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 xxi

Chapter 1 The Bold New World of Web Analytics 2.0 1

State of the Analytics Union 2

State of the Industry 3

Rethinking Web Analytics: Meet Web Analytics 2.0 4

The What: Clickstream 7

The How Much: Multiple Outcomes Analysis 7

The Why: Experimentation and Testing 8

The Why: Voice of Customer 9

The What Else: Competitive Intelligence 9

Change: Yes We Can! 10

The Strategic Imperative 10

The Tactical Shift 11

Bonus Analytics 13

Chapter 2 The Optimal Strategy for Choosing Your Web Analytics Soul Mate 15

Predetermining Your Future Success 16

Step 1: Three Critical Questions to Ask Yourself Before You Seek an Analytics Soul Mate! 17

Q1: “Do I want reporting or analysis?” 17

Q2: “Do I have IT strength, business strength, or both?” 19

Q3: “Am I solving just for Clickstream or for Web Analytics 2.0?” 20

Step 2: Ten Questions to Ask Vendors Before You Marry Them 21

Q1: “What is the difference between your tool/solution and free tools from Yahoo! and Google?” 21

Q2: “Are you 100 percent ASP, or do you offer a software version? Are you planning a software version?” 22

Q3: “What data capture mechanisms do you use?” 22

Q4: “Can you calculate the total cost of ownership for your tool?” 23

Q5: “What kind of support do you offer? What do you include for free, and what costs more? Is it free 24/7?” 24

Q6: “What features in your tool allow me to segment the data?” 25

Q7: “What options do I have for exporting data from your system into our company’s system?” 25

Q8: “What features do you provide for me to integrate data from other sources into your tool?” 26

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?” 26

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?” 27

Comparing Web Analytics Vendors: Diversify and Conquer 28

The Three-Bucket Strategy 28

Step 3: Identifying Your Web Analytics Soul Mate (How to Run an Effective Tool Pilot) 29

Step 4: Negotiating the Prenuptials: Check SLAs for Your Web Analytics Vendor Contract 32

