Web Analytics 2.0

Avinash Kaushik is the author of the leading research & analytics blog Occam's Razor. He is also the Analytics Evangelist for Google and the Chief Education Officer at Market Motive, Inc. He is a bestselling author and a frequent speaker at key industry conferences around the globe and at leading American universities. He was the recipient of the 2009 Statistical Advocate of the Year award from the American Statistical Association. The author donates all proceeds from his books to two charities, The Smile Train and The Ekal Vidyalaya Foundation.

• Introduction xxiChapter 1 The Bold New World of Web Analytics 2.0 1State of the Analytics Union 2State of the Industry 3Rethinking Web Analytics: Meet Web Analytics 2.0 4The What: Clickstream 7The How Much: Multiple Outcomes Analysis 7The Why: Experimentation and Testing 8The Why: Voice of Customer 9The What Else: Competitive Intelligence 9Change: Yes We Can! 10The Strategic Imperative 10The Tactical Shift 11Bonus Analytics 13Chapter 2 The Optimal Strategy for Choosing Your Web Analytics Soul Mate 15Predetermining Your Future Success 16Step 1: Three Critical Questions to Ask Yourself Before You Seek an Analytics Soul Mate! 17Q1: “Do I want reporting or analysis?” 17Q2: “Do I have IT strength, business strength, or both?” 19Q3: “Am I solving just for Clickstream or for Web Analytics 2.0?” 20Step 2: Ten Questions to Ask Vendors Before You Marry Them 21Q1: “What is the difference between your tool/solution and free tools from Yahoo! and Google?” 21Q2: “Are you 100 percent ASP, or do you offer a software version? Are you planning a software version?” 22Q3: “What data capture mechanisms do you use?” 22Q4: “Can you calculate the total cost of ownership for your tool?” 23Q5: “What kind of support do you offer? What do you include for free, and what costs more? Is it free 24/7?” 24Q6: “What features in your tool allow me to segment the data?” 25Q7: “What options do I have for exporting data from your system into our company’s system?” 25Q8: “What features do you provide for me to integrate data from other sources into your tool?” 26Q9: “Can you name two new features/tools/acquisitions your company is cooking up to stay ahead of your competition for the next three years?” 26Q10: “Why did the last two clients you lost cancel their contracts? Who are they using now? May we call one of these former clients?” 27Comparing Web Analytics Vendors: Diversify and Conquer 28The Three-Bucket Strategy 28Step 3: Identifying Your Web Analytics Soul Mate (How to Run an Effective Tool Pilot) 29Step 4: Negotiating the Prenuptials: Check SLAs for Your Web Analytics Vendor Contract 32Chapter 3 The Awesome World of Clickstream Analysis: Metrics 35Standard Metrics Revisited: Eight Critical Web Metrics 36Visits and Visitors 37Time on Page and Time on Site 44Bounce Rate 51Exit Rate 53Conversion Rate 55Engagement 56Web Metrics Demystified 59Four Attributes of Great Metrics 59Example of a Great Web Metric 62Three Avinash Life Lessons for Massive Success 62Strategically-aligned Tactics for Impactful Web Metrics 64Diagnosing the Root Cause of a Metric’s Performance—Conversion 64Leveraging Custom Reporting 66Starting with Macro Insights 70Chapter 4 The Awesome World of Clickstream Analysis: Practical Solutions 75A Web Analytics Primer 76Getting Primitive Indicators Out of the Way 76Understanding Visitor Acquisition Strengths 78Fixing Stuff and Saving Money 79Click Density Analysis 81Measuring Visits to Purchase 83The Best Web Analytics Report 85Sources of Traffic 86Outcomes 87Foundational Analytical Strategies 87Segment or Go Home 88Focus on Customer Behavior, Not Aggregates 93Everyday Clickstream Analyses Made Actionable 94Internal Site Search Analysis 95Search Engine Optimization (SEO) Analysis 101Pay Per Click/Paid Search Analysis 110Direct Traffic Analysis 116Email Campaign Analysis 119Rich Experience Analysis: Flash, Video, and Widgets 122Reality Check: Perspectives on Key Web Analytics Challenges 126Visitor Tracking Cookies 126Data Sampling 411 130The Value of Historical Data 133The Usefulness of Video Playback of Customer Experience 136The Ultimate Data Reconciliation Checklist 138Chapter 5 The Key to Glory: Measuring Success 145Focus on the “Critical Few” 147Five Examples of Actionable Outcome KPIs 149Task Completion Rate 149Share of Search 150Visitor Loyalty and Recency 150RSS/Feed Subscribers 150% of Valuable Exits 151Moving Beyond Conversion Rates 151Cart and Checkout Abandonment 152Days and Visits to Purchase 153Average Order Value 153Primary Purpose (Identify the Convertible) 154Measuring Macro and Micro Conversions 156Examples of Macro and Micro Conversions 158Quantifying Economic Value 159Measuring Success for a Non-ecommerce Website 162Visitor Loyalty 162Visitor Recency 164Length of Visit 165Depth of Visit 165Measuring B2B Websites 166Chapter 6 Solving the “Why” Puzzle: Leveraging Qualitative Data 169Lab Usability Studies: What, Why, and How Much? 170What Is Lab Usability? 