Web Analytics 2.0 E-Book

Avinash Kaushik

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 800

Veröffentlichungsjahr: 2010

Beschreibung

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!

Leseprobe

Table of Contents

Cover

Title Page

Praise for Web Analytics 2.0

Copyright

Publisher's Note

Dedication

Acknowledgments

About the Author

Introduction

The Awesome World of Data-Driven Decision Making

What’s Inside the Book?

Valuable Multimedia Content on the CD

Request for Feedback

The Beginning

Chapter 1: The Bold New World of Web Analytics 2.0

State of the Analytics Union

State of the Industry

Rethinking Web Analytics: Meet Web Analytics 2.0

Change: Yes We Can!

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

Predetermining Your Future Success

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

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

Comparing Web Analytics Vendors: Diversify and Conquer

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

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

Chapter 3: The Awesome World of Clickstream Analysis: Metrics

Standard Metrics Revisited: Eight Critical Web Metrics

Bounce Rate

Exit Rate

Conversion Rate

Engagement

Web Metrics Demystified

Strategically Aligned Tactics for Impactful Web Metrics

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

A Web Analytics Primer

The Best Web Analytics Report

Foundational Analytical Strategies

Everyday Clickstream Analyses Made Actionable

Reality Check: Perspectives on Key Web Analytics Challenges

Chapter 5: The Key to Glory: Measuring Success

Focus on the “Critical Few”

Five Examples of Actionable Outcome KPIs

Moving Beyond Conversion Rates

Measuring Macro and Micro Conversions

Quantifying Economic Value

Measuring Success for a Non-ecommerce Website

Measuring B2B Websites

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

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

Usability Alternatives: Remote and Online Outsourced

Surveys: Truly Scalable Listening

Web-Enabled Emerging User Research Options

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

A Primer on Testing Options: A/B and MVT

Actionable Testing Ideas

Controlled Experiments: Step Up Your Analytics Game!

Creating and Nurturing a Testing Culture

Chapter 8: Competitive Intelligence Analysis

CI Data Sources, Types, and Secrets

Website Traffic Analysis

Search and Keyword Analysis

Audience Identification and Segmentation Analysis

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

Measuring the New Social Web: The Data Challenge

Analyzing Offline Customer Experiences (Applications)

Analyzing Mobile Customer Experiences

Measuring the Success of Blogs

Quantifying the Impact of Twitter

Analyzing Performance of Videos

Chapter 10: Optimal Solutions for Hidden Web Analytics Traps

Accuracy or Precision?

A Six-Step Process for Dealing with Data Quality

Building the Action Dashboard

Nonline Marketing Opportunity and Multichannel Measurement

The Promise and Challenge of Behavior Targeting

Online Data Mining and Predictive Analytics: Challenges

Path to Nirvana: Steps Toward Intelligent Analytics Evolution

Chapter 11: Guiding Principles for Becoming an Analysis Ninja

Context Is Queen

Comparing KPI Trends Over Time

Beyond the Top 10: What’s Changed

True Value: Measuring Latent Conversions and Visitor Behavior

Four Inactionable KPI Measurement Techniques

Search: Achieving the Optimal Long-Tail Strategy

Search: Measuring the Value of Upper Funnel Keywords

Search: Advanced Pay-per-Click Analyses

Chapter 12: Advanced Principles for Becoming an Analysis Ninja

Multitouch Campaign Attribution Analysis

Multichannel Analytics: Measurement Tips for a Nonline World

Chapter 13: The Web Analytics Career

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

Cultivating Skills for a Successful Career in Web Analysis

An Optimal Day in the Life of an Analysis Ninja

Hiring the Best: Advice for Analytics Managers and Directors

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

Transforming Company Culture: How to Excite People About Analytics

Deliver Reports and Analyses That Drive Action

Changing Metric Definitions to Change Cultures: Brand Evangelists Index

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

Five Rules for Creating a Data-Driven Boss

Need Budget? Strategies for Embarrassing Your Organization

Strategies to Break Down Barriers to Web Measurement

Who Owns Web Analytics?

Appendix: About the Companion CD

What You’ll Find on the CD

System Requirements

Using the CD

Troubleshooting

Index

End-User License Agreement

Praise for Web Analytics 2.0

When it comes to the digital marketing channels and understanding what and why people do things online, there is no one smarter than Avinash Kaushik. His first book, Web Analytics: An Hour a Day, should be on every marketer’s desk. Now, with Web Analytics 2.0, there’s a worthy accompaniment. When people ask, ‘Who is the smartest guy in the room when it comes to online marketing?’ only one name comes to mind: Avinash. I’d tell you to buy this book, but I would prefer if you didn’t. I’d love to keep these concepts and theories all to myself and my clients. Yes, it’s that powerful, awesome, and actionable.

—Mitch Joel, president of Twist Image and author of Six Pixels of Separation

Analytics is vitally important, and no one (no one) explains it more elegantly, more simply, or more powerfully than Avinash Kaushik. Consider buying up all the copies of this book before your competition gets a copy.

—Seth Godin, author, Tribes

Lots of companies have spent lots of time and money collecting data—and sadly do little with it. In Web Analytics 2.0, Avinash Kaushik helps us grasp the importance of this underused resource and shows us how to make the most of online data and experimentation.

—Dan Ariely, professor of Behavioral Economics, Duke University and author of Predictably Irrational

Kaushik takes the witchcraft out of analytics. If venture capitalists read this book, they would fire half of the CEOs that they’ve funded.

—Guy Kawasaki, co-founder of Alltop & Garage Technology Ventures

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Cover Image: iStockPhoto

Copyright © 2010 by Wiley Publishing, Inc., Indianapolis, Indiana

Published simultaneously in Canada

ISBN: 978-0-470-52939-3

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Dear Reader,

Thank you for choosing Web Analytics 2.0: The Art of Online Accountability & Science of Customer Centricity. This book is part of a family of premium-quality Sybex books, all of which are written by outstanding authors who combine practical experience with a gift for teaching.

