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Web analytics 2.0 : the art of online accountability & science of customer centricity

Author: Avinash Kaushik
Publisher: Hoboken, N.J : Wiley, 2010.
Series: Sybex serious skills
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Avinash Kaushik
ISBN: 9780470529393 0470529393 9780470596425 0470596422 9780470596449 0470596449
OCLC Number: 891536184
Description: 1 Online-Ressource
Contents: 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 Unboering 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
Series Title: Sybex serious skills
Responsibility: Avinash Kaushik.

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