170How to Conduct a Test 171Best Practices for Lab Usability Studies 174Benefits of Lab Usability Studies 174Areas of Caution 174Usability Alternatives: Remote and Online Outsourced 175Live Recruiting and Remote User Research 176Surveys: Truly Scalable Listening 179Types of Surveys 180The Single Biggest Surveying Mistake 184Three Greatest Survey Questions Ever 185Eight Tips for Choosing an Online Survey Provider 187Web-Enabled Emerging User Research Options 190Competitive Benchmarking Studies 190Rapid Usability Tests 191Online Card-Sorting Studies 191Artificially Intelligent Visual Heat Maps 192Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation 195A Primer on Testing Options: A/B and MVT 197A/B Testing 197Multivariate Testing 198Actionable Testing Ideas 202Fix the Big Losers—Landing Pages 202Focus on Checkout, Registration, and Lead Submission Pages 202Optimize the Number and Layout of Ads 203Test Different Prices and Selling Tactics 203Test Box Layouts, DVD Covers, and Offline Stuff 204Optimize Your Outbound Marketing Efforts 204Controlled Experiments: Step Up Your Analytics Game! 205Measuring Paid Search Impact on Brand Keywords and Cannibalization 205Examples of Controlled Experiments 207Challenges and Benefits 208Creating and Nurturing a Testing Culture 209Tip 1: Your First Test is “Do or Die” 209Tip 2: Don’t Get Caught in the Tool/Consultant Hype 209Tip 3: “Open the Kimono”—Get Over Yourself 210Tip 4: Start with a Hypothesis 210Tip 5: Make Goals Evaluation Criteria and Up-Front Decisions 210Tip 6: Test For and Measure Multiple Outcomes 211Tip 7: Source Your Tests in Customer Pain 211Tip 8: Analyze Data and Communicate Learnings 212Tip 9: Two Must-Haves: Evangelism and Expertise 212Chapter 8 Competitive Intelligence Analysis 213CI Data Sources, Types, and Secrets 214Toolbar Data 215Panel Data 216ISP (Network) Data 217Search Engine Data 217Benchmarks from Web Analytics Vendors 218Self-reported Data 219Hybrid Data 220Website Traffic Analysis 221Comparing Long-Term Traffic Trends 222Analyzing Competitive Sites Overlap and Opportunities 223Analyzing Referrals and Destinations 224Search and Keyword Analysis 225Top Keywords Performance Trend 226Geographic Interest and Opportunity Analysis 227Related and Fast-Rising Searches 230Share-of-Shelf Analysis 231Competitive Keyword Advantage Analysis 233Keyword Expansion Analysis 234Audience Identification and Segmentation Analysis 235Demographic Segmentation Analysis 236Psychographic Segmentation Analysis 238Search Behavior and Audience Segmentation Analysis 239Chapter 9 Emerging Analytics: Social, Mobile, and Video 241Measuring the New Social Web: The Data Challenge 242The Content Democracy Evolution 243The Twitter Revolution 247Analyzing Offline Customer Experiences (Applications) 248Analyzing Mobile Customer Experiences 250Mobile Data Collection: Options 250Mobile Reporting and Analysis 253Measuring the Success of Blogs 257Raw Author Contribution 257Holistic Audience Growth 258Citations and Ripple Index 262Cost of Blogging 263Benefit (ROI) from Blogging 263Quantifying the Impact of Twitter 266Growth in Number of Followers 266Message Amplification 267Click-Through Rates and Conversions 268Conversation Rate 270Emerging Twitter Metrics 271Analyzing Performance of Videos 273Data Collection for Videos 273Key Video Metrics and Analysis 274Advanced Video Analysis 278Chapter 10 Optimal Solutions for Hidden Web Analytics Traps 283Accuracy or Precision? 284A Six-Step Process for Dealing with Data Quality 286Building the Action Dashboard 288Creating Awesome Dashboards 288The Consolidated Dashboard 290Five Rules for High-Impact Dashboards 291Nonline Marketing Opportunity and Multichannel Measurement 294Shifting to the Nonline Marketing Model 294Multichannel Analytics 296The Promise and Challenge of Behavior Targeting 298The Promise of Behavior Targeting 299Overcoming Fundamental Analytics Challenges 299Two Prerequisites for Behavior Targeting 301Online Data Mining and Predictive Analytics: Challenges 302Type of Data 303Number of Variables 304Multiple Primary Purposes 304Multiple Visit Behaviors 305Missing Primary Keys and Data Sets 305Path to Nirvana: Steps Toward Intelligent Analytics Evolution 306Step 1: Tag, Baby, Tag! 307Step 2: Configuring Web Analytics Tool Settings 308Step 3: Campaign/Acquisition Tracking 309Step 4: Revenue and Uber-intelligence 310Step 5: Rich-Media Tracking (Flash, Widgets, Video) 311Chapter 11 Guiding Principles for Becoming an Analysis Ninja 313Context Is Queen 314Comparing Key Metrics Performance for Different Time Periods 314Providing Context Through Segmenting 315Comparing Key Metrics and Segments Against Site Average 316Joining PALM (People Against Lonely Metrics) 318Leveraging Industry Benchmarks and Competitive Data 319Tapping into Tribal Knowledge 320Comparing KPI Trends Over Time 321Presenting Tribal Knowledge 322Segmenting to the Rescue! 