Sybex was founded in 1976. More than 30 years later, we’re still committed to producing consistently exceptional books. With each of our titles we’re working hard to set a new standard for the industry. From the paper we print on to the authors we work with, our goal is to bring you the best books available.

I hope you see all that reflected in these pages. I’d be very interested to hear your comments and get your feedback on how we’re doing. Feel free to let me know what you think about this or any other Sybex book by sending me an email at [email protected], or if you think you’ve found a technical error in this book, please visit http://sybex.custhelp.com. Customer feedback is critical to our efforts at Sybex.

Best regards,

Neil Edde

Vice President and Publisher

Sybex, an Imprint of Wiley

To the wind beneath my wings, my inimitable wife Jennie.

Acknowledgments

Were it not for the love, patience, and support of my family, it would be impossible to write this book and hold down a few full-time jobs, advise three companies, write a blog, and travel the world evangelizing the awesomeness of data. I’m lucky. My wife Jennie is my biggest cheerleader and counsel, and for that I shall remain in debt to her for several lifetimes. My daughter Damini’s courage and kindness is a constant source of inspiration. My son Chirag’s intellect and energy reminds me to always be curious and strive for more.

I would like to express my deep appreciation to the readers of my blog, Occam’s Razor. In approximately three and a half years I have written 411,725 words in my 204 blog posts, and the readers of my blog have written 615,192 words in comments! Their engagement means the world to me and motivates me to make each blog post better than the last. It is impossible to thank each person, so on their behalf let me thank three: Ned Kumar, Rick Curtis, and Joe Teixeira.

As the song goes, I get by with a little help from my friends… in the last few years I have benefited from the help of two dear friends in particular. Bryan Eisenberg, the author of Always Be Testing, has consistently shared life lessons about this business and helped a ton with my own journey. Mitch Joel, the author of Six Pixels of Separation, has helped me become a better public speaker and, as if that were not enough, connected me with anyone worth connecting to! Thanks, guys.

A huge motivation behind this book was the incredible work done by The Smile Train, Doctors Without Borders, and Ekal Vidyalaya. They make the world a better place, and I feel blessed that the money raised by my books helps me be a small part of their mission.

Last, but not least, my fantastic team at Wiley. This book was written and published at a pace that would drive mere mortals crazy, but not them. They worked harder than I did, they pushed deadlines (and me!), and they made the impossible happen. Stephanie Barton, Kim Wimpsett, Liz Britten, and Willem Knibbe, you rock!

About the Author

Avinash Kaushik is author of the best-selling book Web Analytics: An Hour a Day (http://www.snipurl.com/wahour). He is also the analytics evangelist for Google and the cofounder of Market Motive, Inc.

As a thought leader, Avinash puts a commonsense framework around the often frenetic world of web analytics and combines that framework with the philosophy that investing in talented analysts is the key to long-term success. He is also a staunch advocate of listening to the consumer and is committed to helping organizations unlock the value of web data.

Avinash works with some of the largest companies in the world to help them evolve their online marketing and analytics strategies to become data-driven and customer-centric organizations. He recently received the 2009 Statistical Advocate of the Year award from the American Statistical Association.

He is also a frequent speaker at industry conferences in the United States and Europe, such as Ad-Tech, Monaco Media Forum, iCitizen, and JMP Innovators’ Summit, as well as at major universities, such as Stanford University, University of Virginia, and University of Utah.

You’ll find Avinash’s web analytics blog, Occam’s Razor, at www.kaushik.net/avinash.

Introduction

I have a simple, if lofty, goal with this book: to change how the world makes decisions when it comes to online.

For far too long our online efforts have accurately been classified as faith-based initiatives. And why not? That’s exactly how we made decisions for our offline efforts, and when we moved online, we duplicated those practices. But online, in the glorious beautiful world of the Web, we do not have to rely on faith.

We live in the most data-rich environment on the planet—an environment where numbers, data, math, and analysis should be the foundation of our decisions. We can use data to determine how to market effectively, how to truly connect with our audiences, how to improve the customer experience on our sites, how to invest our meager resources, and how to improve our return on investment, be it getting donations, increasing revenue, or winning elections!

You have a God-given right to be data driven, and this book will show you how to exercise that right.

Web Analytics 2.0 is a framework that redefines what data means online. Web Analytics 2.0 is not simply about the clicks that you collect from your website using analytics tools like Google Analytics, Omniture, or XiTi. Web Analytics 2.0 is about pouring your heart into understanding the impact and economic value of your website by doing rigorous outcomes analysis. It is about expressing your love for the principles of customer centricity by embracing voice-of-customer initiatives and, my absolute favorite, learning to fail faster by leveraging the power of experimentation. It is also—and this is so cool—about breaking free from your data silos by using competitive intelligence data to truly understand the strengths and weaknesses of your competitors.

This book answers four existential questions: what, howmuch, why, and what else.

The Second Little Book That Could

Like my first book, 100 percent of my proceeds from this book will be donated to two charities.

The Smile Train does cleft lip and palate surgery in 63 of the world’s poorest countries. They help do more than give the smile back to a child. Their efforts eliminate a condition that can have deep physical and long-term emotional implications for a child.

Ekal Vidyalaya initiates, supports, and runs nonformal one-teacher schools in the most rural parts of India. By locating their schools in remote areas neglected by the government and development agencies, they help eradicate illiteracy and open new paths for children.

By buying this book, you will elevate your knowledge and expertise about web analytics, but you are also helping me support two causes that are near and dear to my heart. When it comes to helping those in need, every little bit counts. Thank you.