Chapter 3 The Awesome World of Clickstream Analysis: Metrics 35

Standard Metrics Revisited: Eight Critical Web Metrics 36

Visits and Visitors 37

Time on Page and Time on Site 44

Bounce Rate 51

Exit Rate 53

Conversion Rate 55

Engagement 56

Web Metrics Demystified 59

Four Attributes of Great Metrics 59

Example of a Great Web Metric 62

Three Avinash Life Lessons for Massive Success 62

Strategically-aligned Tactics for Impactful Web Metrics 64

Diagnosing the Root Cause of a Metric’s Performance—Conversion 64

Leveraging Custom Reporting 66

Starting with Macro Insights 70

Chapter 4 The Awesome World of Clickstream Analysis: Practical Solutions 75

A Web Analytics Primer 76

Getting Primitive Indicators Out of the Way 76

Understanding Visitor Acquisition Strengths 78

Fixing Stuff and Saving Money 79

Click Density Analysis 81

Measuring Visits to Purchase 83

The Best Web Analytics Report 85

Sources of Traffic 86

Outcomes 87

Foundational Analytical Strategies 87

Segment or Go Home 88

Focus on Customer Behavior, Not Aggregates 93

Everyday Clickstream Analyses Made Actionable 94

Internal Site Search Analysis 95

Search Engine Optimization (SEO) Analysis 101

Pay Per Click/Paid Search Analysis 110

Direct Traffic Analysis 116

Email Campaign Analysis 119

Rich Experience Analysis: Flash, Video, and Widgets 122

Reality Check: Perspectives on Key Web Analytics Challenges 126

Visitor Tracking Cookies 126

Data Sampling 411 130

The Value of Historical Data 133

The Usefulness of Video Playback of Customer Experience 136

The Ultimate Data Reconciliation Checklist 138

Chapter 5 The Key to Glory: Measuring Success 145

Focus on the “Critical Few” 147

Five Examples of Actionable Outcome KPIs 149

Task Completion Rate 149

Share of Search 150

Visitor Loyalty and Recency 150

RSS/Feed Subscribers 150

% of Valuable Exits 151

Moving Beyond Conversion Rates 151

Cart and Checkout Abandonment 152

Days and Visits to Purchase 153

Average Order Value 153

Primary Purpose (Identify the Convertible) 154

Measuring Macro and Micro Conversions 156

Examples of Macro and Micro Conversions 158

Quantifying Economic Value 159

Measuring Success for a Non-ecommerce Website 162

Visitor Loyalty 162

Visitor Recency 164

Length of Visit 165

Depth of Visit 165

Measuring B2B Websites 166

Chapter 6 Solving the “Why” Puzzle: Leveraging Qualitative Data 169

Lab Usability Studies: What, Why, and How Much? 170

What Is Lab Usability? 170

How to Conduct a Test 171

Best Practices for Lab Usability Studies 174

Benefits of Lab Usability Studies 174

Areas of Caution 174

Usability Alternatives: Remote and Online Outsourced 175

Live Recruiting and Remote User Research 176

Surveys: Truly Scalable Listening 179

Types of Surveys 180

The Single Biggest Surveying Mistake 184

Three Greatest Survey Questions Ever 185

Eight Tips for Choosing an Online Survey Provider 187

Web-Enabled Emerging User Research Options 190

Competitive Benchmarking Studies 190

Rapid Usability Tests 191

Online Card-Sorting Studies 191

Artificially Intelligent Visual Heat Maps 192

Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation 195

A Primer on Testing Options: A/B and MVT 197

A/B Testing 197

Multivariate Testing 198

Actionable Testing Ideas 202

Fix the Big Losers—Landing Pages 202

Focus on Checkout, Registration, and Lead Submission Pages 202

Optimize the Number and Layout of Ads 203

Test Different Prices and Selling Tactics 203

Test Box Layouts, DVD Covers, and Offline Stuff 204

Optimize Your Outbound Marketing Efforts 204

Controlled Experiments: Step Up Your Analytics Game! 205

Measuring Paid Search Impact on Brand Keywords and Cannibalization 205

Examples of Controlled Experiments 207

Challenges and Benefits 208

Creating and Nurturing a Testing Culture 209

Tip 1: Your First Test is “Do or Die” 209

Tip 2: Don’t Get Caught in the Tool/Consultant Hype 209

Tip 3: “Open the Kimono”—Get Over Yourself 210

Tip 4: Start with a Hypothesis 210

Tip 5: Make Goals Evaluation Criteria and Up-Front Decisions 210

Tip 6: Test For and Measure Multiple Outcomes 211

Tip 7: Source Your Tests in Customer Pain 211

Tip 8: Analyze Data and Communicate Learnings 212

Tip 9: Two Must-Haves: Evangelism and Expertise 212

Chapter 8 Competitive Intelligence Analysis 213

CI Data Sources, Types, and Secrets 214

Toolbar Data 215

Panel Data 216

ISP (Network) Data 217

Search Engine Data 217

Benchmarks from Web Analytics Vendors 218

Self-reported Data 219

Hybrid Data 220

Website Traffic Analysis 221

Comparing Long-Term Traffic Trends 222

Analyzing Competitive Sites Overlap and Opportunities 223

Analyzing Referrals and Destinations 224

Search and Keyword Analysis 225

Top Keywords Performance Trend 226

Geographic Interest and Opportunity Analysis 227

Related and Fast-Rising Searches 230

Share-of-Shelf Analysis 231

Competitive Keyword Advantage Analysis 233

Keyword Expansion Analysis 234

Audience Identification and Segmentation Analysis 235

Demographic Segmentation Analysis 236

Psychographic Segmentation Analysis 238

Search Behavior and Audience Segmentation Analysis 239

Chapter 9 Emerging Analytics: Social, Mobile, and Video 241

Measuring the New Social Web: The Data Challenge 242

The Content Democracy Evolution 243

The Twitter Revolution 247

Analyzing Offline Customer Experiences (Applications) 248

Analyzing Mobile Customer Experiences 250

Mobile Data Collection: Options 250

Mobile Reporting and Analysis 253

Measuring the Success of Blogs 257

Raw Author Contribution 257

Holistic Audience Growth 258

Citations and Ripple Index 262

Cost of Blogging 263

Benefit (ROI) from Blogging 263

Quantifying the Impact of Twitter 266

Growth in Number of Followers 266

Message Amplification 267

Click-Through Rates and Conversions 268

Conversation Rate 270

Emerging Twitter Metrics 271

Analyzing Performance of Videos 273

