. Who Owns Web Analytics?14.8.1. To Centralize or Not to Centralize14.8.2. Evolution of the Team
A. About the Companion CD
A.1. What You'll Find on the CDA.1.1. PodcastsA.1.2. VideosA.1.3. Resources and PresentationsA.1.4. Adobe ReaderA.2. System RequirementsA.3. Using the CDA.4. TroubleshootingA.4.1. Customer Care

Overview

Adeptly address today’s business challenges with this powerful new book from web analytics thought leader Avinash Kaushik. Web Analytics 2.0 presents a new framework that will permanently change how you think about analytics. It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multichannel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers. The book will help your organization become more data driven while you become a super analysis ninja!

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