The Awesome World of Data-Driven Decision Making

The offline world is not going anywhere. But the Web is becoming central to every aspect of our existence. It does not matter if you are a small-business owner, a politician, a mom, a student, an activist, a worker bee, or just one of 7 billion Homo sapiens on this planet. It does not matter if you live in Athens, Antananarivo, Abu Dhabi, or Albuquerque.

We have access to multiple data sources (quantitative, qualitative, and competitive). We have access to an abundance of free tools that we can use to ensure our web decisions, from the tactical to the strategic, are informed by data. Those decisions may range from what content should go on which page, to how to purchase the right set of keywords for our search marketing campaigns, to how to find the audience with the perfect demographic and psychographic profile for our business, to how to delight visitors when they get to our website.

I have compared web analytics to Angelina Jolie; that comparison should suggest how sexy it is, how powerful it is, and what a force for good I think it is. By the time you are halfway through this book I am positive you’ll agree with me.

What’s Inside the Book?

This book builds on the foundation laid by my first book, Web Analytics: An Hour a Day. I am not going to beat around the bush; Chapter 1 starts with a bang by introducing you to the Web Analytics 2.0 framework. That is followed immediately by a strong case for why the multiplicity mental model is mandatory for success with tools. We go from 0 to 60 in 13 pages!

Picking the right set of tools might be just as important as picking your friends: pick the wrong one, and it might take a long time to recover. With Chapter 2, I walk you through a process of self-reflection that will empower you to choose the right set of web analytics tools for your company. You’ll also learn questions you can ask tool vendors (why not stress them a bit?), the approach for choosing your vendor, and finally how to optimally negotiate your contract (stress them again!).

Chapters 3 and 4 cover the awesome world of traditional web analytics, clickstream analysis. In Chapter 3, using eight specific metrics, you’ll learn the intricate nuances that go into modern metrics: what you should look for, what you should avoid, and how to ensure that your company has chosen the right set of metrics. You’ll also learn my favorite technique for diagnosing the root cause behind poor performance.

Chapter 4 picks up the story and gently walks you through a primer on web analytics that will empower you to move very quickly from data to action on your website. I’ll then explore foundational analytical strategies followed by six specific analyses for your daily life. In every section you’ll learn how to kick things up a notch or two above average expectations. This chapter closes with a reality check on five key web analytics challenges (you are not going to want to miss this!).

Chapter 5 will be your best friend because it covers the single biggest reason for the existence of your websites: outcomes. That is, conversions, revenue, customer satisfaction, visitor loyalty, and more. You’ll learn the value of focusing on micro conversions (a must do!). At the end of the chapter, I offer two specific sets of recommendations on how to measure outcomes on non-ecommerce and B2B websites.

In Chapter 6, the Web Analytics 2.0 fun really starts because I cover the wonderful world of customer centricity: listening to customers and doing so at scale. You’ll learn to leverage lab usability, surveys, and other user-centric design methodologies. Finally, I give an outline of exciting techniques on the horizon—techniques that will dramatically change how you think of leveraging voice of customer.

Chapter 7 covers experimentation and testing. If you have ever read my blog or heard me speak, you’ll know how absolutely liberating it is that the Web allows us to fail faster, frequently, and get smarter every single day. You’ll learn about A/B and multivariate testing, but I think you’ll remember this book the most for teaching you about the power of controlled experiments (finally you can answer the hardest questions you’ll ever face!).

Chapter 8 will help you come to grips with competitive intelligence analysis. Like the rest of this book, this chapter is not about teaching you how to use one tool or the other. No sirree, Bob! You’ll learn how to dig under the covers and understand how data is captured and why with competitive intelligence more than anywhere else the principle of garbage in, garbage out applies. By the time you finish this chapter, you’ll know how to analyze the website traffic of your competitors, use search data to measure brand and identify new opportunities, zero in on the audiences relevant for your campaign or business, and benchmark yourself against your competitors.

Chapter 9 will clarify how to measure the new and evolving fields of mobile analytics; you’ll see why measuring blogs is not like measuring websites and how to measure the success of your efforts on social channels such as Twitter. You’ll start by learning about the fundamental challenges that the social Web presents for measurement.

Chapter 10 starts the process of truly converting you to an analysis ninja. I cover the hidden rules of the game, issues to be careful about, tasks to do more, and why some approaches work and others don’t. You’ll want to read the end of this chapter to learn why revolutions in web data fail miserably and evolution works magnificently. Oh, and as you might expect, I offer a very specific recommended path to nirvana!

Chapter 11 is about analytical techniques—the key weapons that you’ll need in your arsenal as you head off to conquer the data world. You’ll get to know context, comparisons, “what’s changed,” latent conversions, the head and tail of search, and really, really advanced paid search analysis. Oh my.

Chapter 12 contains material that will be worth multiple times the price of this book. It tackles the hardest, baddest, meanest web data challenges on the planet today: multitouch campaign attribution analysis and multichannel analytics. There’s no dancing around here, just practical actionable solutions you can implement right now, today. Don’t do anything in web analytics until you have read this chapter.

Chapter 13 was one of the most fun chapters for me to write. Web Analytics 2.0 is about people (not surprising coming from the creator of the 10/90 rule for magnificent success). Regardless of your role in the data world, this chapter includes guidance on how to plan your career to ensure maximum success. I offer best practices for keeping your knowledge current, but I don’t stop there; I suggest ways to move to the bleeding edge. The chapter closes with advice for managers and directors about how to identify the right talent, nurture them, and set them up for success.

Chapter 14 collects all my experience and research in this nascent field and shares recommendations for tackling the one task that will make or break your success: creating a data-driven culture. I recommend approaches on how to present data, how to excite people, how to use metric definitions to influence behavioral change in your organization, and how to create a truly data-driven boss (yea!) and finally strategies for getting budget and support for your analytical program and people.

Does that sound exciting? Oh, it’s so much fun!