Data Collection for Videos 273

Key Video Metrics and Analysis 274

Advanced Video Analysis 278

Chapter 10 Optimal Solutions for Hidden Web Analytics Traps 283

Accuracy or Precision? 284

A Six-Step Process for Dealing with Data Quality 286

Building the Action Dashboard 288

Creating Awesome Dashboards 288

The Consolidated Dashboard 290

Five Rules for High-Impact Dashboards 291

Nonline Marketing Opportunity and Multichannel Measurement 294

Shifting to the Nonline Marketing Model 294

Multichannel Analytics 296

The Promise and Challenge of Behavior Targeting 298

The Promise of Behavior Targeting 299

Overcoming Fundamental Analytics Challenges 299

Two Prerequisites for Behavior Targeting 301

Online Data Mining and Predictive Analytics: Challenges 302

Type of Data 303

Number of Variables 304

Multiple Primary Purposes 304

Multiple Visit Behaviors 305

Missing Primary Keys and Data Sets 305

Path to Nirvana: Steps Toward Intelligent Analytics Evolution 306

Step 1: Tag, Baby, Tag! 307

Step 2: Configuring Web Analytics Tool Settings 308

Step 3: Campaign/Acquisition Tracking 309

Step 4: Revenue and Uber-intelligence 310

Step 5: Rich-Media Tracking (Flash, Widgets, Video) 311

Chapter 11 Guiding Principles for Becoming an Analysis Ninja 313

Context Is Queen 314

Comparing Key Metrics Performance for Different Time Periods 314

Providing Context Through Segmenting 315

Comparing Key Metrics and Segments Against Site Average 316

Joining PALM (People Against Lonely Metrics) 318

Leveraging Industry Benchmarks and Competitive Data 319

Tapping into Tribal Knowledge 320

Comparing KPI Trends Over Time 321

Presenting Tribal Knowledge 322

Segmenting to the Rescue! 323

Beyond the Top 10: What’s Changed 324

True Value: Measuring Latent Conversions and Visitor Behavior 327

Latent Visitor Behavior 327

Latent Conversions 329

Four Inactionable KPI Measurement Techniques 330

Averages 330

Percentages 332

Ratios 334

Compound or Calculated Metrics 336

Search: Achieving the Optimal Long-Tail Strategy 338

Compute Your Head and Tail 339

Understanding Your Brand and Category Terms 341

The Optimal Search Marketing Strategy 342

Executing the Optimal Long-Tail Strategy 344

Search: Measuring the Value of Upper Funnel Keywords 346

Search: Advanced Pay-per-Click Analyses 348

Identifying Keyword Arbitrage Opportunities 349

Focusing on “What’s Changed” 350

Analyzing Visual Impression Share and Lost Revenue 351

Embracing the ROI Distribution Report 353

Zeroing In on the User Search Query and Match Types 354

Chapter 12 Advanced Principles for Becoming an Analysis Ninja 357

Multitouch Campaign Attribution Analysis 358

What Is All This Multitouch? 358

Do You Have an Attribution Problem? 359

Attribution Models 361

Core Challenge with Attribution Analysis in the Real World 364

Promising Alternatives to Attribution Analysis 365

Parting Thoughts About Multitouch 368

Multichannel Analytics: Measurement Tips for a Nonline World 368

Tracking Online Impact of Offline Campaigns 369

Tracking the Offline Impact of Online Campaigns 376

Chapter 13 The Web Analytics Career 385

Planning a Web Analytics Career: Options, Salary Prospects, and Growth 386

Technical Individual Contributor 388

Business Individual Contributor 388

Technical Team Leader 390

Business Team Leader 391

Cultivating Skills for a Successful Career in Web Analysis 393

Do It: Use the Data 393

Get Experience with Multiple Tools 393

Play in the Real World 394

Become a Data Capture Detective 396

Rock Math: Learn Basic Statistics 396

Ask Good Questions 397

Work Closely with Business Teams 398

Learn Effective Data Visualization and Presentation 398

Stay Current: Attend Free Webinars 399

Stay Current: Read Blogs 400

An Optimal Day in the Life of an Analysis Ninja 401

Hiring the Best: Advice for Analytics Managers and Directors 403

Key Attributes of Great Analytics Professionals 404

Experienced or Novice: Making the Right Choice 405

The Single Greatest Test in an Interview: Critical Thinking 405

Chapter 14 HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture 407

Transforming Company Culture: How to Excite People About Analytics 408

Do Something Surprising: Don’t Puke Data 409

Deliver Reports and Analyses That Drive Action 412

The Unböring Filter 413

Connecting Insights with Actual Data 414

Changing Metric Definitions to Change Cultures: Brand Evangelists Index 415

The Case and the Analysis 415

The Problem 416

The Solution 417

The Results 417

The Outcome 418

An Alternative Calculation: Weighted Mean 418

The Punch Line 419

Slay the Data Quality Dragon: Shift from Questioning to Using Data 420

Pick a Different Boss 420

Distract HiPPOs with Actionable Insights 422

Dirty Little Secret 1: Head Data Can Be Actionable in the First Week/Month 422

Dirty Little Secret 2: Data Precision Improves Lower in the Funnel 423

The Solution Is Not to Implement Another Tool! 423

Recognize Diminishing Marginal Returns 424

Small Site, Bigger Problems 424

Fail Faster on the Web 425

Five Rules for Creating a Data-Driven Boss 426

Get Over Yourself 426

Embrace Incompleteness 426

Always Give 10 Percent Extra 427

Become a Marketer 427

Business in the Service of Data. Not! 428

Adopt the Web Analytics 2.0 Mind-Set 428

Need Budget? Strategies for Embarrassing Your Organization 429

Capture Voice of Customer 430

Hijack a Friendly Website 431

If All Else Fails…Call Me! 432

Strategies to Break Down Barriers to Web Measurement 432

First, a Surprising Insight 433

Lack of Budget/Resources 433

Lack of Strategy 434

Siloed Organization 434

Lack of Understanding 435

Too Much Data 435

Lack of Senior Management Buy-In 436

IT Blockages 437

Lack of Trust in Analytics 439

Finding Staff 439

Poor Technology 439

Who Owns Web Analytics? 440

To Centralize or Not to Centralize 440

Evolution of the Team 441

Appendix About the Companion CD 443

Index 447