323Beyond the Top 10: What’s Changed 324True Value: Measuring Latent Conversions and Visitor Behavior 327Latent Visitor Behavior 327Latent Conversions 329Four Inactionable KPI Measurement Techniques 330Averages 330Percentages 332Ratios 334Compound or Calculated Metrics 336Search: Achieving the Optimal Long-Tail Strategy 338Compute Your Head and Tail 339Understanding Your Brand and Category Terms 341The Optimal Search Marketing Strategy 342Executing the Optimal Long-Tail Strategy 344Search: Measuring the Value of Upper Funnel Keywords 346Search: Advanced Pay-per-Click Analyses 348Identifying Keyword Arbitrage Opportunities 349Focusing on “What’s Changed” 350Analyzing Visual Impression Share and Lost Revenue 351Embracing the ROI Distribution Report 353Zeroing In on the User Search Query and Match Types 354Chapter 12 Advanced Principles for Becoming an Analysis Ninja 357Multitouch Campaign Attribution Analysis 358What Is All This Multitouch? 358Do You Have an Attribution Problem? 359Attribution Models 361Core Challenge with Attribution Analysis in the Real World 364Promising Alternatives to Attribution Analysis 365Parting Thoughts About Multitouch 368Multichannel Analytics: Measurement Tips for a Nonline World 368Tracking Online Impact of Offline Campaigns 369Tracking the Offline Impact of Online Campaigns 376Chapter 13 The Web Analytics Career 385Planning a Web Analytics Career: Options, Salary Prospects, and Growth 386Technical Individual Contributor 388Business Individual Contributor 388Technical Team Leader 390Business Team Leader 391Cultivating Skills for a Successful Career in Web Analysis 393Do It: Use the Data 393Get Experience with Multiple Tools 393Play in the Real World 394Become a Data Capture Detective 396Rock Math: Learn Basic Statistics 396Ask Good Questions 397Work Closely with Business Teams 398Learn Effective Data Visualization and Presentation 398Stay Current: Attend Free Webinars 399Stay Current: Read Blogs 400An Optimal Day in the Life of an Analysis Ninja 401Hiring the Best: Advice for Analytics Managers and Directors 403Key Attributes of Great Analytics Professionals 404Experienced or Novice: Making the Right Choice 405The Single Greatest Test in an Interview: Critical Thinking 405Chapter 14 HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture 407Transforming Company Culture: How to Excite People About Analytics 408Do Something Surprising: Don’t Puke Data 409Deliver Reports and Analyses That Drive Action 412The Unböring Filter 413Connecting Insights with Actual Data 414Changing Metric Definitions to Change Cultures: Brand Evangelists Index 415The Case and the Analysis 415The Problem 416The Solution 417The Results 417The Outcome 418An Alternative Calculation: Weighted Mean 418The Punch Line 419Slay the Data Quality Dragon: Shift from Questioning to Using Data 420Pick a Different Boss 420Distract HiPPOs with Actionable Insights 422Dirty Little Secret 1: Head Data Can Be Actionable in the First Week/Month 422Dirty Little Secret 2: Data Precision Improves Lower in the Funnel 423The Solution Is Not to Implement Another Tool! 423Recognize Diminishing Marginal Returns 424Small Site, Bigger Problems 424Fail Faster on the Web 425Five Rules for Creating a Data-Driven Boss 426Get Over Yourself 426Embrace Incompleteness 426Always Give 10 Percent Extra 427Become a Marketer 427Business in the Service of Data. Not! 428Adopt the Web Analytics 2.0 Mind-Set 428Need Budget? Strategies for Embarrassing Your Organization 429Capture Voice of Customer 430Hijack a Friendly Website 431If All Else Fails…Call Me! 432Strategies to Break Down Barriers to Web Measurement 432First, a Surprising Insight 433Lack of Budget/Resources 433Lack of Strategy 434Siloed Organization 434Lack of Understanding 435Too Much Data 435Lack of Senior Management Buy-In 436IT Blockages 437Lack of Trust in Analytics 439Finding Staff 439Poor Technology 439Who Owns Web Analytics? 440To Centralize or Not to Centralize 440Evolution of the Team 441Appendix About the Companion CD 443Index 447