Valuable Multimedia Content on the CD

The podcasts, videos, and resources on the CD extend the content in the book by making concepts easier to understand and offering additional guidance and instruction not in the book. For more information, see the book’s appendix—or better yet, fire up the disc and start exploring.

Request for Feedback

I love preaching about the value of customer data, and I love practicing that mantra as well. I want to hear your thoughts about this book. What was the one thing you found to be of most value in the book? What was the biggest surprise? What was the one big thing you implemented and won praise for? What is one thing I should have done differently? What was the biggest missing piece?

You can email me at [email protected].

I’ll learn from every bit of feedback, and I promise to reply to each and every person who writes to me. Please share your experience, critiques, and kudos.

One more fun thing: for my first book I requested readers to send me a picture with the book (of people, places, babies, buildings, and so on). That led to the wonderful collection of pictures you’ll see here: http://zqi.me/wapeople. It makes the world a bit closer and more real.

I would love to get a picture of you or your hometown or your pet with this book. Please email it to me at [email protected].

Thank you.

The Beginning

I am sure you can tell that I had a ton of fun writing this book. I really, really did. I am confident that you’ll have just as much fun reading it, learning from it, and changing the world one insightful analysis at a time.

Let’s go!

Chapter 1: The Bold New World of Web Analytics 2.0

For years it has been clear that web analytics holds the promise to truly revolutionize how business is done on the Web. And why not? You can track every click of every person on your site. How can that not be actionable? Unfortunately, the revolution has not quite panned out. The root cause is that analysts and marketers have taken a very limited view of data on the Web and have restricted it just to clickstream data. In this chapter, I make the case for why you need to drastically rethink what it means to use data on the Web. The Web Analytics 2.0 strategy adapts to the evolution of the Web and dramatically expands the types of data available to help you achieve your strategic business objectives.

Chapter Contents

State of the Analytics UnionState of the IndustryRethinking Web Analytics: Meet Web Analytics 2.0Change: Yes We Can!

State of the Analytics Union

Let’s start with a tale about the paradox of data. Professionally speaking, I grew up in the world of data warehousing and business intelligence (BI). I worked with massive amounts of enterprise data; multiterabytes; and sophisticated extract, transform, and load (ETL) middle layers—all fronted by complex business intelligence tools from companies such as MicroStrategy, Business Objects, and SAS. Although the whole operation was quite sophisticated and cool, the data set wasn’t really that complex. Sure, we stored customer names and addresses, products purchased, and calls made, along with company metadata and prices. But not much data was involved. As a result, we made lots of great decisions for the company as we valiantly went to battle for insights.

But the lack of breadth and depth of data meant that often, and I say this only partly in jest, we could blame incompetence on the lack of sufficienttypes of data. So, we always had a get-out-of-jail-free card, something like, “Gosh darn it. If I knew our customers’ underwear sizes, I could correlate that to their magazine subscriptions, and then we would know how to better sell them lightweight laptops.”

I know, it sounds preposterous. But it really isn’t.

With that context, you’ll appreciate why I was ecstatic about the world of web analytics. Data, glorious data all around! Depth and breadth and length. Consider this: Yahoo! Web Analytics is a 100 percent free tool. It has approximately 110 standard reports, each with anywhere from 3 to 6 metrics each. That number of 110 excludes the ability to create custom reports covering even more metrics than God really intended humanity to have.

But after a few weeks in this world, I was shocked that even with all this data I was no closer to identifying actionable insights about how to improve our website or connect with our customers.

That’s the paradox of data: a lack of it means you cannot make complete decisions, but even with a lot of data, you still get an infinitesimally small number of insights.

For the Web, the paradox of data is a lesson in humility: yes, there is a lot of data, but there are fundamental barriers to making intelligent decisions. The realization felt like such a letdown, especially for someone who had spent the prior seven years on the quest for more data.

But that’s what this book’s about: shedding old mental models and thinking differently about making decisions on the Web, realizing data is not the problem and that people might be, and focusing less on accuracy and more on precision. We will internalize the idea that the Web is an exquisitely unique animal, like nothing else out there at the moment, and it requires its own exquisitely unique approach to decision making. That’s Web Analytics 2.0.

Before we go any further, let’s first reflect on where we are as an industry today.

State of the Industry

As I reflect upon where we are today, I see a lot that has not changed from the very early days of web analytics—all of about 15 years ago. The landscape is dominated by tools that primarily use data collected by web logs or JavaScript tags. Most companies use tools from Google Analytics, Omniture Site Catalyst, Webtrends, Clicktracks, or Xiti to understand what’s happening on their websites.

However, one of the biggest changes in recent years was the introduction of a free robust web analytics tool, Google Analytics. Web analytics had been mostly the purview of the rich (translation: big companies that could afford to pay). Sure, a few free web log–based solutions existed, but they were hard to implement and needed a good deal of IT caring and feeding, presenting a high barrier to entry for most businesses.

Google Analytics’ biggest impact was to create a massive data democracy. Anyone could quickly add a few lines of JavaScript code to the footer file on their website and possess an easy-to-use reporting tool. The number of people focusing on web analytics in the world went from a few thousand to hundreds of thousands very quickly, and it’s still growing.

This process was only accelerated by Yahoo!’s acquisition of IndexTools in mid-2008. Yahoo! took a commercial enterprise web analytics tool, cleverly rebranded it as Yahoo! Web Analytics, and released it into the wild for free (at this time only to Yahoo! customers).

Other free tools also arrived, including small innovators such as Crazy Egg, free open source tools such as Piwik and Open Web Analytics, or niche tools such as MochiBot to track your Flash files. Some very affordable tools also entered the market, such as the very pretty and focused Mint, which costs just $30 and uses your web logs to report data.

