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!
1100298463
Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity
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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Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity
512
by Avinash Kaushik
Avinash Kaushik
Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity
512
by Avinash Kaushik
Avinash Kaushik
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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!
Product Details
| ISBN-13: | 9780470529393 |
|---|---|
| Publisher: | Wiley |
| Publication date: | 10/26/2009 |
| Pages: | 512 |
| Product dimensions: | 7.60(w) x 9.30(h) x 1.30(d) |
About the Author
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.
Read an Excerpt
Table of Contents
Introduction xxiChapter 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
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