A search on Google today for free web analytics tools results in 49 million results, a testament to the popularity of all these types of tools. All these free tools have put the squeeze on the commercial web analytics vendors, pushing them to become better and more differentiated. Some have struggled to keep up, a few have gone under, but those that remain today have become more sophisticated or offer a multitude of associative solutions.

Omniture is a good example of a competitive vendor. SiteCatalyst, its flagship web analytics tool, is now just one of its core offerings. Omniture now also provides Test&Target, which is a multivariate testing and behavior targeting solution, and the company entered the search bid management and optimization business with SearchCenter. It also offers website surveys, and it can now power ecommerce services through its acquisition of Mercado. Pretty soon Omniture will be able to wake you up with a gentle tap and help you into your work clothes! As a result of this competitive strategy, Omniture has done very well for itself and its shareholders thus far.

Beyond web analytics, I am personally gratified to see so many other tools that exploit the Trinity strategy of Experience, Behavior, and Outcomes, which I presented in my first book, Web Analytics: An Hour a Day (Sybex, 2007).

We can now move beyond the limits of measuring Outcomes from web analytics tools, or conversions, to measuring more robust Outcomes, say our social media efforts. Obvious examples of this are using FeedBurner to measure Outcomes from blogs and using the diverse ecosystem of tools for Twitter to measure the success of your happy tweeting existence. We are inching—OK, scraping—closer toward the Holy Grail of integrated online and offline Outcomes measurement.

The Behavior element of the strategy has not been neglected either. Inexpensive online tools allow you to do card sorts (an expensive option offline) to get rapid customer input into redesigns on your websites’ information architecture (IA). A huge number of free survey tools are now available; allow me to selfishly highlight 4Q, which is a free on-exit survey from iPerceptions that was based on one of my blog posts (“The Three Greatest Survey Questions Ever”; http://zqi.me/ak3gsq).

Then there is the adorable world of competitive intelligence. It did not have an official place in the Trinity strategy (though it was covered in Web Analytics: An Hour A Day) because of the limited (and expensive) options in the market at that time. We have had a massive explosion in this area in the past two years with tools that can transform your business, such as Compete, Google’s Ad Planner and Insights for Search, Quantcast...and I am just scratching the surface.

Reflecting on the early days of web analytics, I am very excited about the progress the industry has made since the publication of my last book a couple years ago.

I am confident massive glory awaits the marketer, analyst, site owner, or CEO who can harness the power of these free or commercial tools to understand customer experience and competitive opportunities.

Rethinking Web Analytics: Meet Web Analytics 2.0

Remember the paradox of data? Just a few pages ago? So much data, so few insights. That paradox led me to create the Trinity strategy for web analytics when I was working at Intuit, and it has now led me to introduce Web Analytics 2.0.

Most businesses that focus on web analytics (and sadly there are still not enough of them) think of analytics simply as the art of collecting and analyzing clickstream data, data from Yahoo! Web Analytics, Omniture, or Mint.

This is a good start. But very quickly a realization dawns, as illustrated in Figure 1-1.

The big circle is the amount of data you have. Lots! After a few months, though, you realize the zit at the bottom of the circle is the amount of actionable insight you get from that data. Why?

Figure 1-1: The old paradigm of Web Analytics 1.0

You have so little actionable insight because clickstream data is great at the what, but not at the why. That is one of the limits of clickstream data. We know every click that everyone ever makes and more. We have the what: What pages did people view on our website? What products did people purchase? What was the average time spent? What sources did they come from? What keywords or campaigns produced clicks? What this, and what that, and what not?

All this what data is missing the why. It’s important to know what happened, but it is even more critical to know why people do the things they do on your site. This was the prime motivation behind my redefinition of web analytics. For thorough web analytics, we need to include not just the why but also key questions that can help us make intelligent decisions about our web presence.

Web Analytics 2.0 is:

the analysis of qualitative and quantitative data from your website and the competition,

to drive a continual improvement of the online experience that your customers, and potential customers have,

which translates into your desired outcomes (online and offline).

This definition is specific, it’s modern, and it results in rethinking how to identify actionable insights. Figure 1-2 illustrates Web Analytics 2.0.

Figure 1-2: The updated paradigm of Web Analytics 2.0

With this definition, I wanted to expand the questions that could be answered by redefining what it meant to do web analytics, what sources an analyst or online marketer would access, and what tools would be put to use.

Clickstream answers the what. Multiple Outcomes Analysis answers the how much; Experimentation and Testing help explain the why (albeit analytically, Voice of Customer also contributes to the why), this time with direct customer input; and lastly Competitive Intelligence answers the what else, which is perhaps the most underappreciated data on the Web.

Figure 1-3 outlines how each of these four important questions map into each source of data/element of the Web Analytics 2.0 strategy.

Ain’t that sweet? Now let’s look at each element briefly; I will cover them in more detail in the upcoming chapters of the book.

Figure 1-3: Key questions associated with Web Analytics 2.0

The What: Clickstream

The what of Clickstream is straightforward. If you have a web analytics solution hosted in-house, then the what is collecting, storing, processing, and analyzing your website’s click-level data. If, like most people, you have a web analytics solution hosted externally or hosted by a vendor, then the what is simply collecting and analyzing the click-level data.

Click-level data is data you get from Webtrends, Google Analytics, and other Clickstream tools. You will have a lot of data—in the order of gigabytes in a few months and more if you store history.

Clickstream is also foundational data; it helps you measure pages and campaigns and helps you analyze all kinds of site behavior: Visits, Visitors, Time on Site, Page Views, Bounce Rate, Sources, and more.

The How Much: Multiple Outcomes Analysis

If you have heard me speak at a conference, you have heard this story. At my first web analytics job, the company was using Webtrends (a wonderful robust tool). I was new. I asked a lot of questions about the use of data and the 200 Webtrends reports that were being produced. At the end of two weeks, I turned off Webtrends.

For three weeks, not a single human being called about their missing 200 reports. 200! In a multibillion-dollar company!

After some reflection, I realized the root cause of this “unmissing” data was that none of these 200 reports focused on measuring Outcomes. A million visits to the site. So what? What were the Outcomes for the company? For the marketer?

Focusing deeply and specifically on measuring Outcomes means connecting customer behavior to the bottom line of the company. The most impactful thing you will do with web analytics is to tie Outcomes to profits and to the bonuses of your report recipients.

A website attempts to deliver just three types of Outcomes:

Increase revenue.Reduce cost.Improve customer satisfaction/loyalty.

That’s it. Three simple things.

Everything you do on your website needs to deliver against these three Outcomes, regardless of whether your website is for ecommerce, tech support, social media, or just general propaganda. You’ll use your Clickstream tools, you’ll use your enterprise resource planning (ERP) systems, you’ll use surveys, you’ll use Technorati, and more.

If you want the love of your senior management, you need to focus on Multiple Outcomes Analysis.

The Why: Experimentation and Testing

I believe that most websites suck because HiPPOs create them. HiPPO is an acronym for the “Highest Paid Person’s Opinion.”

You know how it goes. Someone presents a great idea, but the HiPPO decides what actually happens. If she or he wants the dancing monkey on the home page, well, then the dancing monkey goes on the home page.

The reality is that usually the HiPPO is 10 steps removed from the site, has never visited a Wal-Mart, and is too close to the business. The HiPPO is a poor stand-in for what customers want.

By leveraging the power of Experimentation and Testing tools such as the free Google Website Optimizer or commercial tools such as Omniture’s Test&Target, Autonomy’s Optimost, or SiteSpect, you can change your strategy. Rather than launching a site with one idea (the HiPPO’s idea, of course), you can run experiments live on your site with various ideas and let your customers tell you what works best. So sweet. I call it the “revenge of the customers!”

There is a powerful hidden reason to be best friends forever (BFF) with your testing tool: you fail faster. It is very expensive to fail in all other channels, such as TV, radio, magazines, or big stores. But failing online is cheap and fast.

Consider launching a new product on Walmart.com vs. a Wal-Mart store. For example, why not launch a new product on Walmart.com first rather than at a Wal-Mart store and see how it does? Why not experiment with a few different promotional offers via email or search ads before you finalize your strategy and launch it using print, catalog, or TV ads? In each scenario you can take a bigger risk, launch faster, fail or succeed significantly faster online!

That is a massive strategic advantage. It is also the reason I am fond of saying “Experiment or die.”

The Why: Voice of Customer

For me, a mechanical engineer with an MBA, the why—or the power and value of qualitative data—was a tough lesson. Consider this simple question: can you look at the Top Pages Viewed report from your web analytics tool and for your site—say, www.zappos.com—and understand the content visitors were most interested in?

How would you know which of the top pages visitors actually wanted to see? Maybe they could not find the pages because of a missing internal site search engine or the broken navigation on your site? You have no idea. Your web analytics tool can report only what it can record. What your customers wanted but did not see was not recorded.

That’s why Voice of Customer (VOC) is so important. Through surveys, lab usability testing, remote usability testing, card sorts, and more, you can get direct feedback from customers on your website or from your target customer base.

I have had so many “aha” moments reading open-text VOC from website surveys. “Oh, this is why they abandoned” or “Darn, that’s why no one is buying this product” or, usually, “Why was something so obvious hidden from us?”

If you marry the what with the why, you’ll have a lifetime of happiness. I guarantee it.

The What Else: Competitive Intelligence

Of all the surprises on my web analytics journey, Competitive Intelligence was the biggest one. In the traditional world of enterprise resource planning, customer relationship management (CRM), and deep back-end enterprise systems, all you had was your data. You had very little information about your competitors. On the Web, though, you can gather tons of information about your direct or indirect competitors! And usually that info is free!

At www.compete.com, you can type in the URLs of your competitors and within seconds compare your performance with theirs. You can see how long people spend on your site vs. theirs. You can see repeat visits, page views per visitor, growth, and so on.

So, why should you really care about this?

Consider this simple analogy. If you are using your web analytics tool to measure your website, then it’s like sitting in a car and watching the dashboard to see that you are going exactly 70 mph. But your windshield and windows are all blacked out. You can’t see outside.

Using Competitive Intelligence data is like scraping off that black paint and being able to see outside. Now you can see you are in a race (unbeknownst to you), and you can see that while you are driving at 70 mph, everyone else is racing past at 160 mph. Unless you make drastic changes, you’ll be irrelevant.

That’s the power of Competitive Intelligence data. Knowing how you are performing is good. Knowing how you are performing against your competition is priceless—it helps you improve, it helps you identify new opportunities, and it helps you stay relevant.

In this book, I will cover how you can use free and commercial tools to get Competitive Intelligence related to audience (demographic and psychographic) attributes, keywords, traffic sources, website customer behavior, and more.

That’s the magnificent world of Web Analytics 2.0. This world is broader than you imagined. It is sexier than you imagined. It is all about focusing on the customer.

Change: Yes We Can!

You will need to make two critical changes to succeed in the world of Web Analytics 2.0. The first is a strategic shift—a change to the mental model you apply. The second is a tactical shift—one that will challenge your current thinking about tools and how to use them.

The Strategic Imperative

The big challenge for crossing any modern chasm is rarely technology or tools. The challenge is entrenched mind-sets. For all of us, the biggest challenge to changing our web analytics strategy will be to evolve our mind-set to think 2.0.

Figure 1-4 illustrates the mind-set evolution that you absolutely need to move you or your organization to Web Analytics 2.0.

In the world of Web Analytics 2.0, clicks don’t rule; rather, the combination of the “head and the heart” rules. When you are ruled by the head and the heart, you care equally about what happens on your website as you do about what happens on your competitor’s. All the while you are automating as much decision making as you can to eliminate reporting and even some analysis. Your world is one of continuous actions (that is, surveys, testing, behavior targeting, keyword optimization) and continuous improvements, where customers, not HiPPOs, rule.

Figure 1-4: Mind-set evolution mandated by Web Analytics 2.0

The Tactical Shift

With the second change, you embrace a fantastic, now mandatory, concept of Multiplicity.

In the traditional business intelligence world, we were taught to seek the “single source of the truth.” Bring all data into one place; build massive systems, usually over multiple years; and celebrate. Sadly, this strategy is toxic on the Web.

At the eMetrics summit in 2003, Guy Creese presented the concept of Multiplicity. The concept was brutal in its simplicity: multiple constituencies, tools, and types of data sources make it much harder to do effective analytics.

I have come to believe that Multiplicity is the core reason for the awesomeness of the Web. Consumption of data is vastly more democratic for your web business; everyone needs access to data now. You have a wealth of effective tools to do jobs that you never thought possible. You have not just a lot more data, as in clicks, but a lot more data types (qualitative and quantitative) that make life worth living!

Multiplicity is the only way for you to be successful at Web Analytics 2.0.

As Figure 1-2 illustrated, Web Analytics 2.0 gives you a holistic picture of your website performance. Under that strategy, every solid web decision-making program (call it web analytics or web insights or digital customer insights) in a company will need to solve for the Five Pillars: Clickstream, Multiple Outcomes Analysis, Experimentation and Testing, Voice of Customer, and Competitive Intelligence.

Figure 1-5 shows the approach your tools strategy must take to meet the need of Multiplicity.

Figure 1-5: The Web Analytics 2.0 Multiplicity strategy and tools

As clearly illustrated in Figure 1-5, you’ll need a specialized tool to solve for each element of Web Analytics 2.0.

Clickstream You’ll use Omniture tools, Google Analytics, Unica’s NetInsight, Webtrends, Yahoo! Web Analytics, Lyris HQ (formerly ClickTracks), Coremetrics, and so on.

Multiple Outcomes You’ll use your web analytics tools mentioned for Clickstream but also the likes of iPerceptions (to measure Task Completion Rate!), FeedBurner (to track Subscribers), and various other tools to measure social media success (your traditional web analytics tools are not very good at this last one).

Experimentation and Testing You’ll use Google Website Optimizer, Omniture’s Test&Target, SiteSpect, Optimost, and so on.

Voice of Customer You’ll use iPerceptions, CRM Metrix, Ethnio, ForeSee, and self-service options such as Lab Usability.

Competitive Intelligence You’ll use Google Ad Planner, Insights for Search, Compete, Hitwise, Technorati, and so on.

For optimal success, you’ll need only one tool from each of the previous categories to cover the base for each of the Five Pillars. That’s Multiplicity.

Data from each tool is not meant to duplicate the other areas or relate to the other areas. Each tool provides insights that, taken together, give you the data you need to succeed.

Don’t feel overwhelmed by the Multiplicity strategy.

Notice that in each row in Figure 1-5 you have an option for a free tool, so don’t worry about cost right away. Mercifully you also don’t have to do everything right away. Your company’s size, needs, and sophistication will help you determine your personal strategy.

The following is my list of the must-have elements that different businesses should consider to join the Web Analytics 2.0 world; they are ranked by priority and show the minimal areas that should be addressed:

Small businesses: 1. Clickstream, 2. Outcomes, 3. Voice of Customer.Medium-sized businesses: 1. Outcomes, 2. Clickstream, 3. Voice of Customer, 4. Testing.Large, huge businesses:1. Voice of Customer, 2. Outcomes, 3. Clickstream, 4. Testing, 5. Competitive Intelligence, 6. Deep back-end analysis (Coradiant), 7. Site structure and gaps (Maxamine).

For each category, just choose a free or commercial tool listed in Figure 1-5.

Bonus Analytics

You probably noticed two tools at the very bottom of Figure 1-5. They are bonus items.

When we talk about web analytics, we typically don’t think of Maxamine and Coradiant first. For large companies, Fortune 1,000 especially, both of these tools are almost mandatory. Neither measures what a traditional web analytics tool does, so there is no overlap, but each brings its unique strengths to the business of web data.

You should use Maxamine because it gives you critical data relating to search engine optimization gaps, missing JavaScript tags, duplicative content, broken website functionality (yes, broken links and “bad” forms), security and privacy compliance, black holes not crawled by your internal search engine, and more. Maxamine essentially provides everything you need to know, measure, and report about the existence of your website itself. Another competitive option is ObservePoint.

You should use Coradiant because it gives you critical data, down to an individual user level, about the “matrix” that powers your website—that is, the bits and bytes, the pages and packets. (Disclosure: I am currently on the Advisory Board of Coradiant.) Coradiant includes every single thing you can imagine going out from your web servers (anywhere in the world) to your customers. You can find problems on your website quickly and hold yourself and your IT teams accountable.

With Coradiant, you can also understand why, for example, your conversion rates are down. Is it because suddenly your cart and checkout pages were slow and not making it to your customers? Or is it because of 404 errors on your important pages? These are key questions that traditional tools have a hard time answering, if at all.

That’s the Multiplicity strategy: Clickstream data, a better view of the landscape through Multiple Outcomes, and quicker paths to failure and success through Experimentation and Testing. These are the basic steps toward tackling a competitive industry. And don’t forget to adopt the mental model of “heart and mind,” where you are as vigilant of your competitor’s web activity as you are of your own (outlined in Figure 1-4). Multiplicity provides you with the keys to go out and change the world. Rock on!

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

In the new world order of Web Analytics 2.0, you must move beyond the mental model of a “single source of truth” to a true Multiplicity strategy to identify actionable insights faster. How do you do that? Tools! You must pick ’em right and make sure that one step forward is not three steps back.

In this chapter, you’ll learn how to do deep introspection to understand your needs better, how to get the truth out of analytics vendors, how to compare analytics tools, and how to run a pilot and negotiate a contract.

Chapter Contents

Predetermining Your Future SuccessStep 1: Three Critical Questions to Ask Yourself Before You Seek an Analytics Soul MateStep 2: Ten Questions to Ask Vendors Before You Marry ThemComparing Web Analytics Vendors: Diversify and ConquerStep 3: Identifying Your Web Analytics Soul Mate (How to Run an Effective Tool Pilot)Step 4: Negotiating the Prenuptials: Check SLAs for Your Web Analytics Vendor Contract

Predetermining Your Future Success

We are blessed to have a number of robust free or commercial tools to solve for Web Analytics 2.0. Unfortunately, we significantly underappreciate how critical picking the right tool is. Or how much a wrong tool can regress the organization.

For example, my company chose a web analytics tool after sending a glorious request for proposal (RFP) that contained every question on Earth. The chosen tool took us 15 months to completely implement and then 6 months to get the first inkling that it was completely wrong for the company. Guess that RFP was not so robust after all! By then, we were too vested in the tool—via people, systems, and processes—to change anything quickly. In another 6 months, the senior leader who helped choose this expensive tool left the company. The new leader immediately saw the problem and started the process of choosing a new tool. The company had been stagnant now for more than two-and-a-half years. It took us another 9 months to pick and implement the right tool.

Total time to making strategic web decisions: terribly longer than it needed to be.

You might think this situation happens only at large companies or only at other companies. Trust me, it is probably happening at your company.

We tend to pick tools like we are picking a marriage partner. When we choose wrong, we don’t want to accept it. The reality is that few things will impact your chances at success more than picking the right set of tools for the unique needs of your company—small or medium or large.

The 10/90 Rule

My entry into the world of web analytics was enlightening. The company had one of the best tools money could buy, yet decisions were gut-driven, and all that data was for naught.

The lesson I learned from that experience caused me to postulate the 10/90 rule (published on my blog on May 19, 2006):

Our goal: highest value from web analytics implementation.Cost of analytics tool and vendor professional services: $10.Required investment in “intelligent resources/analysts”: $90.Bottom line for magnificent success: it’s the people.

The rationale was simple because of four basic problems:

Websites are massively complex, and although tools can capture all that data, they don’t actually tell you what to do.Most web analytics tools in the market, even today, simply spew out data. Lots of it.We don’t live in our simple Web Analytics 1.0 world. We now have to deal with quantitative data, qualitative data, results of our multivariate experiments, and competitive intelligence data that might not tie to anything else.One of the most powerful ways to convert data into insights is to keep up with the “tribal knowledge” in the company: unwritten rules, missing metadata, the actions of random people (OK, your CEO), and so on.

To solve these four problems, you need an analyst, that is, a person with a planet-sized brain. Invest multiple times more in her or him, or more of them, if you truly want to take action on your data. Otherwise, you are simply data rich and information poor.

With the proliferation of options online and the sophistication of the Web now, the 10/90 rule is even more relevant today.

Nitpicking: I currently work as the analytics evangelist for Google. Lots of people, mathematically superior people, tell me that with the existence of free tools the 10/90 rule is invalid: the tools ($10 part) are now free. My answer to them is that the tools are still not “free.” If I want to use Google Analytics or Yahoo! Web Analytics, the cost of the tool is zero, but I may have to spend $5,000 working with an authorized consultant to implement it correctly. There’s your $10. Now go spend $90 in getting people with planet-sized brains to make sense of all your data!

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

The biggest mistake we make in the process of selecting tools is that we never pause to reflect on our own awesomeness or, more likely, a lack thereof. We jump into bed with the closest tool that will sleep with us. We rarely consider the qualities that might determine whether that tool is right for us.

So, step numero uno is self-reflection and a brutally honest assessment of your own company, its people, and its position in the evolutionary cycle.

Use the following three questions to prompt the critical self-reflection that should help you pick the right Web Analytics 2.0 soul mate.

Q1: “Do I want reporting or analysis?”

This is a very difficult question to answer because most organizations have a hard time being honest about their needs. Every company says they want analysis, yet few organizations (especially those with greater than 100 people) actually do. They want reporting.

The following are some reasons for choosing reporting only:

Decentralized decision making The organization is structured so that lots of different leaders make decisions, and their buy-in is required for any action. These leaders need data that they can process, not analysis that tells them what action to take.

Company cultures How does your company reach consensus? Do you need to always “cover your back”? Does it have layers of management? Is it matrixed? Paperwork-driven? Often the culture dictates checks and balances, with multiple oversights and the need for proof. This kind of culture requires a supply of information (data).

Availability of tools/features A number of tools are geared toward reporting and not analysis, which sets the pattern for what gets used.

History Older companies historically have worked by people publishing reports and data. “Think smart and move fast” is not the mantra.

Propensity of risk Does your company empower risk taking? Or is taking risks a career limiting move? Doing true analysis means letting go of some control and trusting people who know how to do their jobs. If your company’s culture does not encourage that then you need reporting.

Distribution of knowledge in people/teams (tribal knowledge) If you really want to analyze data, you need to know the context to make sense of the numbers. If information and execution are isolated in your company, no amount of empowering the analyst will help. If your analysts are not plugged in, the best they can do is provide data to people who might be plugged in (ideally the company leaders).

Availability of raw analytical brainpower Bringing it back to the 10/90 rule, if you have invested appropriately in analysts, then it makes sense to choose a tool that allows your company to do true analysis.

Despite these extenuating circumstances, the analytics team is told to go out and buy the tool that is “God’s gift to humanity.”